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Sample records for neural firing patterns

  1. Effect of inhibitory firing pattern on coherence resonance in random neural networks

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    Yu, Haitao; Zhang, Lianghao; Guo, Xinmeng; Wang, Jiang; Cao, Yibin; Liu, Jing

    2018-01-01

    The effect of inhibitory firing patterns on coherence resonance (CR) in random neuronal network is systematically studied. Spiking and bursting are two main types of firing pattern considered in this work. Numerical results show that, irrespective of the inhibitory firing patterns, the regularity of network is maximized by an optimal intensity of external noise, indicating the occurrence of coherence resonance. Moreover, the firing pattern of inhibitory neuron indeed has a significant influence on coherence resonance, but the efficacy is determined by network property. In the network with strong coupling strength but weak inhibition, bursting neurons largely increase the amplitude of resonance, while they can decrease the noise intensity that induced coherence resonance within the neural system of strong inhibition. Different temporal windows of inhibition induced by different inhibitory neurons may account for the above observations. The network structure also plays a constructive role in the coherence resonance. There exists an optimal network topology to maximize the regularity of the neural systems.

  2. Exponential decay characteristics of the stochastic integer multiple neural firing patterns.

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    Gu, Huaguang; Jia, Bing; Lu, Qishao

    2011-03-01

    Integer multiple neural firing patterns exhibit multi-peaks in inter-spike interval (ISI) histogram (ISIH) and exponential decay in amplitude of peaks, which results from their stochastic mechanisms. But in previous experimental observation that the decay in ISIH frequently shows obvious bias from exponential law. This paper studied three typical cases of the decay, by transforming ISI series of the firing to discrete binary chain and calculating the probabilities or frequencies of symbols over the whole chain. The first case is the exponential decay without bias. An example of this case was discovered on hippocampal CA1 pyramidal neuron stimulated by external signal. Probability calculation shows that this decay without bias results from a stochastic renewal process, in which the successive spikes are independent. The second case is the exponential decay with a higher first peak, while the third case is that with a lower first peak. An example of the second case was discovered in experiment on a neural pacemaker. Simulation and calculation of the second and third cases indicate that the dependency in successive spikes of the firing leads to the bias seen in decay of ISIH peaks. The quantitative expression of the decay slope of three cases of firing patterns, as well as the excitatory effect in the second case of firing pattern and the inhibitory effect in the third case of firing pattern are identified. The results clearly reveal the mechanism of the exponential decay in ISIH peaks of a number of important neural firing patterns and provide new understanding for typical bias from the exponential decay law.

  3. Neural coordination can be enhanced by occasional interruption of normal firing patterns: a self-optimizing spiking neural network model.

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    Woodward, Alexander; Froese, Tom; Ikegami, Takashi

    2015-02-01

    The state space of a conventional Hopfield network typically exhibits many different attractors of which only a small subset satisfies constraints between neurons in a globally optimal fashion. It has recently been demonstrated that combining Hebbian learning with occasional alterations of normal neural states avoids this problem by means of self-organized enlargement of the best basins of attraction. However, so far it is not clear to what extent this process of self-optimization is also operative in real brains. Here we demonstrate that it can be transferred to more biologically plausible neural networks by implementing a self-optimizing spiking neural network model. In addition, by using this spiking neural network to emulate a Hopfield network with Hebbian learning, we attempt to make a connection between rate-based and temporal coding based neural systems. Although further work is required to make this model more realistic, it already suggests that the efficacy of the self-optimizing process is independent from the simplifying assumptions of a conventional Hopfield network. We also discuss natural and cultural processes that could be responsible for occasional alteration of neural firing patterns in actual brains. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. Identification of neural firing patterns, frequency and temporal coding mechanisms in individual aortic baroreceptors

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    Huaguang eGu

    2015-08-01

    Full Text Available In rabbit depressor nerve fibers, an on-off firing pattern, period-1 firing, and integer multiple firing with quiescent state were observed as the static pressure level was increased. A bursting pattern with bursts at the systolic phase of blood pressure, continuous firing, and bursting with burst at diastolic phase and quiescent state at systolic phase were observed as the mean level of the dynamic blood pressure was increased. For both static and dynamic pressures, the firing frequency of the first two firing patterns increased and of the last firing pattern decreased due to the quiescent state. If the quiescent state is disregarded, the spike frequency becomes an increasing trend. The instantaneous spike frequency of the systolic phase bursting, continuous firing, and diastolic phase bursting can reflect the temporal process of the systolic phase, whole procedure, and diastolic phase of the dynamic blood pressure signal, respectively. With increasing the static current corresponding to pressure level, the deterministic Hodgkin-Huxley (HH model manifests a process from a resting state first to period-1 firing via a subcritical Hopf bifurcation and then to a resting state via a supercritical Hopf bifurcation, and the firing frequency increases. The on-off firing and integer multiple firing were here identified as noise-induced firing patterns near the subcritical and supercritical Hopf bifurcation points, respectively, using the stochastic HH model. The systolic phase bursting and diastolic phase bursting were identified as pressure-induced firings near the subcritical and supercritical Hopf bifurcation points, respectively, using an HH model with a dynamic signal. The firing, spike frequency, and instantaneous spike frequency observed in the experiment were simulated and explained using HH models. The results illustrate the dynamics of different firing patterns and the frequency and temporal coding mechanisms of aortic baroreceptor.

  5. Bifurcation Scenarios of Neural Firing Patterns across Two Separated Chaotic Regions as Indicated by Theoretical and Biological Experimental Models

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    Huaguang Gu

    2013-01-01

    Full Text Available Nonlinear dynamics can be used to identify relationships between different firing patterns, which play important roles in the information processing. The present study provides novel biological experimental findings regarding complex bifurcation scenarios from period-1 bursting to period-1 spiking with chaotic firing patterns. These bifurcations were found to be similar to those simulated using the Hindmarsh-Rose model across two separated chaotic regions. One chaotic region lay between period-1 and period-2 burstings. This region has not attracted much attention. The other region is a well-known comb-shaped chaotic region, and it appears after period-2 bursting. After period-2 bursting, the chaotic firings lay in a period-adding bifurcation scenario or in a period-doubling bifurcation cascade. The deterministic dynamics of the chaotic firing patterns were identified using a nonlinear prediction method. These results provided details regarding the processes and dynamics of bifurcation containing the chaotic bursting between period-1 and period-2 burstings and other chaotic firing patterns within the comb-shaped chaotic region. They also provided details regarding the relationships between different firing patterns in parameter space.

  6. Global attractor alphabet of neural firing modes.

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    Baram, Yoram

    2013-08-01

    The elementary set, or alphabet, of neural firing modes is derived from the widely accepted conductance-based rectified firing-rate model. The firing dynamics of interacting neurons are shown to be governed by a multidimensional bilinear threshold discrete iteration map. The parameter-dependent global attractors of the map morph into 12 attractor types. Consistent with the dynamic modes observed in biological neuronal firing, the global attractor alphabet is highly visual and intuitive in the scalar, single-neuron case. As synapse permeability varies from high depression to high potentiation, the global attractor type varies from chaotic to multiplexed, oscillatory, fixed, and saturated. As membrane permeability decreases, the global attractor transforms from active to passive state. Under the same activation, learning and retrieval end at the same global attractor. The bilinear threshold structure of the multidimensional map associated with interacting neurons generalizes the global attractor alphabet of neuronal firing modes to multineuron systems. Selective positive or negative activation and neural interaction yield combinatorial revelation and concealment of stored neuronal global attractors.

  7. MEMBRAIN NEURAL NETWORK FOR VISUAL PATTERN RECOGNITION

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    Artur Popko

    2013-06-01

    Full Text Available Recognition of visual patterns is one of significant applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In the paper, a simplified neural approach to recognition of visual patterns is portrayed and discussed. This paper is dedicated for investigators in visual patterns recognition, Artificial Neural Networking and related disciplines. The document describes also MemBrain application environment as a powerful and easy to use neural networks’ editor and simulator supporting ANN.

  8. Dynamics and Physiological Roles of Stochastic Firing Patterns Near Bifurcation Points

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    Jia, Bing; Gu, Huaguang

    2017-06-01

    Different stochastic neural firing patterns or rhythms that appeared near polarization or depolarization resting states were observed in biological experiments on three nervous systems, and closely matched those simulated near bifurcation points between stable equilibrium point and limit cycle in a theoretical model with noise. The distinct dynamics of spike trains and interspike interval histogram (ISIH) of these stochastic rhythms were identified and found to build a relationship to the coexisting behaviors or fixed firing frequency of four different types of bifurcations. Furthermore, noise evokes coherence resonances near bifurcation points and plays important roles in enhancing information. The stochastic rhythms corresponding to Hopf bifurcation points with fixed firing frequency exhibited stronger coherence degree and a sharper peak in the power spectrum of the spike trains than those corresponding to saddle-node bifurcation points without fixed firing frequency. Moreover, the stochastic firing patterns changed to a depolarization resting state as the extracellular potassium concentration increased for the injured nerve fiber related to pathological pain or static blood pressure level increased for aortic depressor nerve fiber, and firing frequency decreased, which were different from the physiological viewpoint that firing frequency increased with increasing pressure level or potassium concentration. This shows that rhythms or firing patterns can reflect pressure or ion concentration information related to pathological pain information. Our results present the dynamics of stochastic firing patterns near bifurcation points, which are helpful for the identification of both dynamics and physiological roles of complex neural firing patterns or rhythms, and the roles of noise.

  9. A hydroclimatic model of global fire patterns

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    Boer, Matthias

    2015-04-01

    Satellite-based earth observation is providing an increasingly accurate picture of global fire patterns. The highest fire activity is observed in seasonally dry (sub-)tropical environments of South America, Africa and Australia, but fires occur with varying frequency, intensity and seasonality in almost all biomes on Earth. The particular combination of these fire characteristics, or fire regime, is known to emerge from the combined influences of climate, vegetation, terrain and land use, but has so far proven difficult to reproduce by global models. Uncertainty about the biophysical drivers and constraints that underlie current global fire patterns is propagated in model predictions of how ecosystems, fire regimes and biogeochemical cycles may respond to projected future climates. Here, I present a hydroclimatic model of global fire patterns that predicts the mean annual burned area fraction (F) of 0.25° x 0.25° grid cells as a function of the climatic water balance. Following Bradstock's four-switch model, long-term fire activity levels were assumed to be controlled by fuel productivity rates and the likelihood that the extant fuel is dry enough to burn. The frequency of ignitions and favourable fire weather were assumed to be non-limiting at long time scales. Fundamentally, fuel productivity and fuel dryness are a function of the local water and energy budgets available for the production and desiccation of plant biomass. The climatic water balance summarizes the simultaneous availability of biologically usable energy and water at a site, and may therefore be expected to explain a significant proportion of global variation in F. To capture the effect of the climatic water balance on fire activity I focused on the upper quantiles of F, i.e. the maximum level of fire activity for a given climatic water balance. Analysing GFED4 data for annual burned area together with gridded climate data, I found that nearly 80% of the global variation in the 0.99 quantile of F

  10. Soldiers and Marksmen Under Fire: Monitoring Performance with Neural Correlates of Small Arms Fire Localization

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    Jason eSherwin

    2013-03-01

    Full Text Available Important decisions in the heat of battle occur rapidly and a key aptitude of a good combat soldier is the ability to determine whether he is under fire. This rapid decision requires the soldier to make a judgment in a fraction of a second, based on a barrage of multisensory cues coming from the auditory, tactile and visual domains. The present study uses an auditory oddball paradigm to examine listener ability to differentiate shooter locations from audio recordings of small arms fire. More importantly, we address the neural correlates involved in this rapid decision process by employing single-trial analysis of electroencephalography (EEG. In particular, we examine small arms expert listeners as they differentiate the sounds of small arms firing events recorded at different observer positions relative to a shooter. Using signal detection theory, we find clear neural signatures related to shooter firing angle by identifying the times of neural discrimination on a trial-to-trial basis. Similar to previous results in oddball experiments, we find common windows relative to the response and the stimulus when neural activity discriminates between target stimuli (forward fire: observer 0° to firing angle vs. standards (off-axis fire: observer 90° to firing angle. We also find, using windows of maximum discrimination, that auditory target vs. standard discrimination yields neural sources in Brodmann Area 19 (BA 19, i.e., in the visual cortex. In summary, we show that single-trial analysis of EEG yields informative scalp distributions and source current localization of discriminating activity when the small arms experts discriminate between forward and off-axis fire observer positions. Furthermore, this perceptual decision implicates brain regions involved in visual processing, even though the task is purely auditory. Finally, we utilize these techniques to quantify the level of expertise in these subjects for the chosen task, having implications for

  11. Recognizing changing seasonal patterns using neural networks

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    Ph.H.B.F. Franses (Philip Hans); G. Draisma (Gerrit)

    1997-01-01

    textabstractIn this paper we propose a graphical method based on an artificial neural network model to investigate how and when seasonal patterns in macroeconomic time series change over time. Neural networks are useful since the hidden layer units may become activated only in certain seasons or

  12. Natural Firing Patterns Imply Low Sensitivity of Synaptic Plasticity to Spike Timing Compared with Firing Rate.

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    Graupner, Michael; Wallisch, Pascal; Ostojic, Srdjan

    2016-11-02

    Synaptic plasticity is sensitive to the rate and the timing of presynaptic and postsynaptic action potentials. In experimental protocols inducing plasticity, the imposed spike trains are typically regular and the relative timing between every presynaptic and postsynaptic spike is fixed. This is at odds with firing patterns observed in the cortex of intact animals, where cells fire irregularly and the timing between presynaptic and postsynaptic spikes varies. To investigate synaptic changes elicited by in vivo-like firing, we used numerical simulations and mathematical analysis of synaptic plasticity models. We found that the influence of spike timing on plasticity is weaker than expected from regular stimulation protocols. Moreover, when neurons fire irregularly, synaptic changes induced by precise spike timing can be equivalently induced by a modest firing rate variation. Our findings bridge the gap between existing results on synaptic plasticity and plasticity occurring in vivo, and challenge the dominant role of spike timing in plasticity. Synaptic plasticity, the change in efficacy of connections between neurons, is thought to underlie learning and memory. The dominant paradigm posits that the precise timing of neural action potentials (APs) is central for plasticity induction. This concept is based on experiments using highly regular and stereotyped patterns of APs, in stark contrast with natural neuronal activity. Using synaptic plasticity models, we investigated how irregular, in vivo-like activity shapes synaptic plasticity. We found that synaptic changes induced by precise timing of APs are much weaker than suggested by regular stimulation protocols, and can be equivalently induced by modest variations of the AP rate alone. Our results call into question the dominant role of precise AP timing for plasticity in natural conditions. Copyright © 2016 Graupner et al.

  13. Interdependencies of Neural Impulse Pattern and Synchronization

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    Braun, Hans; Postnova, Svetlana; Schneider, Horst

    2008-03-01

    Neuronal synchronization plays a crucial role in many physiological functions such as information binding and wake-sleep transitions as well as in pathophysiological processes like Parkinson's disease and epileptic seizures. The occurrence of synchronized activity is often associated with significant alterations of the neuronal impulse pattern, mostly with a transition from tonic firing to burst discharges. We have used Hodgkin-Huxley type simulations to study how alterations of individual neurons' dynamics influence the synchronization in electrotonic coupled networks. The individual neurons have been tuned from tonic firing to bursting with chaotic dynamics in between. Our results demonstrate that these transitions have significant impact on the neurons' synchronization. Vice versa, the synchronization state can essentially modify the impulse pattern. The most remarkably effects appear when the individual neurons operate in a periodically tonic firing regime close to the transition to chaos.

  14. Learning causes reorganization of neuronal firing patterns to represent related experiences within a hippocampal schema.

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    McKenzie, Sam; Robinson, Nick T M; Herrera, Lauren; Churchill, Jordana C; Eichenbaum, Howard

    2013-06-19

    According to schema theory as proposed by Piaget and Bartlett, learning involves the assimilation of new memories into networks of preexisting knowledge, as well as alteration of the original networks to accommodate the new information. Recent evidence has shown that rats form a schema of goal locations and that the hippocampus plays an essential role in adding new memories to the spatial schema. Here we examined the nature of hippocampal contributions to schema updating by monitoring firing patterns of multiple CA1 neurons as rats learned new goal locations in an environment in which there already were multiple goals. Before new learning, many neurons that fired on arrival at one goal location also fired at other goals, whereas ensemble activity patterns also distinguished different goal events, thus constituting a neural representation that linked distinct goals within a spatial schema. During new learning, some neurons began to fire as animals approached the new goals. These were primarily the same neurons that fired at original goals, the activity patterns at new goals were similar to those associated with the original goals, and new learning also produced changes in the preexisting goal-related firing patterns. After learning, activity patterns associated with the new and original goals gradually diverged, such that initial generalization was followed by a prolonged period in which new memories became distinguished within the ensemble representation. These findings support the view that consolidation involves assimilation of new memories into preexisting neural networks that accommodate relationships among new and existing memories.

  15. Patterns of interval correlations in neural oscillators with adaptation.

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    Schwalger, Tilo; Lindner, Benjamin

    2013-01-01

    Neural firing is often subject to negative feedback by adaptation currents. These currents can induce strong correlations among the time intervals between spikes. Here we study analytically the interval correlations of a broad class of noisy neural oscillators with spike-triggered adaptation of arbitrary strength and time scale. Our weak-noise theory provides a general relation between the correlations and the phase-response curve (PRC) of the oscillator, proves anti-correlations between neighboring intervals for adapting neurons with type I PRC and identifies a single order parameter that determines the qualitative pattern of correlations. Monotonically decaying or oscillating correlation structures can be related to qualitatively different voltage traces after spiking, which can be explained by the phase plane geometry. At high firing rates, the long-term variability of the spike train associated with the cumulative interval correlations becomes small, independent of model details. Our results are verified by comparison with stochastic simulations of the exponential, leaky, and generalized integrate-and-fire models with adaptation.

  16. A preliminary study of wildland fire pattern indicator reliability following an experimental fire

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    Albert Simeoni; Zachary C. Owens; Erik W. Christiansen; Abid Kemal; Michael Gallagher; Kenneth L. Clark; Nicholas Skowronski; Eric V. Mueller; Jan C. Thomas; Simon Santamaria; Rory M. Hadden

    2017-01-01

    An experimental fire was conducted in 2016, in the Pinelands National Reserve of New Jersey, to assess the reliability of the fire pattern indicators used in wildland fire investigation. Objects were planted in the burn area to support the creation of the indicators. Fuel properties and environmental data were recorded. Video and infrared cameras were used to document...

  17. Leader neurons in leaky integrate and fire neural network simulations.

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    Zbinden, Cyrille

    2011-10-01

    In this paper, we highlight the topological properties of leader neurons whose existence is an experimental fact. Several experimental studies show the existence of leader neurons in population bursts of activity in 2D living neural networks (Eytan and Marom, J Neurosci 26(33):8465-8476, 2006; Eckmann et al., New J Phys 10(015011), 2008). A leader neuron is defined as a neuron which fires at the beginning of a burst (respectively network spike) more often than we expect by chance considering its mean firing rate. This means that leader neurons have some burst triggering power beyond a chance-level statistical effect. In this study, we characterize these leader neuron properties. This naturally leads us to simulate neural 2D networks. To build our simulations, we choose the leaky integrate and fire (lIF) neuron model (Gerstner and Kistler 2002; Cessac, J Math Biol 56(3):311-345, 2008), which allows fast simulations (Izhikevich, IEEE Trans Neural Netw 15(5):1063-1070, 2004; Gerstner and Naud, Science 326:379-380, 2009). The dynamics of our lIF model has got stable leader neurons in the burst population that we simulate. These leader neurons are excitatory neurons and have a low membrane potential firing threshold. Except for these two first properties, the conditions required for a neuron to be a leader neuron are difficult to identify and seem to depend on several parameters involved in the simulations themselves. However, a detailed linear analysis shows a trend of the properties required for a neuron to be a leader neuron. Our main finding is: A leader neuron sends signals to many excitatory neurons as well as to few inhibitory neurons and a leader neuron receives only signals from few other excitatory neurons. Our linear analysis exhibits five essential properties of leader neurons each with different relative importance. This means that considering a given neural network with a fixed mean number of connections per neuron, our analysis gives us a way of

  18. Patterns of neural differentiation in melanomas

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    Singh Avantika V

    2010-11-01

    Full Text Available Abstract Background Melanomas, highly malignant tumors arise from the melanocytes which originate as multipotent neural crest cells during neural tube genesis. The purpose of this study is to assess the pattern of neural differentiation in relation to angiogenesis in VGP melanomas using the tumor as a three dimensional system. Methods Tumor-vascular complexes [TVC] are formed at the tumor-stroma interphase, by tumor cells ensheathing angiogenic vessels to proliferate into a mantle of 5 to 6 layers [L1 to L5] forming a perivascular mantle zone [PMZ]. The pattern of neural differentiation is assessed by immunopositivity for HMB45, GFAP, NFP and synaptophysin has been compared in: [a] the general tumor [b] tumor-vascular complexes and [c] perimantle zone [PC] on serial frozen and paraffin sections. Statistical Analysis: ANOVA: Kruskal-Wallis One Way Analysis of Variance; All Pairwise Multiple Comparison Procedures [Tukey Test]. Results The cells abutting on the basement membrane acquire GFAP positivity and extend processes. New layers of tumor cells show a transition between L2 to L3 followed by NFP and Syn positivity in L4&L5. The level of GFAP+vity in L1&L2 directly proportionate to the percentage of NFP/Syn+vity in L4&L5, on comparing pigmented PMZ with poorly pigmented PMZ. Tumor cells in the perimantle zone show high NFP [65%] and Syn [35.4%] positivity with very low GFAP [6.9%] correlating with the positivity in the outer layers. Discussion From this study it is seen that melanoma cells revert to the embryonic pattern of differentiation, with radial glial like cells [GFAP+ve] which further differentiate into neuronal positive cells [NFP&Syn+ve] during angiogenic tumor-vascular interaction, as seen during neurogenesis, to populate the tumor substance.

  19. Cross-border firing and injury patterns

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    Nital Gupta

    2016-01-01

    Full Text Available Introduction: Cross-border firing are increasingly being common in the modern era. The injuries resulting from these low intensity conflicts are a source of anxiety among treating physicians and their respective governments. The provisions are required to minimise the suffering of the victims viz. Mode of injuries, mortality patterns, adequacy of treatment at pre-hospital and tertiary care hospital and provisions to decrease morbidity and mortality for the people living in these areas. Materials and Methods: A retrospective study was conducted in GMCH, Jammu who suffered injuries due to cross border firing in the month of October, 2014. 68 patients were reported in the causality wing. All the patients were referred from level 2 trauma centre. There were 51 males and 17 females out of which 5 were children. The cause of injury, involvement of organ system, cause of mortality and morbidity and loopholes in prehospital management were identified. Results: Sharpnel were the most common cause of injury followed by indirect trauma. The common cause of mortality was abdominal and thoracic injuries. There were 4 deaths at hospital 2 of which were brought dead and 2 died during the course of treatment. There were twenty patients with extremity injuries, fourteen with chest trauma, eleven with abdomen including parineal injuries, three with head injuries, eight with ENT injuries, three with eye injuries and nine with splinters in the back out of which two were in the spinal canal. Conclusion: Prehospital stabilisation, early transport, in-transit resuscitation, immediate surgery if required and implementation of triage model and ATLS protocol has been the key to reduce mortality and morbidity.

  20. Granular neural networks, pattern recognition and bioinformatics

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    Pal, Sankar K; Ganivada, Avatharam

    2017-01-01

    This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules,. The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinf...

  1. Temporal Patterns of Fire in West Kalimantan, Indonesia

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    Murphy, K. J.

    2004-12-01

    Fire is an essential landscape management tool extensively employed in West Kalimantan Indonesia to clear land and prepare agricultural areas. Under typically wet climatic conditions fires are easily controlled and seldom spread into adjacent land cover. However, during droughts induced by strong El Nino events, land management fires threaten vast areas of the landscape threatening endangered species habitat and releasing large amounts of carbon into the atmosphere. This study investigates temporal and spatial variations of fires detected by Moderate-resolution Imaging Spectroradiometer (MODIS) and Along Track Scanning Radiometer between 2000 and 2004 against the MODIS Vegetation Continuous Fields and cultural features manually digitized from Landsat ETM+ scenes. Patterns of fire during phases of the El Niño-La Niña cycle are described and the impacts of fires on orangutan habitat are investigated.

  2. Increased Firing Irregularity as an Emergent Property of Neural-State Transition in Monkey Prefrontal Cortex

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    Sakamoto, Kazuhiro; Katori, Yuichi; Saito, Naohiro; Yoshida, Shun; Aihara, Kazuyuki; Mushiake, Hajime

    2013-01-01

    Flexible behaviors are organized by complex neural networks in the prefrontal cortex. Recent studies have suggested that such networks exhibit multiple dynamical states, and can switch rapidly from one state to another. In many complex systems such as the brain, the early-warning signals that may predict whether a critical threshold for state transitions is approaching are extremely difficult to detect. We hypothesized that increases in firing irregularity are a crucial measure for predicting state transitions in the underlying neuronal circuits of the prefrontal cortex. We used both experimental and theoretical approaches to test this hypothesis. Experimentally, we analyzed activities of neurons in the prefrontal cortex while monkeys performed a maze task that required them to perform actions to reach a goal. We observed increased firing irregularity before the activity changed to encode goal-to-action information. Theoretically, we constructed theoretical generic neural networks and demonstrated that changes in neuronal gain on functional connectivity resulted in a loss of stability and an altered state of the networks, accompanied by increased firing irregularity. These results suggest that assessing the temporal pattern of neuronal fluctuations provides important clues regarding the state stability of the prefrontal network. We also introduce a novel scheme that the prefrontal cortex functions in a metastable state near the critical point of bifurcation. According to this scheme, firing irregularity in the prefrontal cortex indicates that the system is about to change its state and the flow of information in a flexible manner, which is essential for executive functions. This metastable and/or critical dynamical state of the prefrontal cortex may account for distractibility and loss of flexibility in the prefrontal cortex in major mental illnesses such as schizophrenia. PMID:24349020

  3. Dynamics, patterns and causes of fires in Northwestern Amazonia.

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    Dolors Armenteras

    Full Text Available According to recent studies, two widespread droughts occurred in the Amazon basin, one during 2005 and one during 2010. The drought increased the prevalence of climate-driven fires over most of the basin. Given the importance of human-atmosphere-vegetation interactions in tropical rainforests, these events have generated concerns over the vulnerability of this area to climate change. This paper focuses on one of the wettest areas of the basin, Northwestern Amazonia, where the interactions between the climate and fires are much weaker and where little is known about the anthropogenic drivers of fires. We have assessed the response of fires to climate over a ten-year period, and analysed the socio-economic and demographic determinants of fire occurrence. The patterns of fires and climate and their linkages in Northwestern Amazonia differ from the enhanced fire response to climate variation observed in the rest of Amazonia. The highest number of recorded fires in Northwestern Amazonia occurred in 2004 and 2007, and this did not coincide with the periods of extreme drought experienced in Amazonia in 2005 and 2010. Rather, during those years, Northwestern Amazonia experienced a relatively small numbers of fire hotspots. We have shown that fire occurrence correlated well with deforestation and was determined by anthropogenic drivers, mainly small-scale agriculture, cattle ranching (i.e., pastures and active agricultural frontiers (including illegal crops. Thus, the particular climatic conditions for air convergence and rainfall created by proximity to the Andes, coupled with the presence of one of the most active colonisation fronts in the region, make this region differently affected by the general drought-induced fire patterns experienced by the rest of the Amazon. Moreover, the results suggest that, even in this wet region, humans are able to modify the frequency of fires and impact these historically well preserved forests.

  4. Alteration of neural action potential patterns by axonal stimulation: the importance of stimulus location.

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    Crago, Patrick E; Makowski, Nathaniel S

    2014-10-01

    Stimulation of peripheral nerves is often superimposed on ongoing motor and sensory activity in the same axons, without a quantitative model of the net action potential train at the axon endpoint. We develop a model of action potential patterns elicited by superimposing constant frequency axonal stimulation on the action potentials arriving from a physiologically activated neural source. The model includes interactions due to collision block, resetting of the neural impulse generator, and the refractory period of the axon at the point of stimulation. Both the mean endpoint firing rate and the probability distribution of the action potential firing periods depend strongly on the relative firing rates of the two sources and the intersite conduction time between them. When the stimulus rate exceeds the neural rate, neural action potentials do not reach the endpoint and the rate of endpoint action potentials is the same as the stimulus rate, regardless of the intersite conduction time. However, when the stimulus rate is less than the neural rate, and the intersite conduction time is short, the two rates partially sum. Increases in stimulus rate produce non-monotonic increases in endpoint rate and continuously increasing block of neurally generated action potentials. Rate summation is reduced and more neural action potentials are blocked as the intersite conduction time increases. At long intersite conduction times, the endpoint rate simplifies to being the maximum of either the neural or the stimulus rate. This study highlights the potential of increasing the endpoint action potential rate and preserving neural information transmission by low rate stimulation with short intersite conduction times. Intersite conduction times can be decreased with proximal stimulation sites for muscles and distal stimulation sites for sensory endings. The model provides a basis for optimizing experiments and designing neuroprosthetic interventions involving motor or sensory stimulation.

  5. Fire Patterns and Drivers of Fires in the West African Tropical Forest

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    Dwomoh, F. K.; Wimberly, M. C.

    2015-12-01

    The West African tropical forest (referred to as the Upper Guinean forest, UGF), is a global biodiversity hotspot providing vital ecosystem services for the region's socio-economic and environmental wellbeing. It is also one of the most fragmented and human-modified tropical forest ecosystems, with the only remaining large patches of original forests contained in protected areas. However, these remnant forests are susceptible to continued fire-mediated degradation and forest loss due to intense climatic, demographic and land use pressures. We analyzed human and climatic drivers of fire activity in the sub-region to better understand the spatial and temporal patterns of these risks. We utilized MODIS active fire and burned area products to identify fire activity within the sub-region. We measured climatic variability using TRMM rainfall data and derived indicators of human land use from a variety of geospatial datasets. We used a boosted regression trees model to determine the influences of predictor variables on fire activity. Our analyses indicated that the spatial and temporal variability of precipitation is a key driving factor of fire activity in the UGF. Anthropogenic effects on fire activity in the area were evident through the influences of agriculture and low-density populations. These human footprints in the landscape make forests more susceptible to fires through forest fragmentation, degradation, and fire spread from agricultural areas. Forested protected areas within the forest savanna mosaic experienced frequent fires, whereas the more humid forest areas located in the south and south-western portions of the study area had fewer fires as these rainforests tend to offer some buffering against fire encroachment. These results improve characterization of UGF fire regime and expand our understanding of the spatio-temporal dynamics of tropical forest fires in response to human and climatic pressures.

  6. Firing rate dynamics in recurrent spiking neural networks with intrinsic and network heterogeneity.

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    Ly, Cheng

    2015-12-01

    Heterogeneity of neural attributes has recently gained a lot of attention and is increasing recognized as a crucial feature in neural processing. Despite its importance, this physiological feature has traditionally been neglected in theoretical studies of cortical neural networks. Thus, there is still a lot unknown about the consequences of cellular and circuit heterogeneity in spiking neural networks. In particular, combining network or synaptic heterogeneity and intrinsic heterogeneity has yet to be considered systematically despite the fact that both are known to exist and likely have significant roles in neural network dynamics. In a canonical recurrent spiking neural network model, we study how these two forms of heterogeneity lead to different distributions of excitatory firing rates. To analytically characterize how these types of heterogeneities affect the network, we employ a dimension reduction method that relies on a combination of Monte Carlo simulations and probability density function equations. We find that the relationship between intrinsic and network heterogeneity has a strong effect on the overall level of heterogeneity of the firing rates. Specifically, this relationship can lead to amplification or attenuation of firing rate heterogeneity, and these effects depend on whether the recurrent network is firing asynchronously or rhythmically firing. These observations are captured with the aforementioned reduction method, and furthermore simpler analytic descriptions based on this dimension reduction method are developed. The final analytic descriptions provide compact and descriptive formulas for how the relationship between intrinsic and network heterogeneity determines the firing rate heterogeneity dynamics in various settings.

  7. Neural Network Based Model of an Industrial Oil-Fired Boiler System ...

    African Journals Online (AJOL)

    In this study, an oil-fired boiler system is modeled as a multivariable plant with two inputs (feed water rate and oil-fired flow rate) and two outputs (steam temperature and pressure). The plant parameters are modeled using artificial neural network, based on experimental data collected directly from the physical plant.

  8. Mechanisms of firing patterns in fast-spiking cortical interneurons.

    Directory of Open Access Journals (Sweden)

    David Golomb

    2007-08-01

    Full Text Available Cortical fast-spiking (FS interneurons display highly variable electrophysiological properties. Their spike responses to step currents occur almost immediately following the step onset or after a substantial delay, during which subthreshold oscillations are frequently observed. Their firing patterns include high-frequency tonic firing and rhythmic or irregular bursting (stuttering. What is the origin of this variability? In the present paper, we hypothesize that it emerges naturally if one assumes a continuous distribution of properties in a small set of active channels. To test this hypothesis, we construct a minimal, single-compartment conductance-based model of FS cells that includes transient Na(+, delayed-rectifier K(+, and slowly inactivating d-type K(+ conductances. The model is analyzed using nonlinear dynamical system theory. For small Na(+ window current, the neuron exhibits high-frequency tonic firing. At current threshold, the spike response is almost instantaneous for small d-current conductance, gd, and it is delayed for larger gd. As gd further increases, the neuron stutters. Noise substantially reduces the delay duration and induces subthreshold oscillations. In contrast, when the Na(+ window current is large, the neuron always fires tonically. Near threshold, the firing rates are low, and the delay to firing is only weakly sensitive to noise; subthreshold oscillations are not observed. We propose that the variability in the response of cortical FS neurons is a consequence of heterogeneities in their gd and in the strength of their Na(+ window current. We predict the existence of two types of firing patterns in FS neurons, differing in the sensitivity of the delay duration to noise, in the minimal firing rate of the tonic discharge, and in the existence of subthreshold oscillations. We report experimental results from intracellular recordings supporting this prediction.

  9. A Drone Remote Sensing for Virtual Reality Simulation System for Forest Fires: Semantic Neural Network Approach

    Science.gov (United States)

    Narasimha Rao, Gudikandhula; Jagadeeswara Rao, Peddada; Duvvuru, Rajesh

    2016-09-01

    Wild fires have significant impact on atmosphere and lives. The demand of predicting exact fire area in forest may help fire management team by using drone as a robot. These are flexible, inexpensive and elevated-motion remote sensing systems that use drones as platforms are important for substantial data gaps and supplementing the capabilities of manned aircraft and satellite remote sensing systems. In addition, powerful computational tools are essential for predicting certain burned area in the duration of a forest fire. The reason of this study is to built up a smart system based on semantic neural networking for the forecast of burned areas. The usage of virtual reality simulator is used to support the instruction process of fire fighters and all users for saving of surrounded wild lives by using a naive method Semantic Neural Network System (SNNS). Semantics are valuable initially to have a enhanced representation of the burned area prediction and better alteration of simulation situation to the users. In meticulous, consequences obtained with geometric semantic neural networking is extensively superior to other methods. This learning suggests that deeper investigation of neural networking in the field of forest fires prediction could be productive.

  10. Evaluating fire danger in Brazilian biomes: present and future patterns

    Science.gov (United States)

    Silva, Patrícia; Bastos, Ana; DaCamara, Carlos; Libonati, Renata

    2017-04-01

    Climate change is expected to have a significant impact on fire occurrence and activity, particularly in Brazil, a region known to be fire-prone [1]. The Brazilian savanna, commonly referred to as cerrado, is a fire-adapted biome covering more than 20% of the country's total area. It presents the highest numbers of fire events, making it particularly susceptible to changes in climate. It is thus essential to understand the present fire regimes in Brazilian biomes, in order to better evaluate future patterns. The CPTEC/INPE, the Brazilian Center for Weather Forecasting and Climate Research at the Brazilian National Institute of Space Research developed a fire danger index based on the occurrence of hundreds of thousands of fire events in the main Brazilian biomes [2]: the Meteorological Fire Danger Index (MFDI). This index indicates the predisposition of vegetation to be burned on a given day, for given climate conditions preceding that day. It relies on daily values of air temperature, relative humidity, accumulated precipitation and vegetation cover. In this study we aim to access the capability of the MFDI to accurately replicate present fire conditions for different biomes, with a special focus on cerrado. To this end, we assess the link between the MFDI as calculated by three different reanalysis (ERA-Interim, NCEP/DOE Reanalysis 2 and MERRA-2) and the observed burned area. We further calculate the validated MFDI using a regional climate model, the RCA4 as forced by EC-Earth from CORDEX, to understand the ability of the model to characterize present fire danger. Finally, the need to calibrate the model to better characterize future fire danger was also evaluated. This work was developed within the framework of the Brazilian Fire-Land-Atmosphere System (BrFLAS) Project financed by the Portuguese and Brazilian science foundations, FCT and FAPESP (project references FAPESP/1389/2014 and 2014/20042-2). [1] KRAWCHUK, M.A.; MORITZ, M.A.; PARISIEN, M.A.; VAN DORN, J

  11. Muscular endurance training and motor unit firing patterns during fatigue.

    Science.gov (United States)

    Mettler, Joni A; Griffin, Lisa

    2016-01-01

    With muscular training, the central nervous system may regulate motor unit firing rates to sustain force output and delay fatigue. The aims of this study were to investigate motor unit firing rates and patterns of the adductor pollicis (AdP) muscle in young, able-bodied adults throughout a sustained submaximal isometric fatiguing contraction and postactivation potentiation pre-post 4 weeks of muscular endurance training. Fifteen participants (training group: N = 10; control group: N = 5) performed maximal voluntary contractions (MVCs) and a sustained isometric 20 % MVC fatigue task pre-post training. Single-motor-unit potentials were recorded from the AdP during the fatigue task with intramuscular fine-wire electrodes. Twitch force potentiation was measured during single-pulse electrical stimulation of the ulnar nerve before and after MVCs. The training group endurance trained their AdP muscle at 20 % MVC for 4 weeks. Mean motor unit firing rates were calculated every 5 % of endurance time (ET). ET increased by 45.2 ± 8.7 % (p endurance training. Although ET increased, mean motor unit firing rates during the fatigue task did not change significantly with training. The general motor unit firing pattern consisted of an initial slowing followed by an increase in firing rate late in fatigue and remained consistent pre-post training. Potentiation did not change following training. These data suggest that the ability of the neuromuscular system to sustain motor unit firing rate may serve as a mechanism to augment the duration of submaximal muscle performance and delay muscular fatigue.

  12. Patterns recognition of electric brain activity using artificial neural networks

    Science.gov (United States)

    Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

    2017-04-01

    An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

  13. Interannual variations in fire weather, fire extent, and synoptic-scale circulation patterns in northern California and Oregon

    Science.gov (United States)

    Trouet, Valerie; Taylor, Alan H.; Carleton, Andrew M.; Skinner, Carl N.

    2009-03-01

    The Mediterranean climate region on the west coast of the United States is characterized by wet winters and dry summers, and by high fire activity. The importance of synoptic-scale circulation patterns (ENSO, PDO, PNA) on fire-climate interactions is evident in contemporary fire data sets and in pre-Euroamerican tree-ring-based fire records. We investigated how interannual variability in two fire weather indices, the Haines index (HI) and the Energy Release Component (ERC), in the Mediterranean region of southern Oregon and northern California is related to atmospheric circulation and fire extent. Years with high and low fire weather index values corresponded to years with a high and low annual area burned, respectively. HI combines atmospheric moisture with atmospheric instability and variation in HI was more strongly associated with interannual variation in wildfire extent than ERC, which is based on moisture alone. The association between fire extent and HI was also higher for fires in southern Oregon than in northern California. In terms of synoptic-scale circulation patterns, years of high fire risk (i.e., increased potential for erratic fire behavior, represented by HI and ERC) were associated with positive winter PNA and PDO conditions, characterized by enhanced regional mid-tropospheric ridging and low atmospheric moisture. The time lag we found between fire risk potential and prior winter circulation patterns could contribute to the development of long-lead fire-climate forecasting.

  14. Part 2-The firings of many neurons and their density; the neural network its connections and field of firings.

    Science.gov (United States)

    Saaty, Thomas

    2017-02-01

    This paper is concerned with the firing of many neurons and the synthesis of these firings to develop functions and their transforms which relate chemical and electrical phenomena to the physical world. The density of such functions in the most general spaces that we encounter allows us to use linear combinations of them to approximate arbitrarily close to any phenomenon we encounter, imagine or think about. Absence of the technology needed to represent all the senses and the mathematical difficulty of making geometric representations of functions of a complex and of more general division algebra variables make it difficult to validate the mathematical outcome of this approach to neural firings. But we think that this problem will be solved in the not-too-distant future when at least the senses of smell, taste and touch would have been so mathematized that it is possible to instill these qualities in robots in some fashion. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Uncorrelated Neural Firing in Mouse Visual Cortex during Spontaneous Retinal Waves

    Directory of Open Access Journals (Sweden)

    Matthew T. Colonnese

    2017-09-01

    Full Text Available Synchronous firing among the elements of forming circuits is critical for stabilization of synapses. Understanding the nature of these local network interactions during development can inform models of circuit formation. Within cortex, spontaneous activity changes throughout development. Unlike the adult, early spontaneous activity occurs in discontinuous population bursts separated by long silent periods, suggesting a high degree of local synchrony. However, whether the micro-patterning of activity within early bursts is unique to this early age and specifically tuned for early development is poorly understood, particularly within the column. To study this we used single-shank multi-electrode array recordings of spontaneous activity in the visual cortex of non-anesthetized neonatal mice to quantify single-unit firing rates, and applied multiple measures of network interaction and synchrony throughout the period of map formation and immediately after eye-opening. We find that despite co-modulation of firing rates on a slow time scale (hundreds of ms, the number of coactive neurons, as well as pair-wise neural spike-rate correlations, are both lower before eye-opening. In fact, on post-natal days (P6–9 correlated activity was lower than expected by chance, suggesting active decorrelation of activity during early bursts. Neurons in lateral geniculate nucleus developed in an opposite manner, becoming less correlated after eye-opening. Population coupling, a measure of integration in the local network, revealed a population of neurons with particularly strong local coupling present at P6–11, but also an adult-like diversity of coupling at all ages, suggesting that a neuron’s identity as locally or distally coupled is determined early. The occurrence probabilities of unique neuronal “words” were largely similar at all ages suggesting that retinal waves drive adult-like patterns of co-activation. These findings suggest that the bursts of

  16. Satellite observations for describing fire patterns and climate-related fire drivers in the Brazilian savannas

    Science.gov (United States)

    Verola Mataveli, Guilherme Augusto; Siqueira Silva, Maria Elisa; Pereira, Gabriel; da Silva Cardozo, Francielle; Shinji Kawakubo, Fernando; Bertani, Gabriel; Cezar Costa, Julio; de Cássia Ramos, Raquel; Valéria da Silva, Viviane

    2018-01-01

    In the Brazilian savannas (Cerrado biome) fires are natural and a tool for shifting land use; therefore, temporal and spatial patterns result from the interaction of climate, vegetation condition and human activities. Moreover, orbital sensors are the most effective approach to establish patterns in the biome. We aimed to characterize fire, precipitation and vegetation condition regimes and to establish spatial patterns of fire occurrence and their correlation with precipitation and vegetation condition in the Cerrado. The Cerrado was first and second biome for the occurrence of burned areas (BA) and hotspots, respectively. Occurrences are higher during the dry season and in the savanna land use. Hotspots and BA tend to decrease, and concentrate in the north, but more intense hotspots are not necessarily located where concentration is higher. Spatial analysis showed that averaged and summed values can hide patterns, such as for precipitation, which has the lowest average in August, but minimum precipitation in August was found in 7 % of the Cerrado. Usually, there is a 2-3-month lag between minimum precipitation and maximum hotspots and BA, while minimum VCI and maximum hotspots and BA occur in the same month. Hotspots and BA are better correlated with VCI than precipitation, qualifying VCI as an indicator of the susceptibility of vegetation to ignition.

  17. Beyond Pattern Recognition With Neural Nets

    Science.gov (United States)

    Arsenault, Henri H.; Macukow, Bohdan

    1989-02-01

    Neural networks are finding many areas of application. Although they are particularly well-suited for applications related to associative recall such as content-addressable memories, neural nets can perform many other applications ranging from logic operations to the solution of optimization problems. The training of a recently introduced model to perform boolean logical operations such as XOR is described. Such simple systems can be combined to perform any complex boolean operation. Any complex task consisting of parallel and serial operations including fuzzy logic that can be described in terms of input-output relations can be accomplished by combining modules such as the ones described here. The fact that some modules can carry out their functions even when their inputs contain erroneous data, and the fact that each module can carry out its functions in parallel with itself and other modules promises some interesting applications.

  18. Temporal-pattern learning in neural models

    CERN Document Server

    Genís, Carme Torras

    1985-01-01

    While the ability of animals to learn rhythms is an unquestionable fact, the underlying neurophysiological mechanisms are still no more than conjectures. This monograph explores the requirements of such mechanisms, reviews those previously proposed and postulates a new one based on a direct electric coding of stimulation frequencies. Experi­ mental support for the option taken is provided both at the single neuron and neural network levels. More specifically, the material presented divides naturally into four parts: a description of the experimental and theoretical framework where this work becomes meaningful (Chapter 2), a detailed specifica­ tion of the pacemaker neuron model proposed together with its valida­ tion through simulation (Chapter 3), an analytic study of the behavior of this model when submitted to rhythmic stimulation (Chapter 4) and a description of the neural network model proposed for learning, together with an analysis of the simulation results obtained when varying seve­ ral factors r...

  19. Improved Discriminability of Spatiotemporal Neural Patterns in Rat Motor Cortical Areas as Directional Choice Learning Progresses

    Directory of Open Access Journals (Sweden)

    Hongwei eMao

    2015-03-01

    Full Text Available Animals learn to choose a proper action among alternatives to improve their odds of success in food foraging and other activities critical for survival. Through trial-and-error, they learn correct associations between their choices and external stimuli. While a neural network that underlies such learning process has been identified at a high level, it is still unclear how individual neurons and a neural ensemble adapt as learning progresses. In this study, we monitored the activity of single units in the rat medial and lateral agranular (AGm and AGl, respectively areas as rats learned to make a left or right side lever press in response to a left or right side light cue. We noticed that rat movement parameters during the performance of the directional choice task quickly became stereotyped during the first 2-3 days or sessions. But learning the directional choice problem took weeks to occur. Accompanying rats’ behavioral performance adaptation, we observed neural modulation by directional choice in recorded single units. Our analysis shows that ensemble mean firing rates in the cue-on period did not change significantly as learning progressed, and the ensemble mean rate difference between left and right side choices did not show a clear trend of change either. However, the spatiotemporal firing patterns of the neural ensemble exhibited improved discriminability between the two directional choices through learning. These results suggest a spatiotemporal neural coding scheme in a motor cortical neural ensemble that may be responsible for and contributing to learning the directional choice task.

  20. Firing patterns of muscle vasoconstrictor neurones in respiratory disease

    Directory of Open Access Journals (Sweden)

    Vaughan G Macefield

    2012-05-01

    Full Text Available Because the cardiovascular system and respiration are so intimately coupled, disturbances in respiratory control often lead to disturbances in cardiovascular control. Obstructive Sleep Apnoea (OSA, Chronic Obstructive Pulmonary Disease (COPD and Bronchiectasis (BE are all associated with a greatly elevated muscle vasoconstrictor drive (muscle sympathetic nerve activity; MSNA. Indeed, the increase in MSNA is comparable to that seen in congestive heart failure (CHF, in which the increase in MSNA compensates for the reduced cardiac output and thereby assists in maintaining blood pressure. However, in OSA – but not COPD or BE – the increase in MSNA can lead to hypertension. Here, the features of the sympathoexcitation in OSA, COPD and BE are reviewed in terms of the firing properties of post-ganglionic muscle vasoconstrictor neurones. Compared to healthy subjects with low levels of resting MSNA, single-unit recordings revealed that the augmented MSNA seen in OSA, BE, COPD and CHF were each associated with an increase in firing probability and mean firing rates of individual neurones. However, unlike patients with heart failure, all patients with respiratory disease exhibited an increase in multiple within-burst firing which, it is argued, reflects an increase in central sympathetic drive. Similar patterns to those seen in OSA, COPD and BE were seen in healthy subjects during an acute increase in muscle vasoconstrictor drive. These observations emphasise the differences by which the sympathetic nervous system grades its output in health and disease, with an increase in firing probability of active neurones and recruitment of additional neurones being the dominant mechanisms.

  1. A simulation study investigating the impact of dendritic morphology and synaptic topology on neuronal firing patterns.

    Science.gov (United States)

    Chen, Jen-Yung

    2010-04-01

    In the brain, complex information interactions among neurons span several spatial and temporal scales, making it extremely difficult to identify the principles governing neural information processing. In this study, we used computational models to investigate the impact of dendritic morphology and synaptic topology on patterns of neuronal firing. We first constructed Hodgkin-Huxley-type neuron models that possessed dendrites with different morphological features. We then simulated the responses of these neurons to a number of spatiotemporal input patterns. The similarity between neuronal responses to different patterned inputs was effectively evaluated by a novel combination of metric space analysis and multidimensional scaling analyses. The results showed that neurons with different morphological or anatomical features exhibit differences in stimulus-specific temporal encoding and firing reliability. These findings support the idea that in addition to biophysical membrane properties, the dendritic morphology and the synaptic topology of a neuron can play a significant role in neuronal information processing and may directly contribute to various brain functions.

  2. Feed Forward Neural Network Algorithm for Frequent Patterns Mining

    OpenAIRE

    Dr. K.R.Pardasani; Sanjay Sharma; Amit Bhagat

    2010-01-01

    Association rule mining is used to find relationships among items in large data sets. Frequent patterns mining is an important aspect in association rule mining. In this paper, an efficient algorithm named Apriori-Feed Forward(AFF) based on Apriori algorithm and the Feed Forward Neural Network is presented to mine frequent patterns. Apriori algorithm scans database many times to generate frequent itemsets whereas Apriori-Feed Forward(AFF) algorithm scans database Only Once. Computational resu...

  3. Climate, fire size, and biophysical setting control fire severity and spatial pattern in the northern Cascade Range, USA.

    Science.gov (United States)

    Cansler, C Alina; McKenzie, Donald

    2014-07-01

    Warmer and drier climate over the past few decades has brought larger fire sizes and increased annual area burned in forested ecosystems of western North America, and continued increases in annual area burned are expected due to climate change. As warming continues, fires may also increase in severity and produce larger contiguous patches of severely burned areas. We used remotely sensed burn-severity data from 125 fires in the northern Cascade Range of Washington, USA, to explore relationships between fire size, severity, and the spatial pattern of severity. We examined relationships between climate and the annual area burned and the size of wildfires over a 25-year period. We tested the hypothesis that increased fire size is commensurate with increased burn severity and increased spatial aggregation of severely burned areas. We also asked how local ecological controls might modulate these relationships by comparing results over the whole study area (the northern Cascade Range) to those from four ecological subsections within it. We found significant positive relationships between climate and fire size, and between fire size and the proportion of high severity and spatial-pattern metrics that quantify the spatial aggregation of high-severity areas within fires, but the strength and significance of these relationships varied among the four subsections. In areas with more contiguous subalpine forests and less complex topography, the proportion and spatial aggregation of severely burned areas were more strongly correlated with fire size. If fire sizes increase in a warming climate, changes in the extent, severity, and spatial pattern of fire regimes are likely to be more pronounced in higher-severity fire regimes with less complex topography and more continuous fuels.

  4. A Feedback Model of Attention Explains the Diverse Effects of Attention on Neural Firing Rates and Receptive Field Structure

    National Research Council Canada - National Science Library

    Miconi, Thomas; VanRullen, Rufin

    2016-01-01

    Visual attention has many effects on neural responses, producing complex changes in firing rates, as well as modifying the structure and size of receptive fields, both in topological and feature space...

  5. [Patterns of action potential firing in cortical neurons of neonatal mice and their electrophysiological property].

    Science.gov (United States)

    Furong, Liu; Shengtian, L I

    2016-05-25

    To investigate patterns of action potential firing in cortical heurons of neonatal mice and their electrophysiological properties. The passive and active membrane properties of cortical neurons from 3-d neonatal mice were observed by whole-cell patch clamp with different voltage and current mode. Three patterns of action potential firing were identified in response to depolarized current injection. The effects of action potential firing patterns on voltage-dependent inward and outward current were found. Neurons with three different firing patterns had different thresholds of depolarized current. In the morphology analysis of action potential, the three type neurons were different in rise time, duration, amplitude and threshold of the first action potential evoked by 80 pA current injection. The passive properties were similar in three patterns of action potential firing. These results indicate that newborn cortical neurons exhibit different patterns of action potential firing with different action potential parameters such as shape and threshold.

  6. Normalized burn ratios link fire severity with patterns of avian occurrence

    Science.gov (United States)

    Rose, Eli T.; Simons, Theodore R.; Klein, Rob; McKerrow, Alexa

    2016-01-01

    ContextRemotely sensed differenced normalized burn ratios (DNBR) provide an index of fire severity across the footprint of a fire. We asked whether this index was useful for explaining patterns of bird occurrence within fire adapted xeric pine-oak forests of the southern Appalachian Mountains.ObjectivesWe evaluated the use of DNBR indices for linking ecosystem process with patterns of bird occurrence. We compared field-based and remotely sensed fire severity indices and used each to develop occupancy models for six bird species to identify patterns of bird occurrence following fire.MethodsWe identified and sampled 228 points within fires that recently burned within Great Smoky Mountains National Park. We performed avian point counts and field-assessed fire severity at each bird census point. We also used Landsat™ imagery acquired before and after each fire to quantify fire severity using DNBR. We used non-parametric methods to quantify agreement between fire severity indices, and evaluated single season occupancy models incorporating fire severity summarized at different spatial scales.ResultsAgreement between field-derived and remotely sensed measures of fire severity was influenced by vegetation type. Although occurrence models using field-derived indices of fire severity outperformed those using DNBR, summarizing DNBR at multiple spatial scales provided additional insights into patterns of occurrence associated with different sized patches of high severity fire.ConclusionsDNBR is useful for linking the effects of fire severity to patterns of bird occurrence, and informing how high severity fire shapes patterns of bird species occurrence on the landscape.

  7. Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

    Directory of Open Access Journals (Sweden)

    Onur Satir

    2016-09-01

    Full Text Available Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are also important player in carbon cycle and decreasing the climate change impacts. This paper discusses forest fire probability mapping of a Mediterranean forestland using a multiple data assessment technique. An artificial neural network (ANN method was used to map forest fire probability in Upper Seyhan Basin (USB in Turkey. Multi-layer perceptron (MLP approach based on back propagation algorithm was applied in respect to physical, anthropogenic, climate and fire occurrence datasets. Result was validated using relative operating characteristic (ROC analysis. Coefficient of accuracy of the MLP was 0.83. Landscape features input to the model were assessed statistically to identify the most descriptive factors on forest fire probability mapping using the Pearson correlation coefficient. Landscape features like elevation (R = −0.43, tree cover (R = 0.93 and temperature (R = 0.42 were strongly correlated with forest fire probability in the USB region.

  8. Dynamics of a modified Hindmarsh-Rose neural model with random perturbations: Moment analysis and firing activities

    Science.gov (United States)

    Mondal, Argha; Upadhyay, Ranjit Kumar

    2017-11-01

    In this paper, an attempt has been made to understand the activity of mean membrane voltage and subsidiary system variables with moment equations (i.e., mean, variance and covariance's) under noisy environment. We consider a biophysically plausible modified Hindmarsh-Rose (H-R) neural system injected by an applied current exhibiting spiking-bursting phenomenon. The effects of predominant parameters on the dynamical behavior of a modified H-R system are investigated. Numerically, it exhibits period-doubling, period halving bifurcation and chaos phenomena. Further, a nonlinear system has been analyzed for the first and second order moments with additive stochastic perturbations. It has been solved using fourth order Runge-Kutta method and noisy systems by Euler's scheme. It has been demonstrated that the firing properties of neurons to evoke an action potential in a certain parameter space of the large exact systems can be estimated using an approximated model. Strong stimulation can cause a change in increase or decrease of the firing patterns. Corresponding to a fixed set of parameter values, the firing behavior and dynamical differences of the collective variables of a large, exact and approximated systems are investigated.

  9. Spatial consistency of neural firing regulates long-range local field potential synchronization: a computational study.

    Science.gov (United States)

    Sato, Naoyuki

    2015-02-01

    Local field potentials (LFPs) are thought to integrate neuronal processes within the range of a few millimeters of radius, which corresponds to the scale of multiple columns. In this study, the model of LFP in the visual cortex proposed by Mazzoni et al. (2008) was adapted to organize a network of two cortical areas, in which pyramidal neurons were divided into two sub-population modeling columns with spatially organized connections to neurons in other areas. Using the model enabled the relationship between neural firing and LFP to be evaluated, in addition to the LFP coherence between the two areas. Results showed that: (1) neurons in a particular sub-population generated the LFP in the area; (2) the spatial consistency of neural firing in the two areas was strongly correlated with LFP coherence; and (3) this consistency was capable of regulating LFP coherence in a lower frequency band, which was originally introduced to neurons in a particular sub-population. These results were derived from a winner-take-all operation in the columnar structure; thus, they are expected to be common in the cortex. It is suggested that the spatial consistency of neural firing is essential for regulating long-range LFP synchronization, which would facilitate neuronal integration processes over multiple cortical areas. Copyright © 2014 Elsevier Ltd. All rights reserved.

  10. Trends in fire patterns in a southern African savanna under alternative land use practices

    Science.gov (United States)

    A. T. Hudak; D. H. K. Fairbanks; B. H. Brockett

    2004-01-01

    Climate, topography, vegetation and land use interact to influence fire regimes.Variable fire regimes may promote landscape heterogeneity, diversification in vegetation pattern and biotic diversity. The objective was to compare effects of alternative land use practices on landscape heterogeneity. Patch characteristics of fire scars were measured from 21 annual burn...

  11. Spiking neural network-based control chart pattern recognition

    Directory of Open Access Journals (Sweden)

    Medhat H.A. Awadalla

    2012-03-01

    Full Text Available Due to an increasing competition in products, consumers have become more critical in choosing products. The quality of products has become more important. Statistical Process Control (SPC is usually used to improve the quality of products. Control charting plays the most important role in SPC. Control charts help to monitor the behavior of the process to determine whether it is stable or not. Unnatural patterns in control charts mean that there are some unnatural causes for variations in SPC. Spiking neural networks (SNNs are the third generation of artificial neural networks that consider time as an important feature for information representation and processing. In this paper, a spiking neural network architecture is proposed to be used for control charts pattern recognition (CCPR. Furthermore, enhancements to the SpikeProp learning algorithm are proposed. These enhancements provide additional learning rules for the synaptic delays, time constants and for the neurons thresholds. Simulated experiments have been conducted and the achieved results show a remarkable improvement in the overall performance compared with artificial neural networks.

  12. Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

    Directory of Open Access Journals (Sweden)

    Andrea Maesani

    2015-11-01

    Full Text Available The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.

  13. The shift from a response strategy to object-in-place strategy during learning is accompanied by a matching shift in neural firing correlates in the hippocampus.

    Science.gov (United States)

    Lee, Inah; Kim, Jangjin

    2010-08-01

    Hippocampal-dependent tasks often involve specific associations among stimuli (including egocentric information), and such tasks are therefore prone to interference from irrelevant task strategies before a correct strategy is found. Using an object-place paired-associate task, we investigated changes in neural firing patterns in the hippocampus in association with a shift in strategy during learning. We used an object-place paired-associate task in which a pair of objects was presented in two different arms of a radial maze. Each object was associated with reward only in one of the arms, thus requiring the rats to consider both object identity and its location in the maze. Hippocampal neurons recorded in CA1 displayed a dynamic transition in their firing patterns during the acquisition of the task across days, and this corresponded to a shift in strategy manifested in behavioral data. Specifically, before the rats learned the task, they chose an object that maintained a particular egocentric relationship with their body (response strategy) irrespective of the object identity. However, as the animal acquired the task, it chose an object according to both its identity and the associated location in the maze (object-in-place strategy). We report that CA1 neurons in the hippocampus changed their prospective firing correlates according to the dominant strategy (i.e., response versus object-in-place strategy) employed at a given stage of learning. The results suggest that neural firing pattern in the hippocampus is heavily influenced by the task demand hypothesized by the animal and the firing pattern changes flexibly as the perceived task demand changes.

  14. Bifurcation and Firing Patterns of the Pancreatic β-Cell

    Science.gov (United States)

    Wang, Jing; Liu, Shenquan; Liu, Xuanliang; Zeng, Yanjun

    Using a model of individual isolated pancreatic β-cells, we investigated bifurcation diagrams of interspike intervals (ISIs) and largest Lyapunov exponents (LLE), which clearly demonstrated a wide range of transitions between different firing patterns. The numerical simulation results revealed the effect of different time constants and ion channels on the neuronal discharge rhythm. Furthermore, an individual cell exhibited tonic spiking, square-wave bursting, and tapered bursting. Additionally, several bifurcation phenomena can be observed in this paper, such as period-doubling, period-adding, inverse period-doubling and inverse period-adding scenarios. In addition, we researched the mechanisms underlying two kinds of bursting (tapered and square-wave bursting) by use of fast-slow dynamics analysis. Finally, we analyzed the codimension-two bifurcation of the fast subsystem and studied cusp bifurcation, generalized Hopf (or Bautin) bifurcation and Bogdanov-Takens bifurcation.

  15. The effects of dynamical synapses on firing rate activity: a spiking neural network model.

    Science.gov (United States)

    Khalil, Radwa; Moftah, Marie Z; Moustafa, Ahmed A

    2017-11-01

    Accumulating evidence relates the fine-tuning of synaptic maturation and regulation of neural network activity to several key factors, including GABA A signaling and a lateral spread length between neighboring neurons (i.e., local connectivity). Furthermore, a number of studies consider short-term synaptic plasticity (STP) as an essential element in the instant modification of synaptic efficacy in the neuronal network and in modulating responses to sustained ranges of external Poisson input frequency (IF). Nevertheless, evaluating the firing activity in response to the dynamical interaction between STP (triggered by ranges of IF) and these key parameters in vitro remains elusive. Therefore, we designed a spiking neural network (SNN) model in which we incorporated the following parameters: local density of arbor essences and a lateral spread length between neighboring neurons. We also created several network scenarios based on these key parameters. Then, we implemented two classes of STP: (1) short-term synaptic depression (STD) and (2) short-term synaptic facilitation (STF). Each class has two differential forms based on the parametric value of its synaptic time constant (either for depressing or facilitating synapses). Lastly, we compared the neural firing responses before and after the treatment with STP. We found that dynamical synapses (STP) have a critical differential role on evaluating and modulating the firing rate activity in each network scenario. Moreover, we investigated the impact of changing the balance between excitation (E) and inhibition (I) on stabilizing this firing activity. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  16. Electrophysiological evidence of mediolateral functional dichotomy in the rat nucleus accumbens during cocaine self-administration II: phasic firing patterns.

    Science.gov (United States)

    Fabbricatore, Anthony T; Ghitza, Udi E; Prokopenko, Volodymyr F; West, Mark O

    2010-05-01

    In the cocaine self-administering rat, individual nucleus accumbens (NAcc) neurons exhibit phasic changes in firing rate within minutes and/or seconds of lever presses (i.e. slow phasic and rapid phasic changes, respectively). To determine whether neurons that demonstrate these changes during self-administration sessions are differentially distributed in the NAcc, rats were implanted with jugular catheters and microwire arrays in different NAcc subregions (core, dorsal shell, ventromedial shell, ventrolateral shell, or rostral pole). Neural recording sessions were typically conducted on days 13-17 of cocaine self-administration (0.77 mg/kg per 0.2-mL infusion; fixed-ratio 1 schedule of reinforcement; 6-h daily sessions). Pre-press rapid phasic firing rate changes were greater in lateral accumbal (core and ventrolateral shell) than in medial accumbal (dorsal shell and rostral pole shell) subregions. Slow phasic pattern analysis revealed that reversal latencies of neurons that exhibited change + reversal patterns differed mediolaterally: medial NAcc neurons exhibited more early reversals and fewer progressive/late reversals than lateral NAcc neurons. Comparisons of firing patterns within individual neurons across time bases indicated that lateral NAcc pre-press rapid phasic increases were correlated with tonic increases. Tonic decreases were correlated with slow phasic patterns in individual medial NAcc neurons, indicative of greater pharmacological sensitivity of neurons in this region. On the other hand, the bias of the lateral NAcc towards increased pre-press rapid phasic activity, coupled with a greater prevalence of tonic increase firing, may reflect particular sensitivity of these neurons to excitatory afferent signaling and perhaps differential pharmacological influences on firing rates between regions.

  17. Mild blast events alter anxiety, memory, and neural activity patterns in the anterior cingulate cortex.

    Directory of Open Access Journals (Sweden)

    Kun Xie

    Full Text Available There is a general interest in understanding of whether and how exposure to emotionally traumatizing events can alter memory function and anxiety behaviors. Here we have developed a novel laboratory-version of mild blast exposure comprised of high decibel bomb explosion sound coupled with strong air blast to mice. This model allows us to isolate the effects of emotionally fearful components from those of traumatic brain injury or bodily injury typical associated with bomb blasts. We demonstrate that this mild blast exposure is capable of impairing object recognition memory, increasing anxiety in elevated O-maze test, and resulting contextual generalization. Our in vivo neural ensemble recording reveal that such mild blast exposures produced diverse firing changes in the anterior cingulate cortex, a region processing emotional memory and inhibitory control. Moreover, we show that these real-time neural ensemble patterns underwent post-event reverberations, indicating rapid consolidation of those fearful experiences. Identification of blast-induced neural activity changes in the frontal brain may allow us to better understand how mild blast experiences result in abnormal changes in memory functions and excessive fear generalization related to post-traumatic stress disorder.

  18. Association between the sympathetic firing pattern and anxiety level in patients with the metabolic syndrome and elevated blood pressure.

    Science.gov (United States)

    Lambert, Elisabeth; Dawood, Tye; Straznicky, Nora; Sari, Carolina; Schlaich, Markus; Esler, Murray; Lambert, Gavin

    2010-03-01

    Recent evidence indicates that stress is associated with obesity, hypertension and metabolic abnormalities. Stress pathways, including both the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system, are activated in individuals with the metabolic syndrome. In order to gain some insight into the relation between sympathetic nervous system activation, metabolic profile and stress, we examined the pattern of sympathetic nervous firing in eight women and 17 men with the metabolic syndrome and elevated blood pressure (BP) in relation to their underlying psychological stress. Both multiunit and single-unit muscle sympathetic nerve activity (MSNA) were recorded by using the technique of microneurography and psychological stress was assessed by Spielberger's State and Trait Anxiety scores and the Beck Depression Inventory II (BDI-II). Women had higher cholesterol levels, higher depressive symptom scores and similar multiunit MSNA compared with the men but displayed a disturbed firing pattern of sympathetic activity as indicated by a higher incidence of multiple spikes per burst (P pattern did not correlate with any aspect of the metabolic profile; however it was significantly associated with anxiety state and trait and the affective component of the BDI scores. In particular, higher incidence of multiple firing (more than two spikes) during a sympathetic neural burst was associated with higher trait anxiety score (R = 0.557, P = 0.004) and higher affective depressive symptoms (R = 0.517, P = 0.008). Somatic symptoms bore no association with the sympathetic firing pattern. These results suggest that chronic mental stress modulates the pattern of sympathetic activity, which, in turn, may confer greater cardiovascular risk on individuals with the metabolic syndrome and elevated BP.

  19. Mixed-Severity Fire Fosters Heterogeneous Spatial Patterns of Conifer Regeneration in a Dry Conifer Forest

    Directory of Open Access Journals (Sweden)

    Sparkle L. Malone

    2018-01-01

    Full Text Available We examined spatial patterns of post-fire regenerating conifers in a Colorado, USA, dry conifer forest 11–12 years following the reintroduction of mixed-severity fire. We mapped and measured all post-fire regenerating conifers, as well as all other post-fire regenerating trees and all residual (i.e., surviving trees, in three 4-ha plots following the 2002 Hayman Fire. Residual tree density ranged from 167 to 197 trees ha−1 (TPH, and these trees were clustered at distances up to 30 m. Post-fire regenerating conifers, which ranged in density from 241 to 1036 TPH, were also clustered at distances up to at least 30 m. Moreover, residual tree locations drove post-fire regenerating conifer locations, with the two showing a pattern of repulsion. Topography and post-fire sprouting tree species locations further drove post-fire conifer regeneration locations. These results provide a foundation for anticipating how the reintroduction of mixed-severity fire may affect long-term forest structure, and also yield insights into how historical mixed-severity fire may have regulated the spatially heterogeneous conditions commonly described for pre-settlement dry conifer forests of Colorado and elsewhere.

  20. Distinct neural patterns enable grasp types decoding in monkey dorsal premotor cortex

    Science.gov (United States)

    Hao, Yaoyao; Zhang, Qiaosheng; Controzzi, Marco; Cipriani, Christian; Li, Yue; Li, Juncheng; Zhang, Shaomin; Wang, Yiwen; Chen, Weidong; Chiara Carrozza, Maria; Zheng, Xiaoxiang

    2014-12-01

    Objective. Recent studies have shown that dorsal premotor cortex (PMd), a cortical area in the dorsomedial grasp pathway, is involved in grasp movements. However, the neural ensemble firing property of PMd during grasp movements and the extent to which it can be used for grasp decoding are still unclear. Approach. To address these issues, we used multielectrode arrays to record both spike and local field potential (LFP) signals in PMd in macaque monkeys performing reaching and grasping of one of four differently shaped objects. Main results. Single and population neuronal activity showed distinct patterns during execution of different grip types. Cluster analysis of neural ensemble signals indicated that the grasp related patterns emerged soon (200-300 ms) after the go cue signal, and faded away during the hold period. The timing and duration of the patterns varied depending on the behaviors of individual monkey. Application of support vector machine model to stable activity patterns revealed classification accuracies of 94% and 89% for each of the two monkeys, indicating a robust, decodable grasp pattern encoded in the PMd. Grasp decoding using LFPs, especially the high-frequency bands, also produced high decoding accuracies. Significance. This study is the first to specify the neuronal population encoding of grasp during the time course of grasp. We demonstrate high grasp decoding performance in PMd. These findings, combined with previous evidence for reach related modulation studies, suggest that PMd may play an important role in generation and maintenance of grasp action and may be a suitable locus for brain-machine interface applications.

  1. Contrasting spatial patterns in active-fire and fire-suppressed Mediterranean climate old-growth mixed conifer forests.

    Directory of Open Access Journals (Sweden)

    Danny L Fry

    Full Text Available In Mediterranean environments in western North America, historic fire regimes in frequent-fire conifer forests are highly variable both temporally and spatially. This complexity influenced forest structure and spatial patterns, but some of this diversity has been lost due to anthropogenic disruption of ecosystem processes, including fire. Information from reference forest sites can help management efforts to restore forests conditions that may be more resilient to future changes in disturbance regimes and climate. In this study, we characterize tree spatial patterns using four-ha stem maps from four old-growth, Jeffrey pine-mixed conifer forests, two with active-fire regimes in northwestern Mexico and two that experienced fire exclusion in the southern Sierra Nevada. Most of the trees were in patches, averaging six to 11 trees per patch at 0.007 to 0.014 ha(-1, and occupied 27-46% of the study areas. Average canopy gap sizes (0.04 ha covering 11-20% of the area were not significantly different among sites. The putative main effects of fire exclusion were higher densities of single trees in smaller size classes, larger proportion of trees (≥ 56% in large patches (≥ 10 trees, and decreases in spatial complexity. While a homogenization of forest structure has been a typical result from fire exclusion, some similarities in patch, single tree, and gap attributes were maintained at these sites. These within-stand descriptions provide spatially relevant benchmarks from which to manage for structural heterogeneity in frequent-fire forest types.

  2. Spatiotemporal patterns of tundra fires: late-Quaternary charcoal records from Alaska

    Science.gov (United States)

    Chipman, M. L.; Hudspith, V.; Higuera, P. E.; Duffy, P. A.; Kelly, R.; Oswald, W. W.; Hu, F. S.

    2015-07-01

    Anthropogenic climate change has altered many ecosystem processes in the Arctic tundra and may have resulted in unprecedented fire activity. Evaluating the significance of recent fires requires knowledge from the paleofire record because observational data in the Arctic span only several decades, much shorter than the natural fire rotation in Arctic tundra regions. Here we report results of charcoal analysis on lake sediments from four Alaskan lakes to infer the broad spatial and temporal patterns of tundra-fire occurrence over the past 35 000 years. Background charcoal accumulation rates are low in all records (range is 0-0.05 pieces cm-2 yr-1), suggesting minimal biomass burning across our study areas. Charcoal peak analysis reveals that the mean fire-return interval (FRI; years between consecutive fire events) ranged from ca. 1650 to 6050 years at our sites, and that the most recent fire events occurred from ca. 880 to 7030 years ago, except for the CE 2007 Anaktuvuk River Fire. These mean FRI estimates are longer than the fire rotation periods estimated for the past 63 years in the areas surrounding three of the four study lakes. This result suggests that the frequency of tundra burning was higher over the recent past compared to the late Quaternary in some tundra regions. However, the ranges of FRI estimates from our paleofire records overlap with the expected values based on fire-rotation-period estimates from the observational fire data, and the differences are statistically insignificant. Together with previous tundra-fire reconstructions, these data suggest that the rate of tundra burning was spatially variable and that fires were extremely rare in our study areas throughout the late Quaternary. Given the rarity of tundra burning over multiple millennia in our study areas and the pronounced effects of fire on tundra ecosystem processes such as carbon cycling, dramatic tundra ecosystem changes are expected if anthropogenic climate change leads to more

  3. Cultured Neural Networks: Optimization of Patterned Network Adhesiveness and Characterization of their Neural Activity

    Directory of Open Access Journals (Sweden)

    W. L. C. Rutten

    2006-01-01

    Full Text Available One type of future, improved neural interface is the “cultured probe”. It is a hybrid type of neural information transducer or prosthesis, for stimulation and/or recording of neural activity. It would consist of a microelectrode array (MEA on a planar substrate, each electrode being covered and surrounded by a local circularly confined network (“island” of cultured neurons. The main purpose of the local networks is that they act as biofriendly intermediates for collateral sprouts from the in vivo system, thus allowing for an effective and selective neuron–electrode interface. As a secondary purpose, one may envisage future information processing applications of these intermediary networks. In this paper, first, progress is shown on how substrates can be chemically modified to confine developing networks, cultured from dissociated rat cortex cells, to “islands” surrounding an electrode site. Additional coating of neurophobic, polyimide-coated substrate by triblock-copolymer coating enhances neurophilic-neurophobic adhesion contrast. Secondly, results are given on neuronal activity in patterned, unconnected and connected, circular “island” networks. For connected islands, the larger the island diameter (50, 100 or 150 μm, the more spontaneous activity is seen. Also, activity may show a very high degree of synchronization between two islands. For unconnected islands, activity may start at 22 days in vitro (DIV, which is two weeks later than in unpatterned networks.

  4. Modeling multiple time scale firing rate adaptation in a neural network of local field potentials.

    Science.gov (United States)

    Lundstrom, Brian Nils

    2015-02-01

    In response to stimulus changes, the firing rates of many neurons adapt, such that stimulus change is emphasized. Previous work has emphasized that rate adaptation can span a wide range of time scales and produce time scale invariant power law adaptation. However, neuronal rate adaptation is typically modeled using single time scale dynamics, and constructing a conductance-based model with arbitrary adaptation dynamics is nontrivial. Here, a modeling approach is developed in which firing rate adaptation, or spike frequency adaptation, can be understood as a filtering of slow stimulus statistics. Adaptation dynamics are modeled by a stimulus filter, and quantified by measuring the phase leads of the firing rate in response to varying input frequencies. Arbitrary adaptation dynamics are approximated by a set of weighted exponentials with parameters obtained by fitting to a desired filter. With this approach it is straightforward to assess the effect of multiple time scale adaptation dynamics on neural networks. To demonstrate this, single time scale and power law adaptation were added to a network model of local field potentials. Rate adaptation enhanced the slow oscillations of the network and flattened the output power spectrum, dampening intrinsic network frequencies. Thus, rate adaptation may play an important role in network dynamics.

  5. Patterns of work attitudes: A neural network approach

    Science.gov (United States)

    Mengov, George D.; Zinovieva, Irina L.; Sotirov, George R.

    2000-05-01

    In this paper we introduce a neural networks based approach to analyzing empirical data and models from work and organizational psychology (WOP), and suggest possible implications for the practice of managers and business consultants. With this method it becomes possible to have quantitative answers to a bunch of questions like: What are the characteristics of an organization in terms of its employees' motivation? What distinct attitudes towards the work exist? Which pattern is most desirable from the standpoint of productivity and professional achievement? What will be the dynamics of behavior as quantified by our method, during an ongoing organizational change or consultancy intervention? Etc. Our investigation is founded on the theoretical achievements of Maslow (1954, 1970) in human motivation, and of Hackman & Oldham (1975, 1980) in job diagnostics, and applies the mathematical algorithm of the dARTMAP variation (Carpenter et al., 1998) of the Adaptive Resonance Theory (ART) neural networks introduced by Grossberg (1976). We exploit the ART capabilities to visualize the knowledge accumulated in the network's long-term memory in order to interpret the findings in organizational research.

  6. Rainfall patterns after fire differentially affect the recruitment of three Mediterranean shrubs

    Directory of Open Access Journals (Sweden)

    J. M. Moreno

    2011-12-01

    Full Text Available In fire-prone environments, the "event-dependent hypothesis" states that plant population changes are driven by the unique set of conditions of a fire (e.g. fire season, climate. Climate variability, in particular changes in rainfall patterns, can be most important for seeder species, since they regenerate after fire from seeds, and for Mediterranean shrublands, given the high yearly variability of rainfall in these ecosystems. Yet, the role of rainfall variability and its interaction with fire characteristics (e.g. fire season on plant populations has received little attention. Here we investigated the changes in seedling emergence and recruitment of three seeder species (Cistus ladanifer, Erica umbellata and Rosmarinus officinalis after fires lit during three different years and at two times (early and late during the fire season. Three plots were burned at each season, for a total of 18 plots burned during the three years. After fire, emerged seedlings were tallied, tagged and monitored during three years (two in the last burning year. Rainfall during the study period was rather variable and, in some years, it was well below average. Postfire seedling emergence varied by a factor of 3 to 12, depending on the species and on the burning year. The bulk of seedling emergence occurred during the first year after fire; seedling recruitment at the end of the study period was tightly correlated with this early emergence. Emergence in Erica and Rosmarinus, but not in Cistus, was correlated with precipitation in the fall and winter immediately after fire, with Erica being the most sensitive to reduced rainfall. Fire season was generally neither an important factor in controlling emergence nor, in particular, recruitment. We discuss how projected changes in rainfall patterns with global warming could alter the balance of species in this shrubland, and could drive some species to near local extinction.

  7. Rainfall patterns after fire differentially affect the recruitment of three Mediterranean shrubs

    Science.gov (United States)

    Moreno, J. M.; Zuazua, E.; Pérez, B.; Luna, B.; Velasco, A.; Resco de Dios, V.

    2011-12-01

    In fire-prone environments, the "event-dependent hypothesis" states that plant population changes are driven by the unique set of conditions of a fire (e.g. fire season, climate). Climate variability, in particular changes in rainfall patterns, can be most important for seeder species, since they regenerate after fire from seeds, and for Mediterranean shrublands, given the high yearly variability of rainfall in these ecosystems. Yet, the role of rainfall variability and its interaction with fire characteristics (e.g. fire season) on plant populations has received little attention. Here we investigated the changes in seedling emergence and recruitment of three seeder species (Cistus ladanifer, Erica umbellata and Rosmarinus officinalis) after fires lit during three different years and at two times (early and late) during the fire season. Three plots were burned at each season, for a total of 18 plots burned during the three years. After fire, emerged seedlings were tallied, tagged and monitored during three years (two in the last burning year). Rainfall during the study period was rather variable and, in some years, it was well below average. Postfire seedling emergence varied by a factor of 3 to 12, depending on the species and on the burning year. The bulk of seedling emergence occurred during the first year after fire; seedling recruitment at the end of the study period was tightly correlated with this early emergence. Emergence in Erica and Rosmarinus, but not in Cistus, was correlated with precipitation in the fall and winter immediately after fire, with Erica being the most sensitive to reduced rainfall. Fire season was generally neither an important factor in controlling emergence nor, in particular, recruitment. We discuss how projected changes in rainfall patterns with global warming could alter the balance of species in this shrubland, and could drive some species to near local extinction.

  8. Enteric neural crest cells regulate vertebrate stomach patterning and differentiation.

    Science.gov (United States)

    Faure, Sandrine; McKey, Jennifer; Sagnol, Sébastien; de Santa Barbara, Pascal

    2015-01-15

    In vertebrates, the digestive tract develops from a uniform structure where reciprocal epithelial-mesenchymal interactions pattern this complex organ into regions with specific morphologies and functions. Concomitant with these early patterning events, the primitive GI tract is colonized by the vagal enteric neural crest cells (vENCCs), a population of cells that will give rise to the enteric nervous system (ENS), the intrinsic innervation of the GI tract. The influence of vENCCs on early patterning and differentiation of the GI tract has never been evaluated. In this study, we report that a crucial number of vENCCs is required for proper chick stomach development, patterning and differentiation. We show that reducing the number of vENCCs by performing vENCC ablations induces sustained activation of the BMP and Notch pathways in the stomach mesenchyme and impairs smooth muscle development. A reduction in vENCCs also leads to the transdifferentiation of the stomach into a stomach-intestinal mixed phenotype. In addition, sustained Notch signaling activity in the stomach mesenchyme phenocopies the defects observed in vENCC-ablated stomachs, indicating that inhibition of the Notch signaling pathway is essential for stomach patterning and differentiation. Finally, we report that a crucial number of vENCCs is also required for maintenance of stomach identity and differentiation through inhibition of the Notch signaling pathway. Altogether, our data reveal that, through the regulation of mesenchyme identity, vENCCs act as a new mediator in the mesenchymal-epithelial interactions that control stomach development. © 2015. Published by The Company of Biologists Ltd.

  9. Increasing memory capacity and reducing spurious states in neural networks by introducing coherent and collective firing.

    Science.gov (United States)

    Koh, Yang Wei; Takatsuka, Kazuo

    2009-05-01

    It is well known that higher-order Hopfield nets called multispin models can increase memory capacity to some extent by extending the direct product of spin states to more than second order. However, a group of neurons can then respond degenerately to different loaded patterns, resulting in many spurious states due to cross-talk effects. We present an idea to increase the number of attracting basins for patterns while suppressing the associated spurious states, by introducing coherent and collective firing in multispin groups. We numerically implement the method and test the number, stability, and basin size of the attractors thus created. Increasing the size of a group of coherent excitation suppresses spurious states, stabilizes loaded patterns, and dramatically increases the number of pattern attractors.

  10. Flow Pattern Identification of Horizontal Two-Phase Refrigerant Flow Using Neural Networks

    Science.gov (United States)

    2015-12-31

    making classification difficult. Consequently, Table 5 shows neural net - work classification results for nine flow patterns. The number of runs...AFRL-RQ-WP-TP-2016-0079 FLOW PATTERN IDENTIFICATION OF HORIZONTAL TWO-PHASE REFRIGERANT FLOW USING NEURAL NETWORKS (POSTPRINT) Abdeel J... NEURAL NETWORKS (POSTPRINT) 5a. CONTRACT NUMBER In-house 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 62203F 6. AUTHOR(S) Abdeel J. Roman and

  11. A NEURAL OSCILLATOR-NETWORK MODEL OF TEMPORAL PATTERN GENERATION

    NARCIS (Netherlands)

    Schomaker, Lambert

    Most contemporary neural network models deal with essentially static, perceptual problems of classification and transformation. Models such as multi-layer feedforward perceptrons generally do not incorporate time as an essential dimension, whereas biological neural networks are inherently temporal

  12. Activity Patterns of Cultured Neural Networks on Micro Electrode Arrays

    National Research Council Canada - National Science Library

    Rutten, Wim

    2001-01-01

    A hybrid neuro-electronic interface is a cell-cultured micro electrode array, acting as a neural information transducer for stimulation and/or recording of neural activity in the brain or the spinal cord...

  13. Exergy diagnosis of coal fired CHP plant with application of neural and regression modelling

    Directory of Open Access Journals (Sweden)

    Stanek Wojciech

    2012-01-01

    Full Text Available Mathematical models of the processes, that proceed in energetic machines and devices, in many cases are very complicated. In such cases, the exact analytical models should be equipped with the auxiliary empirical models that describe those parameters which are difficult to model in a theoretical way. Regression or neural models identified basing on measurements are rather simple and are characterized by relatively short computation time. For this reason they can be effectively applied for simulation and optimization of steering and regulation processes, as well as, for control and thermal diagnosis of operation (eq. power plants or CHP plants. In the paper regression and neural models of thermal processes developed for systems of operation control of thermal plants are presented. Theoretical-empirical model of processes proceeding in coal fired CHP plant have been applied. Simulative calculations basing on these models have been carried out. Results of simulative calculations have been used for the exergetic evaluation of considered power plant. The diagnosis procedure let to investigate the formation of exergy costs in interconnected components of the system of CHP, as well as, investigate the influence of defects in operation of components on exergy losses and on the exergetic cost in other components. [Acknowledgment. The paper has been prepared within the RECENT project (REsearch Center for Energy and New Technologies supported by 7th Framework Programme, Theme 4, Capacities.

  14. Firearm Classification using Neural Networks on Ring of Firing Pin Impression Images

    Directory of Open Access Journals (Sweden)

    SAADI Bin Ahmad KAMARUDDIN

    2012-12-01

    Full Text Available This paper implements two layer neural networks with different feedforward backpropagation algorithms for better performance of firearm classification us-ing numerical features from the ring image. A total of 747 ring images which are extracted from centre of the firing pin impression have been captured from five different pistols of the Parabellum Vector SPI 9mm model. Then, based on finding from the previous studies, the six best geometric moments numerical fea-tures were extracted from those ring images. The elements of the dataset were further randomly divided into the training set (523 elements, testing set (112 el-ements and validation set (112 elements in accordance with the requirement of the supervised learning nature of the backpropagation neural network (BPNN. Empirical results show that a two layer BPNN with a 6-7-5 configura-tion and tansig/tansig transfer functions with ‘trainscg’ training algorithm has produced the best classification result of 98%. The classification result is an improvement compared to the previous studies as well as confirming that the ring image region contains useful information for firearm classification.

  15. Firearm Classification using Neural Networks on Ring of Firing Pin Impression Images

    Directory of Open Access Journals (Sweden)

    Abdul AZIZ JEMAIN

    2013-07-01

    Full Text Available This paper implements two layer neural networks with different feedforward backpropagation algorithms for better performance of firearm classification us-ing numerical features from the ring image. A total of 747 ring images which are extracted from centre of the firing pin impression have been captured from five different pistols of the Parabellum Vector SPI 9mm model. Then, based on finding from the previous studies, the six best geometric moments numerical fea-tures were extracted from those ring images. The elements of the dataset were further randomly divided into the training set (523 elements, testing set (112 el-ements and validation set (112 elements in accordance with the requirement of the supervised learning nature of the backpropagation neural network (BPNN. Empirical results show that a two layer BPNN with a 6-7-5 configura-tion and tansig/tansig transfer functions with ‘trainscg’ training algorithm has produced the best classification result of 98%. The classification result is an improvement compared to the previous studies as well as confirming that the ring image region contains useful information for firearm classification.

  16. Modeling the differentiation of A- and C-type baroreceptor firing patterns

    DEFF Research Database (Denmark)

    Sturdy, Jacob; Ottesen, Johnny T.; Olufsen, Mette

    2017-01-01

    of their axons and their distinct firing patterns elicited in response to specific pressure stimuli. This study has developed a comprehensive model of the afferent baroreceptor discharge built on physiological knowledge of arterial wall mechanics, firing rate responses to controlled pressure stimuli, and ion...... channel dynamics within the baroreceptor neurons. With this model, we were able to predict firing rates observed in previously published experiments in both A- and C-type neurons. These results were obtained by adjusting model parameters determining the maximal ion-channel conductances. The observed...... basal firing, whereas a tenfold higher mechanosensitive conductance is responsible for the greater firing rate observed in A-type neurons. A better understanding of the difference between the two neuron types can potentially be used to gain more insight into the underlying pathophysiology facilitating...

  17. Facilitation of Memory Encoding in Primate Hippocampus by a Neuroprosthesis that Promotes Task Specific Neural Firing

    Science.gov (United States)

    Hampson, Robert E.; Song, Dong; Opris, Ioan; Santos, Lucas M.; Shin, Dae C.; Gerhardt, Greg A.; Marmarelis, Vasilis Z.; Berger, Theodore W.; Deadwyler, Sam A.

    2014-01-01

    Objective Memory accuracy is a major problem in human disease and is the primary factor that defines Alzheimer’s’, aging and dementia resulting from impaired hippocampal function in medial temporal lobe. Development of a hippocampal memory neuroprosthesis that facilitates normal memory encoding in nonhuman primates (NHPs) could provide the basis for improving memory in human disease states. Approach NHPs trained to perform a short-term delayed match to sample (DMS) memory task were examined with multi-neuron recordings from synaptically connected hippocampal cell fields, CA1 and CA3. Recordings were analyzed utilizing a previously developed nonlinear multi-input multi-output (MIMO) neuroprosthetic model, capable of extracting CA3-to-CA1 spatiotemporal firing patterns during DMS performance. Main Results The MIMO model verified that specific CA3-to-CA1 firing patterns were critical for successful encoding of Sample phase information on more difficult DMS trials. This was validated by delivery of successful MIMO-derived encoding patterns via electrical stimulation to the same CA1 recording locations during the Sample phase which facilitated task performance in the subsequent delayed Match phase on difficult trials that required more precise encoding of Sample information. Significance These findings provide the first successful application of a neuroprosthesis designed to enhance and/or repair memory encoding in primate brain. PMID:24216292

  18. Neural-Net Processing of Characteristic Patterns From Electronic Holograms of Vibrating Blades

    Science.gov (United States)

    Decker, Arthur J.

    1999-01-01

    Finite-element-model-trained artificial neural networks can be used to process efficiently the characteristic patterns or mode shapes from electronic holograms of vibrating blades. The models used for routine design may not yet be sufficiently accurate for this application. This document discusses the creation of characteristic patterns; compares model generated and experimental characteristic patterns; and discusses the neural networks that transform the characteristic patterns into strain or damage information. The current potential to adapt electronic holography to spin rigs, wind tunnels and engines provides an incentive to have accurate finite element models lor training neural networks.

  19. Cultured neural networks: Optimisation of patterned network adhesiveness and characterisation of their neural activity

    NARCIS (Netherlands)

    Rutten, Wim; Ruardij, T.G.; Marani, Enrico; Roelofsen, B.H.

    2006-01-01

    One type of future, improved neural interface is the "cultured probe"?. It is a hybrid type of neural information transducer or prosthesis, for stimulation and/or recording of neural activity. It would consist of a microelectrode array (MEA) on a planar substrate, each electrode being covered and

  20. Extreme fire severity patterns in topographic, convective and wind-driven historical wildfires of Mediterranean pine forests.

    Directory of Open Access Journals (Sweden)

    Judit Lecina-Diaz

    Full Text Available Crown fires associated with extreme fire severity are extremely difficult to control. We have assessed fire severity using differenced Normalized Burn Ratio (dNBR from Landsat imagery in 15 historical wildfires of Pinus halepensis Mill. We have considered a wide range of innovative topographic, fuel and fire behavior variables with the purposes of (1 determining the variables that influence fire severity patterns among fires (considering the 15 wildfires together and (2 ascertaining whether different variables affect extreme fire severity within the three fire types (topographic, convective and wind-driven fires. The among-fires analysis showed that fires in less arid climates and with steeper slopes had more extreme severity. In less arid conditions there was more crown fuel accumulation and closer forest structures, promoting high vertical and horizontal fuel continuity and extreme fire severity. The analyses carried out for each fire separately (within fires showed more extreme fire severity in areas in northern aspects, with steeper slopes, with high crown biomass and in climates with more water availability. In northern aspects solar radiation was lower and fuels had less water limitation to growth which, combined with steeper slopes, produced more extreme severity. In topographic fires there was more extreme severity in northern aspects with steeper slopes and in areas with more water availability and high crown biomass; in convection-dominated fires there was also more extreme fire severity in northern aspects with high biomass; while in wind-driven fires there was only a slight interaction between biomass and water availability. This latter pattern could be related to the fact that wind-driven fires spread with high wind speed, which could have minimized the effect of other variables. In the future, and as a consequence of climate change, new zones with high crown biomass accumulated in non-common drought areas will be available to burn

  1. Extreme Fire Severity Patterns in Topographic, Convective and Wind-Driven Historical Wildfires of Mediterranean Pine Forests

    Science.gov (United States)

    Lecina-Diaz, Judit; Alvarez, Albert; Retana, Javier

    2014-01-01

    Crown fires associated with extreme fire severity are extremely difficult to control. We have assessed fire severity using differenced Normalized Burn Ratio (dNBR) from Landsat imagery in 15 historical wildfires of Pinus halepensis Mill. We have considered a wide range of innovative topographic, fuel and fire behavior variables with the purposes of (1) determining the variables that influence fire severity patterns among fires (considering the 15 wildfires together) and (2) ascertaining whether different variables affect extreme fire severity within the three fire types (topographic, convective and wind-driven fires). The among-fires analysis showed that fires in less arid climates and with steeper slopes had more extreme severity. In less arid conditions there was more crown fuel accumulation and closer forest structures, promoting high vertical and horizontal fuel continuity and extreme fire severity. The analyses carried out for each fire separately (within fires) showed more extreme fire severity in areas in northern aspects, with steeper slopes, with high crown biomass and in climates with more water availability. In northern aspects solar radiation was lower and fuels had less water limitation to growth which, combined with steeper slopes, produced more extreme severity. In topographic fires there was more extreme severity in northern aspects with steeper slopes and in areas with more water availability and high crown biomass; in convection-dominated fires there was also more extreme fire severity in northern aspects with high biomass; while in wind-driven fires there was only a slight interaction between biomass and water availability. This latter pattern could be related to the fact that wind-driven fires spread with high wind speed, which could have minimized the effect of other variables. In the future, and as a consequence of climate change, new zones with high crown biomass accumulated in non-common drought areas will be available to burn as extreme

  2. Extreme fire severity patterns in topographic, convective and wind-driven historical wildfires of Mediterranean pine forests.

    Science.gov (United States)

    Lecina-Diaz, Judit; Alvarez, Albert; Retana, Javier

    2014-01-01

    Crown fires associated with extreme fire severity are extremely difficult to control. We have assessed fire severity using differenced Normalized Burn Ratio (dNBR) from Landsat imagery in 15 historical wildfires of Pinus halepensis Mill. We have considered a wide range of innovative topographic, fuel and fire behavior variables with the purposes of (1) determining the variables that influence fire severity patterns among fires (considering the 15 wildfires together) and (2) ascertaining whether different variables affect extreme fire severity within the three fire types (topographic, convective and wind-driven fires). The among-fires analysis showed that fires in less arid climates and with steeper slopes had more extreme severity. In less arid conditions there was more crown fuel accumulation and closer forest structures, promoting high vertical and horizontal fuel continuity and extreme fire severity. The analyses carried out for each fire separately (within fires) showed more extreme fire severity in areas in northern aspects, with steeper slopes, with high crown biomass and in climates with more water availability. In northern aspects solar radiation was lower and fuels had less water limitation to growth which, combined with steeper slopes, produced more extreme severity. In topographic fires there was more extreme severity in northern aspects with steeper slopes and in areas with more water availability and high crown biomass; in convection-dominated fires there was also more extreme fire severity in northern aspects with high biomass; while in wind-driven fires there was only a slight interaction between biomass and water availability. This latter pattern could be related to the fact that wind-driven fires spread with high wind speed, which could have minimized the effect of other variables. In the future, and as a consequence of climate change, new zones with high crown biomass accumulated in non-common drought areas will be available to burn as extreme

  3. Detection of Coal Fires: A Case Study Conducted on Indian Coal Seams Using Neural Network and Particle Swarm Optimization

    Science.gov (United States)

    Singh, B. B.

    2016-12-01

    India produces majority of its electricity from coal but a huge quantity of coal burns every day due to coal fires and also poses a threat to the environment as severe pollutants. In the present study we had demonstrated the usage of Neural Network based approach with an integrated Particle Swarm Optimization (PSO) inversion technique. The Self Potential (SP) data set is used for the early detection of coal fires. The study was conducted over the East Basuria colliery, Jharia Coal Field, Jharkhand, India. The causative source was modelled as an inclined sheet like anomaly and the synthetic data was generated. Neural Network scheme consists of an input layer, hidden layers and an output layer. The input layer corresponds to the SP data and the output layer is the estimated depth of the coal fire. A synthetic dataset was modelled with some of the known parameters such as depth, conductivity, inclination angle, half width etc. associated with causative body and gives a very low misfit error of 0.0032%. Therefore, the method was found accurate in predicting the depth of the source body. The technique was applied to the real data set and the model was trained until a very good correlation of determination `R2' value of 0.98 is obtained. The depth of the source body was found to be 12.34m with a misfit error percentage of 0.242%. The inversion results were compared with the lithologs obtained from a nearby well which corresponds to the L3 coal seam. The depth of the coal fire had exactly matched with the half width of the anomaly which suggests that the fire is widely spread. The inclination angle of the anomaly was 135.510 which resembles the development of the geometrically complex fracture planes. These fractures may be developed due to anisotropic weakness of the ground which acts as passage for the air. As a result coal fires spreads along these fracture planes. The results obtained from the Neural Network was compared with PSO inversion results and were found in

  4. Neural progenitors, patterning and ecology in neocortical origins

    Science.gov (United States)

    Aboitiz, Francisco; Zamorano, Francisco

    2013-01-01

    The anatomical organization of the mammalian neocortex stands out among vertebrates for its laminar and columnar arrangement, featuring vertically oriented, excitatory pyramidal neurons. The evolutionary origin of this structure is discussed here in relation to the brain organization of other amniotes, i.e., the sauropsids (reptiles and birds). Specifically, we address the developmental modifications that had to take place to generate the neocortex, and to what extent these modifications were shared by other amniote lineages or can be considered unique to mammals. In this article, we propose a hypothesis that combines the control of proliferation in neural progenitor pools with the specification of regional morphogenetic gradients, yielding different anatomical results by virtue of the differential modulation of these processes in each lineage. Thus, there is a highly conserved genetic and developmental battery that becomes modulated in different directions according to specific selective pressures. In the case of early mammals, ecological conditions like nocturnal habits and reproductive strategies are considered to have played a key role in the selection of the particular brain patterning mechanisms that led to the origin of the neocortex. PMID:24273496

  5. A Neural Network-Based Interval Pattern Matcher

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2015-07-01

    Full Text Available One of the most important roles in the machine learning area is to classify, and neural networks are very important classifiers. However, traditional neural networks cannot identify intervals, let alone classify them. To improve their identification ability, we propose a neural network-based interval matcher in our paper. After summarizing the theoretical construction of the model, we take a simple and a practical weather forecasting experiment, which show that the recognizer accuracy reaches 100% and that is promising.

  6. Twentieth-century fire patterns in the Selway-Bitterroot Wilderness Area, Idaho/Montana, and the Gila/Aldo Leopold Wilderness Complex, New Mexico

    Science.gov (United States)

    Matthew Rollins; Tom Swetnam; Penelope Morgan

    2000-01-01

    Twentieth century fire patterns were analyzed for two large, disparate wilderness areas in the Rocky Mountains. Spatial and temporal patterns of fires were represented as GIS-based digital fire atlases compiled from archival Forest Service data. We find that spatial and temporal fire patterns are related to landscape features and changes in land use. The rate and...

  7. Patterns of fire activity over Indonesia and Malaysia from polar and geostationary satellite observations

    Science.gov (United States)

    Hyer, Edward J.; Reid, Jeffrey S.; Prins, Elaine M.; Hoffman, Jay P.; Schmidt, Christopher C.; Miettinen, Jukka I.; Giglio, Louis

    2013-03-01

    Biomass burning patterns over the Maritime Continent of Southeast Asia are examined using a new active fire detection product based on application of the Wildfire Automated Biomass Burning Algorithm (WF_ABBA) to data from the imagers on the MTSAT geostationary satellites operated by the Japanese space agency JAXA. Data from MTSAT-1R and MTSAT-2 covering 34 months from September 2008 to July 2011 are examined for a study region consisting of Indonesia, Malaysia, and nearby environs. The spatial and temporal distributions of fires detected in the MTSAT WF_ABBA product are described and compared with active fire observations from MODIS MOD14 data. Land cover distributions for the two instruments are examined using a new 250 m land cover product from the National University of Singapore. The two products show broadly similar patterns of fire activity, land cover distribution of fires, and pixel fire radiative power (FRP). However, the MTSAT WF_ABBA data differ from MOD14 in important ways. Relative to MODIS, the MTSAT WF_ABBA product has lower overall detection efficiency, but more fires detected due to more frequent looks, a greater relative fraction of fires in forest and a lower relative fraction of fires in open areas, and significantly higher single-pixel retrieved FRP. The differences in land cover distribution and FRP between the MTSAT and MODIS products are shown to be qualitatively consistent with expectations based on pixel size and diurnal sampling. The MTSAT WF_ABBA data are used to calculate coverage-corrected diurnal cycles of fire for different regions within the study area. These diurnal cycles are preliminary but demonstrate that the fraction of diurnal fire activity sampled by the two MODIS sensors varies significantly by region and vegetation type. Based on the results from comparison of the two fire products, a series of steps is outlined to account for some of the systematic biases in each of these satellite products in order to produce a

  8. Tree regeneration spatial patterns in ponderosa pine forests following stand-replacing fire: Influence of topography and neighbors

    Science.gov (United States)

    Justin P. Ziegler; Chad M. Hoffman; Paula J. Fornwalt; Carolyn H. Sieg; Michael A. Battaglia; Marin E. Chambers; Jose M. Iniguez

    2017-01-01

    Shifting fire regimes alter forest structure assembly in ponderosa pine forests and may produce structural heterogeneity following stand-replacing fire due, in part, to fine-scale variability in growing environments. We mapped tree regeneration in eighteen plots 11 to 15 years after stand-replacing fire in Colorado and South Dakota, USA. We used point pattern analyses...

  9. A Feedback Model of Attention Explains the Diverse Effects of Attention on Neural Firing Rates and Receptive Field Structure: e1004770

    National Research Council Canada - National Science Library

    Thomas Miconi; Rufin VanRullen

    2016-01-01

      Visual attention has many effects on neural responses, producing complex changes in firing rates, as well as modifying the structure and size of receptive fields, both in topological and feature space...

  10. Pattern and process of prescribed fires influence effectiveness at reducing wildfire severity in dry coniferous forests

    Science.gov (United States)

    Arkle, Robert S.; Pilliod, David S.; Welty, Justin L.

    2012-01-01

    We examined the effects of three early season (spring) prescribed fires on burn severity patterns of summer wildfires that occurred 1–3 years post-treatment in a mixed conifer forest in central Idaho. Wildfire and prescribed fire burn severities were estimated as the difference in normalized burn ratio (dNBR) using Landsat imagery. We used GIS derived vegetation, topography, and treatment variables to generate models predicting the wildfire burn severity of 1286–5500 30-m pixels within and around treated areas. We found that wildfire severity was significantly lower in treated areas than in untreated areas and significantly lower than the potential wildfire severity of the treated areas had treatments not been implemented. At the pixel level, wildfire severity was best predicted by an interaction between prescribed fire severity, topographic moisture, heat load, and pre-fire vegetation volume. Prescribed fire severity and vegetation volume were the most influential predictors. Prescribed fire severity, and its influence on wildfire severity, was highest in relatively warm and dry locations, which were able to burn under spring conditions. In contrast, wildfire severity peaked in cooler, more mesic locations that dried later in the summer and supported greater vegetation volume. We found considerable evidence that prescribed fires have landscape-level influences within treatment boundaries; most notable was an interaction between distance from the prescribed fire perimeter and distance from treated patch edges, which explained up to 66% of the variation in wildfire severity. Early season prescribed fires may not directly target the locations most at risk of high severity wildfire, but proximity of these areas to treated patches and the discontinuity of fuels following treatment may influence wildfire severity and explain how even low severity treatments can be effective management tools in fire-prone landscapes.

  11. Detection of Variability of the Motor Unit Action Potential Shape by Means of the Firing Patterns

    DEFF Research Database (Denmark)

    Krarup, Christian; Nikolic, Mile; Dahl, Kristian

    1997-01-01

    The motor unit action potential is a summation of the potentials of the individual muscle fibers from the same motor unit.By using a newly developed automatic EMG decomposition system, variability of the firing patterns of the muscle fibers are analyzed.......The motor unit action potential is a summation of the potentials of the individual muscle fibers from the same motor unit.By using a newly developed automatic EMG decomposition system, variability of the firing patterns of the muscle fibers are analyzed....

  12. Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception

    Science.gov (United States)

    Kim, Junsuk; Yeon, Jiwon; Ryu, Jaekyun; Park, Jang-Yeon; Chung, Soon-Cheol; Kim, Sung-Phil

    2017-01-01

    Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra- and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness. PMID:28936171

  13. Land-use and fire drive temporal patterns of soil solution chemistry and nutrient fluxes.

    Science.gov (United States)

    Potthast, Karin; Meyer, Stefanie; Crecelius, Anna C; Schubert, Ulrich S; Tischer, Alexander; Michalzik, Beate

    2017-12-15

    Land-use type and ecosystem disturbances are important drivers for element cycling and bear the potential to modulate soil processes and hence ecosystem functions. To better understand the effect of such drivers on the magnitude and temporal patterns of organic matter (OM) and associated nutrient fluxes in soils, continuous flux monitoring is indispensable but insufficiently studied yet. We conducted a field study to elucidate the impact of land-use and surface fires on OM and nutrient fluxes with soil solution regarding seasonal and temporal patterns analyzing short (land-use types, but were subjected to strong seasonal patterns. Fire disturbance significantly lowered the annual soil solution pH in both land-uses and increased water fluxes, while DOC fluxes remained unaffected. A positive response of POC and S to fire was limited to short-term effects, while amplified particulate and dissolved nitrogen fluxes were observed in the longer run and co-ocurred with accelerated Ca and Mg fluxes. In summary, surface fires generated stronger effects on element fluxes than the land-use. Fire-induced increases in POM fluxes suggest that the particulate fraction represent a major pathway of OM translocation into the subsoil and beyond. With regard to ecosystem functions, pasture ecosystems were less prone to the risk of nutrient losses following fire events than the forest. In pastures, fire-induced base cation export may accelerate soil acidification, consequently exhausting soil buffer systems and thus may reduce the resilience to acidic depositions and disturbances. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Post-fire spatial patterns of soil nitrogen mineralization and microbial abundance.

    Directory of Open Access Journals (Sweden)

    Erica A H Smithwick

    Full Text Available Stand-replacing fires influence soil nitrogen availability and microbial community composition, which may in turn mediate post-fire successional dynamics and nutrient cycling. However, fires create patchiness at both local and landscape scales and do not result in consistent patterns of ecological dynamics. The objectives of this study were to (1 quantify the spatial structure of microbial communities in forest stands recently affected by stand-replacing fire and (2 determine whether microbial variables aid predictions of in situ net nitrogen mineralization rates in recently burned stands. The study was conducted in lodgepole pine (Pinus contorta var. latifolia and Engelmann spruce/subalpine fir (Picea engelmannii/Abies lasiocarpa forest stands that burned during summer 2000 in Greater Yellowstone (Wyoming, USA. Using a fully probabilistic spatial process model and Bayesian kriging, the spatial structure of microbial lipid abundance and fungi-to-bacteria ratios were found to be spatially structured within plots two years following fire (for most plots, autocorrelation range varied from 1.5 to 10.5 m. Congruence of spatial patterns among microbial variables, in situ net N mineralization, and cover variables was evident. Stepwise regression resulted in significant models of in situ net N mineralization and included variables describing fungal and bacterial abundance, although explained variance was low (R²<0.29. Unraveling complex spatial patterns of nutrient cycling and the biotic factors that regulate it remains challenging but is critical for explaining post-fire ecosystem function, especially in Greater Yellowstone, which is projected to experience increased fire frequencies by mid 21(st Century.

  15. Post-Fire Spatial Patterns of Soil Nitrogen Mineralization and Microbial Abundance

    Science.gov (United States)

    Smithwick, Erica A. H.; Naithani, Kusum J.; Balser, Teri C.; Romme, William H.; Turner, Monica G.

    2012-01-01

    Stand-replacing fires influence soil nitrogen availability and microbial community composition, which may in turn mediate post-fire successional dynamics and nutrient cycling. However, fires create patchiness at both local and landscape scales and do not result in consistent patterns of ecological dynamics. The objectives of this study were to (1) quantify the spatial structure of microbial communities in forest stands recently affected by stand-replacing fire and (2) determine whether microbial variables aid predictions of in situ net nitrogen mineralization rates in recently burned stands. The study was conducted in lodgepole pine (Pinus contorta var. latifolia) and Engelmann spruce/subalpine fir (Picea engelmannii/Abies lasiocarpa) forest stands that burned during summer 2000 in Greater Yellowstone (Wyoming, USA). Using a fully probabilistic spatial process model and Bayesian kriging, the spatial structure of microbial lipid abundance and fungi-to-bacteria ratios were found to be spatially structured within plots two years following fire (for most plots, autocorrelation range varied from 1.5 to 10.5 m). Congruence of spatial patterns among microbial variables, in situ net N mineralization, and cover variables was evident. Stepwise regression resulted in significant models of in situ net N mineralization and included variables describing fungal and bacterial abundance, although explained variance was low (R2<0.29). Unraveling complex spatial patterns of nutrient cycling and the biotic factors that regulate it remains challenging but is critical for explaining post-fire ecosystem function, especially in Greater Yellowstone, which is projected to experience increased fire frequencies by mid 21st Century. PMID:23226324

  16. Pattern recognition via synchronization in phase-locked loop neural networks.

    Science.gov (United States)

    Hoppensteadt, F C; Izhikevich, E M

    2000-01-01

    We propose a novel architecture of an oscillatory neural network that consists of phase-locked loop (PLL) circuits. It stores and retrieves complex oscillatory patterns as synchronized states with appropriate phase relations between neurons.

  17. Replay of rule-learning related neural patterns in the prefrontal cortex during sleep

    NARCIS (Netherlands)

    Peyrache, A.; Khamassi, M.; Benchenane, K.; Wiener, S.I.; Battaglia, F.P.

    2009-01-01

    Slow-wave sleep (SWS) is important for memory consolidation. During sleep, neural patterns reflecting previously acquired information are replayed. One possible reason for this is that such replay exchanges information between hippocampus and neocortex, supporting consolidation. We recorded neuron

  18. Pattern Recognition with Stochastic Resonance in a Generic Neural Network

    Science.gov (United States)

    Tan, Z.; Ali, M. K.

    We discuss stochastic resonance in associative memory with a canonical neural network model that describes the generic behavior of a large family of dynamical systems near bifurcation. Our result shows that stochastic resonance helps memory association. The relationship between stochastic resonance, associative memory, storage load, history of memory and initial states are studied. In intelligent systems like neural networks, it is likely that stochastic resonance combined with synaptic information enhances memory recalls.

  19. Resting network is composed of more than one neural pattern: an fMRI study.

    Science.gov (United States)

    Lee, T-W; Northoff, G; Wu, Y-T

    2014-08-22

    In resting state, the dynamics of blood oxygen level-dependent signals recorded by functional magnetic resonance imaging (fMRI) showed reliable modular structures. To explore the network property, previous research used to construct an adjacency matrix by Pearson's correlation and prune it using stringent statistical threshold. However, traditional analyses may lose useful information at middle to moderate high correlation level. This resting fMRI study adopted full connection as a criterion to partition the adjacency matrix into composite sub-matrices (neural patterns) and investigated the associated community organization and network features. Modular consistency across subjects was assessed using scaled inclusivity index. Our results disclosed two neural patterns with reliable modular structures. Concordant with the results of traditional intervention, community detection analysis showed that neural pattern 1, the sub-matrix at highest correlation level, was composed of sensory-motor, visual associative, default mode/midline, temporal limbic and basal ganglia structures. The neural pattern 2 was situated at middle to moderate high correlation level and comprised two larger modules, possibly associated with mental processing of outer world (such as visuo-associative, auditory and sensory-motor networks) and inner homeostasis (such as default-mode, midline and limbic systems). Graph theoretical analyses further demonstrated that the network feature of neural pattern 1 was more local and segregate, whereas that of neural pattern 2 was more global and integrative. Our results suggest that future resting fMRI research may take the neural pattern at middle to moderate high correlation range into consideration, which has long been ignored in extant literature. The variation of neural pattern 2 could be relevant to individual characteristics of self-regulatory functions, and the disruption in its topology may underlie the pathology of several neuropsychiatric illnesses

  20. Vasopressin regularizes the phasic firing pattern of rat hypothalamic magnocellular vasopressin neurons.

    Science.gov (United States)

    Gouzènes, L; Desarménien, M G; Hussy, N; Richard, P; Moos, F C

    1998-03-01

    Vasopressin (AVP) magnocellular neurons of hypothalamic nuclei express specific phasic firing (successive periods of activity and silence), which conditions the mode of neurohypophyseal vasopression release. In situations favoring plasmatic secretion of AVP, the hormone is also released at the somatodendritic level, at which it is believed to modulate the activity of AVP neurons. We investigated the nature of this autocontrol by testing the effects of juxtamembrane applications of AVP on the extracellular activity of presumed AVP neurons in paraventricular and supraoptic nuclei of anesthetized rats. AVP had three effects depending on the initial firing pattern: (1) excitation of faintly active neurons (periods of activity of <10 sec), which acquired or reinforced their phasic pattern; (2) inhibition of quasi-continuously active neurons (periods of silences of <10 sec), which became clearly phasic; and (3) no effect on neurons already showing an intermediate phasic pattern (active and silent periods of 10-30 sec). Consequently, AVP application resulted in a narrower range of activity patterns of the population of AVP neurons, with a Gaussian distribution centered around a mode of 57% of time in activity, indicating a homogenization of the firing pattern. The resulting phasic pattern had characteristics close to those established previously for optimal release of AVP from neurohypophyseal endings. These results suggest a new role for AVP as an optimizing factor that would foster the population of AVP neurons to discharge with a phasic pattern known to be most efficient for hormone release.

  1. Effects of ion channel noise on neural circuits: an application to the respiratory pattern generator to investigate breathing variability.

    Science.gov (United States)

    Yu, Haitao; Dhingra, Rishi R; Dick, Thomas E; Galán, Roberto F

    2017-01-01

    Neural activity generally displays irregular firing patterns even in circuits with apparently regular outputs, such as motor pattern generators, in which the output frequency fluctuates randomly around a mean value. This "circuit noise" is inherited from the random firing of single neurons, which emerges from stochastic ion channel gating (channel noise), spontaneous neurotransmitter release, and its diffusion and binding to synaptic receptors. Here we demonstrate how to expand conductance-based network models that are originally deterministic to include realistic, physiological noise, focusing on stochastic ion channel gating. We illustrate this procedure with a well-established conductance-based model of the respiratory pattern generator, which allows us to investigate how channel noise affects neural dynamics at the circuit level and, in particular, to understand the relationship between the respiratory pattern and its breath-to-breath variability. We show that as the channel number increases, the duration of inspiration and expiration varies, and so does the coefficient of variation of the breath-to-breath interval, which attains a minimum when the mean duration of expiration slightly exceeds that of inspiration. For small channel numbers, the variability of the expiratory phase dominates over that of the inspiratory phase, and vice versa for large channel numbers. Among the four different cell types in the respiratory pattern generator, pacemaker cells exhibit the highest sensitivity to channel noise. The model shows that suppressing input from the pons leads to longer inspiratory phases, a reduction in breathing frequency, and larger breath-to-breath variability, whereas enhanced input from the raphe nucleus increases breathing frequency without changing its pattern. A major source of noise in neuronal circuits is the "flickering" of ion currents passing through the neurons' membranes (channel noise), which cannot be suppressed experimentally. Computational

  2. Shoulder muscle firing patterns during the windmill softball pitch.

    Science.gov (United States)

    Maffet, M W; Jobe, F W; Pink, M M; Brault, J; Mathiyakom, W

    1997-01-01

    The purpose of this study was to describe the activity of eight shoulder muscles during the windmill fast-pitch softball throw. Ten collegiate female pitchers were analyzed with intramuscular electromyography, high-speed cinematography, and motion analysis. The supraspinatus muscle fired maximally during arm elevation from the 6 to 3 o'clock position phase, centralizing the humeral head within the glenoid. The posterior deltoid and teres minor muscles acted maximally from the 3 to 12 o'clock position phase to continue arm elevation and externally rotate the humerus. The pectoralis major muscle accelerated the arm from the 12 o'clock position to ball release phase. The serratus anterior muscle characteristically acted to position the scapula for optimal glenohumeral congruency, and the subscapularis muscle functioned as an internal rotator and to protect the anterior capsule. Although the windmill softball pitch is overtly different from the baseball pitch, several surprising similarities were revealed. The serratus anterior and pectoralis major muscles work in synchrony and seem to have similar functions in both pitches. Although the infraspinatus and teres minor muscles are both posterior cuff muscles, they are characteristically uncoupled during the 6 to 3 o'clock position phase, with the infraspinatus muscle acting more independently below 90 degrees. Subscapularis muscle activity seems important in dynamic anterior glenohumeral stabilization and as an internal rotator in both the baseball and softball throws.

  3. Numerical approaches to model perturbation fire in turing pattern formations

    Science.gov (United States)

    Campagna, R.; Brancaccio, M.; Cuomo, S.; Mazzoleni, S.; Russo, L.; Siettos, K.; Giannino, F.

    2017-11-01

    Turing patterns were observed in chemical, physical and biological systems described by coupled reaction-diffusion equations. Several models have been formulated proposing the water as the causal mechanism of vegetation pattern formation, but this isn't an exhaustive hypothesis in some natural environments. An alternative explanation has been related to the plant-soil negative feedback. In Marasco et al. [1] the authors explored the hypothesis that both mechanisms contribute in the formation of regular and irregular vegetation patterns. The mathematical model consists in three partial differential equations (PDEs) that take into account for a dynamic balance between biomass, water and toxic compounds. A numerical approach is mandatory also to investigate on the predictions of this kind of models. In this paper we start from the mathematical model described in [1], set the model parameters such that the biomass reaches a stable spatial pattern (spots) and present preliminary studies about the occurrence of perturbing events, such as wildfire, that can affect the regularity of the biomass configuration.

  4. Changing patterns of fire occurrence in proximity to forest edges, roads and rivers between NW Amazonian countries

    Science.gov (United States)

    Armenteras, Dolors; Barreto, Joan Sebastian; Tabor, Karyn; Molowny-Horas, Roberto; Retana, Javier

    2017-06-01

    Tropical forests in NW Amazonia are highly threatened by the expansion of the agricultural frontier and subsequent deforestation. Fire is used, both directly and indirectly, in Brazilian Amazonia to propagate deforestation and increase forest accessibility. Forest fragmentation, a measure of forest degradation, is also attributed to fire occurrence in the tropics. However, outside the Brazilian Legal Amazonia the role of fire in increasing accessibility and forest fragmentation is less explored. In this study, we compared fire regimes in five countries that share this tropical biome in the most north-westerly part of the Amazon Basin (Venezuela, Colombia, Ecuador, Peru and Brazil). We analysed spatial differences in the timing of peak fire activity and in relation to proximity to roads and rivers using 12 years of MODIS active fire detections. We also distinguished patterns of fire in relation to forest fragmentation by analysing fire distance to the forest edge as a measure of fragmentation for each country. We found significant hemispheric differences in peak fire occurrence with the highest number of fires in the south in 2005 vs. 2007 in the north. Despite this, both hemispheres are equally affected by fire. We also found difference in peak fire occurrence by country. Fire peaked in February in Colombia and Venezuela, whereas it peaked in September in Brazil and Peru, and finally Ecuador presented two fire peaks in January and October. We confirmed the relationship between fires and forest fragmentation for all countries and also found significant differences in the distance between the fire and the forest edge for each country. Fires were associated with roads and rivers in most countries. These results can inform land use planning at the regional, national and subnational scales to minimize the contribution of road expansion and subsequent access to the Amazonian natural resources to fire occurrence and the associated deforestation and carbon emissions.

  5. Proliferation and recapitulation of developmental patterning associated with regulative regeneration of the spinal cord neural tube.

    Science.gov (United States)

    Halasi, Gabor; Søviknes, Anne Mette; Sigurjonsson, Olafur; Glover, Joel C

    2012-05-01

    Developmental patterning during regulative regeneration of the chicken embryo spinal neural tube was characterized by assessing proliferation and the expression of transcription factors specific to neural progenitor and postmitotic neuron populations. One to several segments of the thoracolumbar neural tube were selectively excised unilaterally to initiate regeneration. The capacity for regeneration depended on the stage when ablation was performed and the extent of tissue removed. 20% of surviving embryos exhibited complete regulative regeneration, wherein the missing hemi-neural tube was reconstituted to normal size and morphology. Fate-mapping of proliferative adjacent tissue indicated contributions from the opposite side of the neural tube and potentially from the ipsilateral neural tube rostral and caudal to the lesion. Application of the thymidine analog EdU (5-ethynyl-2'-deoxyuridine) demonstrated a moderate increase in cell proliferation in lesioned relative to control embryos, and quantitative PCR demonstrated a parallel moderate increase in transcription of proliferation-related genes. Mathematical calculation showed that such modest increases are sufficient to account for the amount of regenerated tissue. Within the regenerated neural tube the expression pattern of progenitor-specific transcription factors was recapitulated in the separate advancing ventral and dorsal fronts of regeneration, with no evidence of abnormal mixing of progenitor subpopulations, indicating that graded patterning mechanisms do not require continuity of neural tube tissue along the dorsoventral axis and do not involve a sorting out of committed progenitors. Upon completion of the regeneration process, the pattern of neuron-specific transcription factor expression was essentially normal. Modest deficits in the numbers of transcription factor-defined neuron types were evident in the regenerated tissue, increasing particularly in dorsal neuron types with later lesions. These

  6. Mid-term successional patterns after fire of mixed pine oak forests in NE Spain

    Science.gov (United States)

    Gracia, Marc; Retana, Javier; Roig, Pere

    2002-12-01

    This study analyzes the factors affecting the current variability in density and age and size structure of mixed pine-oak forests of Pinus nigra and Quercus faginea in Central Catalonia (NE Spain), 37 years after a wildfire. The objective is to determine whether different post-disturbance responses may be obtained from the same pre-fire community and which factors can determine these different potential responses. The two factors analyzed were the distance to the unburned forest and site conditions (represented in this case by different aspects). The response of pines and oaks was different to the pattern expected for the Mediterranean Basin. Oaks resprouted immediately from stools already present before the fire and dominated during the first years, independent of both disturbance and site conditions. Pines established later, and their response depended on both factors: pine density decreased sharply from the forest edge to the burned area, and the number of pines was also higher in the more mesic than in the more xeric conditions. The age structure analysis for pines and oaks in the different aspects also revealed site-dependent rates of succession manifested by initial differences in post-fire establishment. In mesic plots, the establishment of pines occurred quite early, while in xeric plots, pine recruitment was delayed several years. These different patterns of post-fire recovery have led to pine dominance in more mesic sites and codominance of pines and oaks in more xeric ones, suggesting that different mid-term post-fire patterns can be identified for the same pre-fire forest type, depending on variations in environmental conditions.

  7. The neural origins of shell structure and pattern in aquatic mollusks.

    Science.gov (United States)

    Boettiger, Alistair; Ermentrout, Bard; Oster, George

    2009-04-21

    We present a model to explain how the neurosecretory system of aquatic mollusks generates their diversity of shell structures and pigmentation patterns. The anatomical and physiological basis of this model sets it apart from other models used to explain shape and pattern. The model reproduces most known shell shapes and patterns and accurately predicts how the pattern alters in response to environmental disruption and subsequent repair. Finally, we connect the model to a larger class of neural models.

  8. Comparing effects of fire modeling methods on simulated fire patterns and succession: a case study in the Missouri Ozarks

    Science.gov (United States)

    Jian Yang; Hong S. He; Brian R. Sturtevant; Brian R. Miranda; Eric J. Gustafson

    2008-01-01

    We compared four fire spread simulation methods (completely random, dynamic percolation. size-based minimum travel time algorithm. and duration-based minimum travel time algorithm) and two fire occurrence simulation methods (Poisson fire frequency model and hierarchical fire frequency model) using a two-way factorial design. We examined these treatment effects on...

  9. Distributed dynamical computation in neural circuits with propagating coherent activity patterns.

    Directory of Open Access Journals (Sweden)

    Pulin Gong

    2009-12-01

    Full Text Available Activity in neural circuits is spatiotemporally organized. Its spatial organization consists of multiple, localized coherent patterns, or patchy clusters. These patterns propagate across the circuits over time. This type of collective behavior has ubiquitously been observed, both in spontaneous activity and evoked responses; its function, however, has remained unclear. We construct a spatially extended, spiking neural circuit that generates emergent spatiotemporal activity patterns, thereby capturing some of the complexities of the patterns observed empirically. We elucidate what kind of fundamental function these patterns can serve by showing how they process information. As self-sustained objects, localized coherent patterns can signal information by propagating across the neural circuit. Computational operations occur when these emergent patterns interact, or collide with each other. The ongoing behaviors of these patterns naturally embody both distributed, parallel computation and cascaded logical operations. Such distributed computations enable the system to work in an inherently flexible and efficient way. Our work leads us to propose that propagating coherent activity patterns are the underlying primitives with which neural circuits carry out distributed dynamical computation.

  10. Wnt/Yes-Associated Protein Interactions During Neural Tissue Patterning of Human Induced Pluripotent Stem Cells.

    Science.gov (United States)

    Bejoy, Julie; Song, Liqing; Zhou, Yi; Li, Yan

    2017-08-31

    Human induced pluripotent stem cells (hiPSCs) have special ability to self-assemble into neural spheroids or mini-brain-like structures. During the self-assembly process, Wnt signaling plays an important role in regional patterning and establishing positional identity of hiPSC-derived neural progenitors. Recently, the role of Wnt signaling in regulating Yes-associated protein (YAP) expression (nuclear or cytoplasmic), the pivotal regulator during organ growth and tissue generation, has attracted increasing interests. However, the interactions between Wnt and YAP expression for neural lineage commitment of hiPSCs remain poorly explored. The objective of this study is to investigate the effects of Wnt signaling and YAP expression on the cellular population in three-dimensional (3D) neural spheroids derived from hiPSCs. In this study, Wnt signaling was activated using CHIR99021 for 3D neural spheroids derived from human iPSK3 cells through embryoid body formation. Our results indicate that Wnt activation induces nuclear localization of YAP and upregulates the expression of HOXB4, the marker for hindbrain/spinal cord. By contrast, the cells exhibit more rostral forebrain neural identity (expression of TBR1) without Wnt activation. Cytochalasin D was then used to induce cytoplasmic YAP and the results showed the decreased HOXB4 expression. In addition, the incorporation of microparticles in the neural spheroids was investigated for the perturbation of neural patterning. This study may indicate the bidirectional interactions of Wnt signaling and YAP expression during neural tissue patterning, which have the significance in neurological disease modeling, drug screening, and neural tissue regeneration.

  11. Expression patterns of neural genes in Euperipatoides kanangrensis suggest divergent evolution of onychophoran and euarthropod neurogenesis.

    Science.gov (United States)

    Eriksson, Bo Joakim; Stollewerk, Angelika

    2010-12-28

    One of the controversial debates on euarthropod relationships centers on the question as to whether insects, crustaceans, and myriapods (Mandibulata) share a common ancestor or whether myriapods group with the chelicerates (Myriochelata). The debate was stimulated recently by studies in chelicerates and myriapods that show that neural precursor groups (NPGs) segregate from the neuroectoderm generating the nervous system, whereas in insects and crustaceans the nervous tissue is produced by stem cells. Do the shared neural characters of myriapods and chelicerates represent derived characters that support the Myriochelata grouping? Or do they rather reflect the ancestral pattern? Analyses of neurogenesis in a group closely related to euarthropods, the onychophorans, show that, similar to insects and crustaceans, single neural precursors are formed in the neuroectoderm, potentially supporting the Myriochelata hypothesis. Here we show that the nature and the selection of onychophoran neural precursors are distinct from euarthropods. The onychophoran nervous system is generated by the massive irregular segregation of single neural precursors, contrasting with the limited number and stereotyped arrangement of NPGs/stem cells in euarthropods. Furthermore, neural genes do not show the spatiotemporal pattern that sets up the precise position of neural precursors as in euarthropods. We conclude that neurogenesis in onychophorans largely does not reflect the ancestral pattern of euarthropod neurogenesis, but shows a mixture of derived characters and ancestral characters that have been modified in the euarthropod lineage. Based on these data and additional evidence, we suggest an evolutionary sequence of arthropod neurogenesis that is in line with the Mandibulata hypothesis.

  12. Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks

    Science.gov (United States)

    Aguiar, Manuela A. D.; Dias, Ana Paula S.; Ferreira, Flora

    2017-01-01

    We consider feed-forward and auto-regulation feed-forward neural (weighted) coupled cell networks. In feed-forward neural networks, cells are arranged in layers such that the cells of the first layer have empty input set and cells of each other layer receive only inputs from cells of the previous layer. An auto-regulation feed-forward neural coupled cell network is a feed-forward neural network where additionally some cells of the first layer have auto-regulation, that is, they have a self-loop. Given a network structure, a robust pattern of synchrony is a space defined in terms of equalities of cell coordinates that is flow-invariant for any coupled cell system (with additive input structure) associated with the network. In this paper, we describe the robust patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks. Regarding feed-forward neural networks, we show that only cells in the same layer can synchronize. On the other hand, in the presence of auto-regulation, we prove that cells in different layers can synchronize in a robust way and we give a characterization of the possible patterns of synchrony that can occur for auto-regulation feed-forward neural networks.

  13. Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks.

    Science.gov (United States)

    Aguiar, Manuela A D; Dias, Ana Paula S; Ferreira, Flora

    2017-01-01

    We consider feed-forward and auto-regulation feed-forward neural (weighted) coupled cell networks. In feed-forward neural networks, cells are arranged in layers such that the cells of the first layer have empty input set and cells of each other layer receive only inputs from cells of the previous layer. An auto-regulation feed-forward neural coupled cell network is a feed-forward neural network where additionally some cells of the first layer have auto-regulation, that is, they have a self-loop. Given a network structure, a robust pattern of synchrony is a space defined in terms of equalities of cell coordinates that is flow-invariant for any coupled cell system (with additive input structure) associated with the network. In this paper, we describe the robust patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks. Regarding feed-forward neural networks, we show that only cells in the same layer can synchronize. On the other hand, in the presence of auto-regulation, we prove that cells in different layers can synchronize in a robust way and we give a characterization of the possible patterns of synchrony that can occur for auto-regulation feed-forward neural networks.

  14. Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review

    Directory of Open Access Journals (Sweden)

    Thuan Chu

    2013-12-01

    Full Text Available The frequency and severity of forest fires, coupled with changes in spatial and temporal precipitation and temperature patterns, are likely to severely affect the characteristics of forest and permafrost patterns in boreal eco-regions. Forest fires, however, are also an ecological factor in how forest ecosystems form and function, as they affect the rate and characteristics of tree recruitment. A better understanding of fire regimes and forest recovery patterns in different environmental and climatic conditions will improve the management of sustainable forests by facilitating the process of forest resilience. Remote sensing has been identified as an effective tool for preventing and monitoring forest fires, as well as being a potential tool for understanding how forest ecosystems respond to them. However, a number of challenges remain before remote sensing practitioners will be able to better understand the effects of forest fires and how vegetation responds afterward. This article attempts to provide a comprehensive review of current research with respect to remotely sensed data and methods used to model post-fire effects and forest recovery patterns in boreal forest regions. The review reveals that remote sensing-based monitoring of post-fire effects and forest recovery patterns in boreal forest regions is not only limited by the gaps in both field data and remotely sensed data, but also the complexity of far-northern fire regimes, climatic conditions and environmental conditions. We expect that the integration of different remotely sensed data coupled with field campaigns can provide an important data source to support the monitoring of post-fire effects and forest recovery patterns. Additionally, the variation and stratification of pre- and post-fire vegetation and environmental conditions should be considered to achieve a reasonable, operational model for monitoring post-fire effects and forest patterns in boreal regions.

  15. Patterning and predicting aquatic macroinvertebrate diversities using artificial neural network

    NARCIS (Netherlands)

    Park, Y.S.; Verdonschot, P.F.M.; Chon, T.S.; Lek, S.

    2003-01-01

    A counterpropagation neural network (CPN) was applied to predict species richness (SR) and Shannon diversity index (SH) of benthic macroinvertebrate communities using 34 environmental variables. The data were collected at 664 sites at 23 different water types such as springs, streams, rivers,

  16. Hypothetical Pattern Recognition Design Using Multi-Layer Perceptorn Neural Network For Supervised Learning

    Directory of Open Access Journals (Sweden)

    Md. Abdullah-al-mamun

    2015-08-01

    Full Text Available Abstract Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain called Artificial Neural Network that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research now a days pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural networkin the algorithm of artificial Intelligence as the best possible way of utilizing available resources to make a decision that can be a human like performance.

  17. Using tree recruitment patterns and fire history to guide restoration of an unlogged ponderosa pine/Douglas-fir landscape in the southern Rocky Mountains after a century of fire suppression

    Science.gov (United States)

    Merrill R. Kaufmann; Laurie S. Huckaby; Paula J. Fornwalt; Jason M. Stoker; William H. Romme

    2003-01-01

    Tree age and fire history were studied in an unlogged ponderosa pine/Douglas-fir (Pinus ponderosa/Pseudotsuga menziesii) landscape in the Colorado Front Range mountains. These data were analysed to understand tree survival during fire and post-fire recruitment patterns after fire, as a basis for understanding the characteristics of, and restoration needs for, an...

  18. Neural Pattern Similarity in the Left IFG and Fusiform Is Associated with Novel Word Learning

    Directory of Open Access Journals (Sweden)

    Jing Qu

    2017-08-01

    Full Text Available Previous studies have revealed that greater neural pattern similarity across repetitions is associated with better subsequent memory. In this study, we used an artificial language training paradigm and representational similarity analysis to examine whether neural pattern similarity across repetitions before training was associated with post-training behavioral performance. Twenty-four native Chinese speakers were trained to learn a logographic artificial language for 12 days and behavioral performance was recorded using the word naming and picture naming tasks. Participants were scanned while performing a passive viewing task before training, after 4-day training and after 12-day training. Results showed that pattern similarity in the left pars opercularis (PO and fusiform gyrus (FG before training was negatively associated with reaction time (RT in both word naming and picture naming tasks after training. These results suggest that neural pattern similarity is an effective neurofunctional predictor of novel word learning in addition to word memory.

  19. Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

    CERN Document Server

    Melin, Patricia

    2012-01-01

    This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

  20. The use of satellite data for monitoring temporal and spatial patterns of fire: a comprehensive review

    Science.gov (United States)

    Lasaponara, R.

    2009-04-01

    fire regimes from Earth observation data Global Change Biology vo. 14. doi: 10.1111/j.1365-2486.2008.01585.x 1-15, Chuvieco E., P. Englefield, Alexander P. Trishchenko, Yi Luo Generation of long time series of burn area maps of the boreal forest from NOAA-AVHRR composite data. Remote Sensing of Environment, Volume 112, Issue 5, 15 May 2008, Pages 2381-2396 Chuvieco Emilio 2006, Remote Sensing of Forest Fires: Current limitations and future prospects in Observing Land from Space: Science, Customers and Technology, Advances in Global Change Research Vol. 4 pp 47-51 De Santis A., E. Chuvieco Burn severity estimation from remotely sensed data: Performance of simulation versus empirical models, Remote Sensing of Environment, Volume 108, Issue 4, 29 June 2007, Pages 422-435. De Santis A., E. Chuvieco, Patrick J. Vaughan, Short-term assessment of burn severity using the inversion of PROSPECT and GeoSail models, Remote Sensing of Environment, Volume 113, Issue 1, 15 January 2009, Pages 126-136 García M., E. Chuvieco, H. Nieto, I. Aguado Combining AVHRR and meteorological data for estimating live fuel moisture content Remote Sensing of Environment, Volume 112, Issue 9, 15 September 2008, Pages 3618-3627 Ichoku C., L. Giglio, M. J. Wooster, L. A. Remer Global characterization of biomass-burning patterns using satellite measurements of fire radiative energy. Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2950-2962. Lasaponara R. and Lanorte, On the capability of satellite VHR QuickBird data for fuel type characterization in fragmented landscape Ecological Modelling Volume 204, Issues 1-2, 24 May 2007, Pages 79-84 Lasaponara R., A. Lanorte, S. Pignatti,2006 Multiscale fuel type mapping in fragmented ecosystems: preliminary results from Hyperspectral MIVIS and Multispectral Landsat TM data, Int. J. Remote Sens., vol. 27 (3) pp. 587-593. Lasaponara R., V. Cuomo, M. F. Macchiato, and T. Simoniello, 2003 .A self-adaptive algorithm based on AVHRR multitemporal

  1. Early ictal and interictal patterns in FIRES: The sparks before the blaze.

    Science.gov (United States)

    Farias-Moeller, Raquel; Bartolini, Luca; Staso, Katelyn; Schreiber, John M; Carpenter, Jessica L

    2017-08-01

    Febrile infection-related epilepsy syndrome (FIRES) is a catastrophic epileptic encephalopathy described as explosive onset of super refractory status epilepticus (SRSE) in previously healthy children. We describe electroencephalography (EEG) abnormalities in the hyperacute phase of FIRES, with the aim of contributing to the diagnostic characterization of a syndrome otherwise lacking specific biomarkers. This is a retrospective single-center, case series of seven children with FIRES. Cases were identified from a Neurocritical Care database. Patient characteristics and clinical course were obtained from electronic medical records. Electroencephalography recordings were reviewed in two segments: the initial 12 h of recording and the 12 h prior to initiation of a medically induced burst suppression (BS). Fourteen 12-h segments of video-electroencephalography (EEG) recordings were analyzed for commonalities. A beta-delta complex resembling extreme delta brush (EDB) occurred in at least one 12-h segment for all patients. In six patients, seizures were brief and relatively infrequent during the first recording, with a gradual evolution to status epilepticus by the second. We observed a characteristic electrographic seizure pattern in six of seven patients with prolonged focal fast activity at onset. Shifting seizures were seen in four of seven patients. The diagnosis of FIRES is typically assigned late in a patient's clinical course, which has broad implications for clinical care and research. We retrospectively analyzed acute EEG features in seven patients with FIRES and discovered three common features: gradual increase in seizure burden, presence of a recurrent EDB, and a typical seizure pattern. Recognition of this pattern may facilitate early diagnosis and treatment. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

  2. Characteristic effects of stochastic oscillatory forcing on neural firing: analytical theory and comparison to paddlefish electroreceptor data.

    Directory of Open Access Journals (Sweden)

    Christoph Bauermeister

    Full Text Available Stochastic signals with pronounced oscillatory components are frequently encountered in neural systems. Input currents to a neuron in the form of stochastic oscillations could be of exogenous origin, e.g. sensory input or synaptic input from a network rhythm. They shape spike firing statistics in a characteristic way, which we explore theoretically in this report. We consider a perfect integrate-and-fire neuron that is stimulated by a constant base current (to drive regular spontaneous firing, along with Gaussian narrow-band noise (a simple example of stochastic oscillations, and a broadband noise. We derive expressions for the nth-order interval distribution, its variance, and the serial correlation coefficients of the interspike intervals (ISIs and confirm these analytical results by computer simulations. The theory is then applied to experimental data from electroreceptors of paddlefish, which have two distinct types of internal noisy oscillators, one forcing the other. The theory provides an analytical description of their afferent spiking statistics during spontaneous firing, and replicates a pronounced dependence of ISI serial correlation coefficients on the relative frequency of the driving oscillations, and furthermore allows extraction of certain parameters of the intrinsic oscillators embedded in these electroreceptors.

  3. Rootstock-regulated gene expression patterns associated with fire blight resistance in apple

    Directory of Open Access Journals (Sweden)

    Jensen Philip J

    2012-01-01

    Full Text Available Abstract Background Desirable apple varieties are clonally propagated by grafting vegetative scions onto rootstocks. Rootstocks influence many phenotypic traits of the scion, including resistance to pathogens such as Erwinia amylovora, which causes fire blight, the most serious bacterial disease of apple. The purpose of the present study was to quantify rootstock-mediated differences in scion fire blight susceptibility and to identify transcripts in the scion whose expression levels correlated with this response. Results Rootstock influence on scion fire blight resistance was quantified by inoculating three-year old, orchard-grown apple trees, consisting of 'Gala' scions grafted to a range of rootstocks, with E. amylovora. Disease severity was measured by the extent of shoot necrosis over time. 'Gala' scions grafted to G.30 or MM.111 rootstocks showed the lowest rates of necrosis, while 'Gala' on M.27 and B.9 showed the highest rates of necrosis. 'Gala' scions on M.7, S.4 or M.9F56 had intermediate necrosis rates. Using an apple DNA microarray representing 55,230 unique transcripts, gene expression patterns were compared in healthy, un-inoculated, greenhouse-grown 'Gala' scions on the same series of rootstocks. We identified 690 transcripts whose steady-state expression levels correlated with the degree of fire blight susceptibility of the scion/rootstock combinations. Transcripts known to be differentially expressed during E. amylovora infection were disproportionately represented among these transcripts. A second-generation apple microarray representing 26,000 transcripts was developed and was used to test these correlations in an orchard-grown population of trees segregating for fire blight resistance. Of the 690 transcripts originally identified using the first-generation array, 39 had expression levels that correlated with fire blight resistance in the breeding population. Conclusions Rootstocks had significant effects on the fire blight

  4. Rootstock-regulated gene expression patterns associated with fire blight resistance in apple.

    Science.gov (United States)

    Jensen, Philip J; Halbrendt, Noemi; Fazio, Gennaro; Makalowska, Izabela; Altman, Naomi; Praul, Craig; Maximova, Siela N; Ngugi, Henry K; Crassweller, Robert M; Travis, James W; McNellis, Timothy W

    2012-01-09

    Desirable apple varieties are clonally propagated by grafting vegetative scions onto rootstocks. Rootstocks influence many phenotypic traits of the scion, including resistance to pathogens such as Erwinia amylovora, which causes fire blight, the most serious bacterial disease of apple. The purpose of the present study was to quantify rootstock-mediated differences in scion fire blight susceptibility and to identify transcripts in the scion whose expression levels correlated with this response. Rootstock influence on scion fire blight resistance was quantified by inoculating three-year old, orchard-grown apple trees, consisting of 'Gala' scions grafted to a range of rootstocks, with E. amylovora. Disease severity was measured by the extent of shoot necrosis over time. 'Gala' scions grafted to G.30 or MM.111 rootstocks showed the lowest rates of necrosis, while 'Gala' on M.27 and B.9 showed the highest rates of necrosis. 'Gala' scions on M.7, S.4 or M.9F56 had intermediate necrosis rates. Using an apple DNA microarray representing 55,230 unique transcripts, gene expression patterns were compared in healthy, un-inoculated, greenhouse-grown 'Gala' scions on the same series of rootstocks. We identified 690 transcripts whose steady-state expression levels correlated with the degree of fire blight susceptibility of the scion/rootstock combinations. Transcripts known to be differentially expressed during E. amylovora infection were disproportionately represented among these transcripts. A second-generation apple microarray representing 26,000 transcripts was developed and was used to test these correlations in an orchard-grown population of trees segregating for fire blight resistance. Of the 690 transcripts originally identified using the first-generation array, 39 had expression levels that correlated with fire blight resistance in the breeding population. Rootstocks had significant effects on the fire blight susceptibility of 'Gala' scions, and rootstock-regulated gene

  5. Mapping regional patterns of large forest fires in Wildland-Urban Interface areas in Europe.

    Science.gov (United States)

    Modugno, Sirio; Balzter, Heiko; Cole, Beth; Borrelli, Pasquale

    2016-05-01

    Over recent decades, Land Use and Cover Change (LUCC) trends in many regions of Europe have reconfigured the landscape structures around many urban areas. In these areas, the proximity to landscape elements with high forest fuels has increased the fire risk to people and property. These Wildland-Urban Interface areas (WUI) can be defined as landscapes where anthropogenic urban land use and forest fuel mass come into contact. Mapping their extent is needed to prioritize fire risk control and inform local forest fire risk management strategies. This study proposes a method to map the extent and spatial patterns of the European WUI areas at continental scale. Using the European map of WUI areas, the hypothesis is tested that the distance from the nearest WUI area is related to the forest fire probability. Statistical relationships between the distance from the nearest WUI area, and large forest fire incidents from satellite remote sensing were subsequently modelled by logistic regression analysis. The first European scale map of the WUI extent and locations is presented. Country-specific positive and negative relationships of large fires and the proximity to the nearest WUI area are found. A regional-scale analysis shows a strong influence of the WUI zones on large fires in parts of the Mediterranean regions. Results indicate that the probability of large burned surfaces increases with diminishing WUI distance in touristic regions like Sardinia, Provence-Alpes-Côte d'Azur, or in regions with a strong peri-urban component as Catalunya, Comunidad de Madrid, Comunidad Valenciana. For the above regions, probability curves of large burned surfaces show statistical relationships (ROC value > 0.5) inside a 5000 m buffer of the nearest WUI. Wise land management can provide a valuable ecosystem service of fire risk reduction that is currently not explicitly included in ecosystem service valuations. The results re-emphasise the importance of including this ecosystem service

  6. Responses from two firing patterns in inferior colliculus neurons to stimulation of the lateral lemniscus dorsal nucleus

    Directory of Open Access Journals (Sweden)

    Xiao-ting Li

    2016-01-01

    Full Text Available The γ-aminobutyric acid neurons (GABAergic neurons in the inferior colliculus are classified into various patterns based on their intrinsic electrical properties to a constant current injection. Although this classification is associated with physiological function, the exact role for neurons with various firing patterns in acoustic processing remains poorly understood. In the present study, we analyzed characteristics of inferior colliculus neurons in vitro, and recorded responses to stimulation of the dorsal nucleus of the lateral lemniscus using the whole-cell patch clamp technique. Seven inferior colliculus neurons were tested and were classified into two firing patterns: sustained-regular (n = 4 and sustained-adapting firing patterns (n = 3. The majority of inferior colliculus neurons exhibited slight changes in response to stimulation and bicuculline. The responses of one neuron with a sustained-adapting firing pattern were suppressed after stimulation, but recovered to normal levels following application of the γ-aminobutyric acid receptor antagonist. One neuron with a sustained-regular pattern showed suppressed stimulation responses, which were not affected by bicuculline. Results suggest that GABAergic neurons in the inferior colliculus exhibit sustained-regular or sustained-adapting firing patterns. Additionally, GABAergic projections from the dorsal nucleus of the lateral lemniscus to the inferior colliculus are associated with sound localization. The different neuronal responses of various firing patterns suggest a role in sound localization. A better understanding of these mechanisms and functions will provide better clinical treatment paradigms for hearing deficiencies.

  7. The relationship between landscape patterns and human-caused fire occurrence in Spain

    Energy Technology Data Exchange (ETDEWEB)

    Castafreda-Aumedes, S.; Garcia-Martin, A.; Vega-Garcia, C.

    2013-05-01

    Aim of study: Human settlements and activities have completely modified landscape structure in the Mediterranean region. Vegetation patterns show the interactions between human activities and natural processes on the territory, and allow understanding historical ecological processes and socioeconomic factors. The arrangement of land uses in the rural landscape can be perceived as a proxy for human activities that often lead to the use, and escape, of fire, the most important disturbance in our forest landscapes. In this context, we tried to predict human-caused fire occurrence in a 5-year period by quantifying landscape patterns. Area of study: This study analyses the Spanish territory included in the Iberian Peninsula and Balearic Islands (497,166 km{sup 2}). Material and Methods: We evaluated spatial pattern applying a set of commonly used landscape ecology metrics to landscape windows of 10x10 sq km (4751 units in the UTM grid) overlaid on the Forest Map of Spain, MFE200. Main results: The best logistic regression model obtained included Shannon's Diversity Index, Mean Patch Edge and Mean Shape Index as explicative variables and the global percentage of correct predictions was 66.3 %. Research highlights: Our results suggested that the highest probability of fire occurrence at that time was associated with areas with a greater diversity of land uses and with more compact patches with fewer edges. (Author) 58 refs.

  8. The Chronotron: A Neuron That Learns to Fire Temporally Precise Spike Patterns

    Science.gov (United States)

    Florian, Răzvan V.

    2012-01-01

    In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely temporally coded, both as input and as output. We introduce two new supervised learning rules for spiking neurons with temporal coding of information (chronotrons), one that provides high memory capacity (E-learning), and one that has a higher biological plausibility (I-learning). With I-learning, the neuron learns to fire the target spike trains through synaptic changes that are proportional to the synaptic currents at the timings of real and target output spikes. We study these learning rules in computer simulations where we train integrate-and-fire neurons. Both learning rules allow neurons to fire at the desired timings, with sub-millisecond precision. We show how chronotrons can learn to classify their inputs, by firing identical, temporally precise spike trains for different inputs belonging to the same class. When the input is noisy, the classification also leads to noise reduction. We compute lower bounds for the memory capacity of chronotrons and explore the influence of various parameters on chronotrons' performance. The chronotrons can model neurons that encode information in the time of the first spike relative to the onset of salient stimuli or neurons in oscillatory networks that encode information in the phases of spikes relative to the background oscillation. Our results show that firing one spike per cycle optimizes memory capacity in neurons encoding information in the phase of firing relative to a background rhythm. PMID:22879876

  9. Artificial neural network for bubbles pattern recognition on the images

    Science.gov (United States)

    Poletaev, I. E.; Pervunin, K. S.; Tokarev, M. P.

    2016-10-01

    Two-phase bubble flows have been used in many technological and energy processes as processing oil, chemical and nuclear reactors. This explains large interest to experimental and numerical studies of such flows last several decades. Exploiting of optical diagnostics for analysis of the bubble flows allows researchers obtaining of instantaneous velocity fields and gaseous phase distribution with the high spatial resolution non-intrusively. Behavior of light rays exhibits an intricate manner when they cross interphase boundaries of gaseous bubbles hence the identification of the bubbles images is a complicated problem. This work presents a method of bubbles images identification based on a modern technology of deep learning called convolutional neural networks (CNN). Neural networks are able to determine overlapping, blurred, and non-spherical bubble images. They can increase accuracy of the bubble image recognition, reduce the number of outliers, lower data processing time, and significantly decrease the number of settings for the identification in comparison with standard recognition methods developed before. In addition, usage of GPUs speeds up the learning process of CNN owning to the modern adaptive subgradient optimization techniques.

  10. An improved genetic algorithm for designing optimal temporal patterns of neural stimulation

    Science.gov (United States)

    Cassar, Isaac R.; Titus, Nathan D.; Grill, Warren M.

    2017-12-01

    Objective. Electrical neuromodulation therapies typically apply constant frequency stimulation, but non-regular temporal patterns of stimulation may be more effective and more efficient. However, the design space for temporal patterns is exceedingly large, and model-based optimization is required for pattern design. We designed and implemented a modified genetic algorithm (GA) intended for design optimal temporal patterns of electrical neuromodulation. Approach. We tested and modified standard GA methods for application to designing temporal patterns of neural stimulation. We evaluated each modification individually and all modifications collectively by comparing performance to the standard GA across three test functions and two biophysically-based models of neural stimulation. Main results. The proposed modifications of the GA significantly improved performance across the test functions and performed best when all were used collectively. The standard GA found patterns that outperformed fixed-frequency, clinically-standard patterns in biophysically-based models of neural stimulation, but the modified GA, in many fewer iterations, consistently converged to higher-scoring, non-regular patterns of stimulation. Significance. The proposed improvements to standard GA methodology reduced the number of iterations required for convergence and identified superior solutions.

  11. An improved genetic algorithm for designing optimal temporal patterns of neural stimulation.

    Science.gov (United States)

    Cassar, Isaac R; Titus, Nathan D; Grill, Warren M

    2017-12-01

    Electrical neuromodulation therapies typically apply constant frequency stimulation, but non-regular temporal patterns of stimulation may be more effective and more efficient. However, the design space for temporal patterns is exceedingly large, and model-based optimization is required for pattern design. We designed and implemented a modified genetic algorithm (GA) intended for design optimal temporal patterns of electrical neuromodulation. We tested and modified standard GA methods for application to designing temporal patterns of neural stimulation. We evaluated each modification individually and all modifications collectively by comparing performance to the standard GA across three test functions and two biophysically-based models of neural stimulation. The proposed modifications of the GA significantly improved performance across the test functions and performed best when all were used collectively. The standard GA found patterns that outperformed fixed-frequency, clinically-standard patterns in biophysically-based models of neural stimulation, but the modified GA, in many fewer iterations, consistently converged to higher-scoring, non-regular patterns of stimulation. The proposed improvements to standard GA methodology reduced the number of iterations required for convergence and identified superior solutions.

  12. Spatial Patterns of Fire Recurrence Using Remote Sensing and GIS in the Brazilian Savanna: Serra do Tombador Nature Reserve, Brazil

    National Research Council Canada - National Science Library

    Daldegan, Gabriel; de Carvalho, Osmar; Guimarães, Renato; Gomes, Roberto; Ribeiro, Fernanda; McManus, Concepta

    2014-01-01

    .... The present study aimed to map the burnt areas and to describe the spatial patterns of fire recurrence and its interactions with the classes of land-cover that occurred in STNR and its surroundings...

  13. Spatial and Temporal Patterns of Unburned Areas within Fire Perimeters in the Northwestern United States from 1984 to 2014

    Science.gov (United States)

    Meddens, A. J.; Kolden, C.; Lutz, J. A.; Abatzoglou, J. T.; Hudak, A. T.

    2016-12-01

    Recently, there has been concern about increasing extent and severity of wildfires across the globe given rapid climate change. Areas that do not burn within fire perimeters can act as fire refugia, providing (1) protection from the detrimental effects of the fire, (2) seed sources, and (3) post-fire habitat on the landscape. However, recent studies have mainly focused on the higher end of the burn severity spectrum whereas the lower end of the burn severity spectrum has been largely ignored. We developed a spatially explicit database for 2,200 fires across the inland northwestern USA, delineating unburned areas within fire perimeters from 1984 to 2014. We used 1,600 Landsat scenes with one or two scenes before and one or two scenes after the fires to capture the unburned proportion of the fire. Subsequently, we characterized the spatial and temporal patterns of unburned areas and related the unburned proportion to interannual climate variability. The overall classification accuracy detecting unburned locations was 89.2% using a 10-fold cross-validation classification tree approach in combination with 719 randomly located field plots. The unburned proportion ranged from 2% to 58% with an average of 19% for a select number of fires. We find that using both an immediate post-fire image and a one-year post fire image improves classification accuracy of unburned islands over using just a single post-fire image. The spatial characteristics of the unburned islands differ between forested and non-forested regions with a larger amount of unburned area within non-forest. In addition, we show trends of unburned proportion related primarily to concurrent climatic drought conditions across the entire region. This database is important for subsequent analyses of fire refugia prioritization, vegetation recovery studies, ecosystem resilience, and forest management to facilitate unburned islands through fuels breaks, prescribed burning, and fire suppression strategies.

  14. Spotting neural spike patterns using an adversary background model.

    Science.gov (United States)

    Gat, I; Tishby, N

    2001-12-01

    The detection of a specific stochastic pattern embedded in an unknown background noise is a difficult pattern recognition problem, encountered in many applications such as word spotting in speech. A similar problem emerges when trying to detect a multineural spike pattern in a single electrical recording, embedded in the complex cortical activity of a behaving animal. Solving this problem is crucial for the identification of neuronal code words with specific meaning. The technical difficulty of this detection is due to the lack of a good statistical model for the background activity, which rapidly changes with the recording conditions and activity of the animal. This work introduces the use of an adversary background model. This model assumes that the background "knows" the pattern sought, up to a first-order statistics, and this "knowledge" creates a background composed of all the permutations of our pattern. We show that this background model is tightly connected to the type-based information-theoretic approach. Furthermore, we show that computing the likelihood ratio is actually decomposing the log-likelihood distribution according to types of the empirical counts. We demonstrate the application of this method for detection of the reward patterns in the basal ganglia of behaving monkeys, yielding some unexpected biological results.

  15. Comparison of GPU- and CPU-implementations of mean-firing rate neural networks on parallel hardware.

    Science.gov (United States)

    Dinkelbach, Helge Ülo; Vitay, Julien; Beuth, Frederik; Hamker, Fred H

    2012-01-01

    Modern parallel hardware such as multi-core processors (CPUs) and graphics processing units (GPUs) have a high computational power which can be greatly beneficial to the simulation of large-scale neural networks. Over the past years, a number of efforts have focused on developing parallel algorithms and simulators best suited for the simulation of spiking neural models. In this article, we aim at investigating the advantages and drawbacks of the CPU and GPU parallelization of mean-firing rate neurons, widely used in systems-level computational neuroscience. By comparing OpenMP, CUDA and OpenCL implementations towards a serial CPU implementation, we show that GPUs are better suited than CPUs for the simulation of very large networks, but that smaller networks would benefit more from an OpenMP implementation. As this performance strongly depends on data organization, we analyze the impact of various factors such as data structure, memory alignment and floating precision. We then discuss the suitability of the different hardware depending on the networks' size and connectivity, as random or sparse connectivities in mean-firing rate networks tend to break parallel performance on GPUs due to the violation of coalescence.

  16. Sociocultural patterning of neural activity during self-reflection

    DEFF Research Database (Denmark)

    Ma, Yina; Bang, Dan; Wang, Chenbo

    2014-01-01

    Western cultures encourage self-construals independent of social contexts whereas East Asian cultures foster interdependent self-construals that rely on how others perceive the self. How are culturally specific self-construals mediated by the human brain? Using functional MRI, we monitored neural...... that judgments of self vs. a public figure elicited greater activation in the medial prefrontal cortex (mPFC) in Danish than in Chinese participants regardless of attribute dimensions for judgments. However, self-judgments of social attributes induced greater activity in the temporoparietal junction (TPJ......) in Chinese than in Danish participants. Moreover, the group difference in TPJ activity was mediated by a measure of a cultural value (i.e., interdependence of self-construal). Our findings suggest that individuals in different sociocultural contexts may learn and/or adopt distinct strategies for self...

  17. Spatial Patterns of Fire Recurrence Using Remote Sensing and GIS in the Brazilian Savanna: Serra do Tombador Nature Reserve, Brazil

    Directory of Open Access Journals (Sweden)

    Gabriel Antunes Daldegan

    2014-10-01

    Full Text Available The Cerrado is the second largest biome in Brazil after the Amazon and is the savanna with the highest biodiversity in the world. Serra Tombador Natural Reserve (STNR is the largest private reserve located in Goiás State, and the fourth largest in the Cerrado biome. The present study aimed to map the burnt areas and to describe the spatial patterns of fire recurrence and its interactions with the classes of land-cover that occurred in STNR and its surroundings in the period between 2001 and 2010. Several Landsat TM images acquired around the months of July, August and September, coinciding with the region’s dry season when fire events intensify, were employed to monitor burnt areas. Fire scars were mapped using the supervised Mahalanobis-distance classifier and further refined using expert visual interpretation. Burnt area patterns were described by spatial landscape metrics. The effects of fire on landscape structure were obtained by comparing results among different land-cover classes, and results summarized in terms of fire history and frequencies. During the years covered by the study, 69% of the areas analyzed had fire events. The year with the largest burnt area was 2004, followed by 2001, 2007 and 2010. Thus, the largest fire events occurred in a 3-year cycle, which is compatible with other areas of the Brazilian savanna. The regions with higher annual probabilities of fire recurrence occur in the buffer zone around the park. The year 2004 also had the highest number of burnt area patches (831. In contrast, the burnt area in 2007 showed the most extensive fires with low number of patches (82. The physiognomies that suffered most fires were the native savanna formations. The study also identified areas where fires are frequently recurrent, highlighting priority areas requiring special attention. Thus, the methodology adopted in this study assists in monitoring and recovery of areas affected by fire over time.

  18. A Feedback Model of Attention Explains the Diverse Effects of Attention on Neural Firing Rates and Receptive Field Structure.

    Science.gov (United States)

    Miconi, Thomas; VanRullen, Rufin

    2016-02-01

    Visual attention has many effects on neural responses, producing complex changes in firing rates, as well as modifying the structure and size of receptive fields, both in topological and feature space. Several existing models of attention suggest that these effects arise from selective modulation of neural inputs. However, anatomical and physiological observations suggest that attentional modulation targets higher levels of the visual system (such as V4 or MT) rather than input areas (such as V1). Here we propose a simple mechanism that explains how a top-down attentional modulation, falling on higher visual areas, can produce the observed effects of attention on neural responses. Our model requires only the existence of modulatory feedback connections between areas, and short-range lateral inhibition within each area. Feedback connections redistribute the top-down modulation to lower areas, which in turn alters the inputs of other higher-area cells, including those that did not receive the initial modulation. This produces firing rate modulations and receptive field shifts. Simultaneously, short-range lateral inhibition between neighboring cells produce competitive effects that are automatically scaled to receptive field size in any given area. Our model reproduces the observed attentional effects on response rates (response gain, input gain, biased competition automatically scaled to receptive field size) and receptive field structure (shifts and resizing of receptive fields both spatially and in complex feature space), without modifying model parameters. Our model also makes the novel prediction that attentional effects on response curves should shift from response gain to contrast gain as the spatial focus of attention drifts away from the studied cell.

  19. Embryonic requirements for ErbB signaling in neural crest development and adult pigment pattern formation

    Science.gov (United States)

    Budi, Erine H.; Patterson, Larissa B.; Parichy, David M.

    2009-01-01

    SUMMARY Vertebrate pigment cells are derived from neural crest cells and are a useful system for studying neural crest-derived traits during post-embryonic development. In zebrafish, neural crest-derived melanophores differentiate during embryogenesis to produce stripes in the early larva. Dramatic changes to the pigment pattern occur subsequently during the larva-to-adult transformation, or metamorphosis. At this time, embryonic melanophores are replaced by newly differentiating metamorphic melanophores that form the adult stripes. Mutants with normal embryonic/early larval pigment patterns but defective adult patterns identify factors required uniquely to establish, maintain, or recruit the latent precursors to metamorphic melanophores. We show that one such mutant, picasso, lacks most metamorphic melanophores and results from mutations in the ErbB gene erbb3b, encoding an EGFR-like receptor tyrosine kinase. To identify critical periods for ErbB activities, we treated fish with pharmacological ErbB inhibitors and also knocked-down erbb3b by morpholino injection. These analyses reveal an embryonic critical period for ErbB signaling in promoting later pigment pattern metamorphosis, despite the normal patterning of embryonic/early larval melanophores. We further demonstrate a peak requirement during neural crest migration that correlates with early defects in neural crest pathfinding and peripheral ganglion formation. Finally, we show that erbb3b activities are both autonomous and non-autonomous to the metamorphic melanophore lineage. These data identify a very early, embryonic, requirement for erbb3b in the development of much later metamorphic melanophores, and suggest complex modes by which ErbB signals promote adult pigment pattern development. PMID:18508863

  20. Bursting as a source of non-linear determinism in the firing patterns of nigral dopamine neurons.

    Science.gov (United States)

    Jeong, Jaeseung; Shi, Wei-Xing; Hoffman, Ralph; Oh, Jihoon; Gore, John C; Bunney, Benjamin S; Peterson, Bradley S

    2012-11-01

    Nigral dopamine (DA) neurons in vivo exhibit complex firing patterns consisting of tonic single-spikes and phasic bursts that encode information for certain types of reward-related learning and behavior. Non-linear dynamical analysis has previously demonstrated the presence of a non-linear deterministic structure in complex firing patterns of DA neurons, yet the origin of this non-linear determinism remains unknown. In this study, we hypothesized that bursting activity is the primary source of non-linear determinism in the firing patterns of DA neurons. To test this hypothesis, we investigated the dimension complexity of inter-spike interval data recorded in vivo from bursting and non-bursting DA neurons in the chloral hydrate-anesthetized rat substantia nigra. We found that bursting DA neurons exhibited non-linear determinism in their firing patterns, whereas non-bursting DA neurons showed truly stochastic firing patterns. Determinism was also detected in the isolated burst and inter-burst interval data extracted from firing patterns of bursting neurons. Moreover, less bursting DA neurons in halothane-anesthetized rats exhibited higher dimensional spiking dynamics than do more bursting DA neurons in chloral hydrate-anesthetized rats. These results strongly indicate that bursting activity is the main source of low-dimensional, non-linear determinism in the firing patterns of DA neurons. This finding furthermore suggests that bursts are the likely carriers of meaningful information in the firing activities of DA neurons. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

  1. Prefrontal cortex HCN1 channels enable intrinsic persistent neural firing and executive memory function.

    Science.gov (United States)

    Thuault, Sébastien J; Malleret, Gaël; Constantinople, Christine M; Nicholls, Russell; Chen, Irene; Zhu, Judy; Panteleyev, Andrey; Vronskaya, Svetlana; Nolan, Matthew F; Bruno, Randy; Siegelbaum, Steven A; Kandel, Eric R

    2013-08-21

    In many cortical neurons, HCN1 channels are the major contributors to Ih, the hyperpolarization-activated current, which regulates the intrinsic properties of neurons and shapes their integration of synaptic inputs, paces rhythmic activity, and regulates synaptic plasticity. Here, we examine the physiological role of Ih in deep layer pyramidal neurons in mouse prefrontal cortex (PFC), focusing on persistent activity, a form of sustained firing thought to be important for the behavioral function of the PFC during working memory tasks. We find that HCN1 contributes to the intrinsic persistent firing that is induced by a brief depolarizing current stimulus in the presence of muscarinic agonists. Deletion of HCN1 or acute pharmacological blockade of Ih decreases the fraction of neurons capable of generating persistent firing. The reduction in persistent firing is caused by the membrane hyperpolarization that results from the deletion of HCN1 or Ih blockade, rather than a specific role of the hyperpolarization-activated current in generating persistent activity. In vivo recordings show that deletion of HCN1 has no effect on up states, periods of enhanced synaptic network activity. Parallel behavioral studies demonstrate that HCN1 contributes to the PFC-dependent resolution of proactive interference during working memory. These results thus provide genetic evidence demonstrating the importance of HCN1 to intrinsic persistent firing and the behavioral output of the PFC. The causal role of intrinsic persistent firing in PFC-mediated behavior remains an open question.

  2. Intelligent fuzzy-neural pattern generation and control of a quadrupedal bionic inspection robot

    Science.gov (United States)

    Sayfeddine, D.; Bulgakov, A. G.

    2017-02-01

    This paper represents a case study on ‘single leg single step’ pattern generation and control of quadrupedal bionic robot movement using intelligent fuzzy-neural approaches. The aim is to set up a flip-flop mechanical configuration allowing the robot to move one step forward. The same algorithm can be integrated to develop a full trajectory pattern as an interconnected task of global path planning for autonomous quadrupedal robots.

  3. Behavioral and Physiological Neural Network Analyses: A Common Pathway toward Pattern Recognition and Prediction

    Science.gov (United States)

    Ninness, Chris; Lauter, Judy L.; Coffee, Michael; Clary, Logan; Kelly, Elizabeth; Rumph, Marilyn; Rumph, Robin; Kyle, Betty; Ninness, Sharon K.

    2012-01-01

    Using 3 diversified datasets, we explored the pattern-recognition ability of the Self-Organizing Map (SOM) artificial neural network as applied to diversified nonlinear data distributions in the areas of behavioral and physiological research. Experiment 1 employed a dataset obtained from the UCI Machine Learning Repository. Data for this study…

  4. Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition

    NARCIS (Netherlands)

    Leon Rincon, Carlos; Moreno, José Fernando; Cely, Jorge

    2017-01-01

    The balance sheet is a snapshot that portraits the financial position of a firm at a specific point of time. Under the reasonable assumption that the financial position of a firm is unique and representative, we use a basic artificial neural network pattern recognition method on Colombian banks’

  5. Mechanisms and Neural Basis of Object and Pattern Recognition: A Study with Chess Experts

    Science.gov (United States)

    Bilalic, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

    2010-01-01

    Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and…

  6. Larval neurogenesis in Sabellaria alveolata reveals plasticity in polychaete neural patterning

    DEFF Research Database (Denmark)

    Brinkmann, Nora; Wanninger, Andreas

    2008-01-01

    reconstruction software. The overall pattern of neurogenesis in S. alveolata resembles the condition found in other planktonic polychaete trochophores where the larval neural body plan including a serotonergic prototroch nerve ring is directly followed by adult features of the nervous system...

  7. Moran's I quantifies spatio-temporal pattern formation in neural imaging data.

    Science.gov (United States)

    Schmal, Christoph; Myung, Jihwan; Herzel, Hanspeter; Bordyugov, Grigory

    2017-10-01

    Neural activities of the brain occur through the formation of spatio-temporal patterns. In recent years, macroscopic neural imaging techniques have produced a large body of data on these patterned activities, yet a numerical measure of spatio-temporal coherence has often been reduced to the global order parameter, which does not uncover the degree of spatial correlation. Here, we propose to use the spatial autocorrelation measure Moran's I, which can be applied to capture dynamic signatures of spatial organization. We demonstrate the application of this technique to collective cellular circadian clock activities measured in the small network of the suprachiasmatic nucleus (SCN) in the hypothalamus. We found that Moran's I is a practical quantitative measure of the degree of spatial coherence in neural imaging data. Initially developed with a geographical context in mind, Moran's I accounts for the spatial organization of any interacting units. Moran's I can be modified in accordance with the characteristic length scale of a neural activity pattern. It allows a quantification of statistical significance levels for the observed patterns. We describe the technique applied to synthetic datasets and various experimental imaging time-series from cultured SCN explants. It is demonstrated that major characteristics of the collective state can be described by Moran's I and the traditional Kuramoto order parameter R in a complementary fashion. Python 2.7 code of illustrative examples can be found in the Supplementary Material. christoph.schmal@charite.de or grigory.bordyugov@hu-berlin.de. Supplementary data are available at Bioinformatics online.

  8. Signal Processing, Pattern Formation and Adaptation in Neural Oscillators

    Science.gov (United States)

    2016-11-29

    be. Humans recognize complex acoustic patterns under challenging listening conditions, such as a voice in a crowded room or on a city street. We...double limit cycle regime. Filled circles indicate stable fixed points (attractors) and empty circles unstable fixed points (repellers). Arrows...plotted over time for a trajectory in panel C (phase locking). Filled circles in panels B and C indicate stable fixed points. DISTRIBUTION A

  9. Global neural pattern similarity as a common basis for categorization and recognition memory.

    Science.gov (United States)

    Davis, Tyler; Xue, Gui; Love, Bradley C; Preston, Alison R; Poldrack, Russell A

    2014-05-28

    Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categorization and long-term memory, we develop a theory and a method to test whether memory strength arises from the global similarity among neural representations. In human subjects, we find significant correlations between global similarity among activation patterns in the MTLs and both subsequent memory confidence in a recognition memory task and model-based measures of memory strength in a category learning task. Our work bridges formal cognitive theories and neuroscientific models by illustrating that the same global similarity computations underlie processing in multiple cognitive domains. Moreover, by establishing a link between neural similarity and psychological memory strength, our findings suggest that there may be an isomorphism between psychological and neural representational spaces that can be exploited to test cognitive theories at both the neural and behavioral levels. Copyright © 2014 the authors 0270-6474/14/347472-13$15.00/0.

  10. Global Neural Pattern Similarity as a Common Basis for Categorization and Recognition Memory

    Science.gov (United States)

    Xue, Gui; Love, Bradley C.; Preston, Alison R.; Poldrack, Russell A.

    2014-01-01

    Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categorization and long-term memory, we develop a theory and a method to test whether memory strength arises from the global similarity among neural representations. In human subjects, we find significant correlations between global similarity among activation patterns in the MTLs and both subsequent memory confidence in a recognition memory task and model-based measures of memory strength in a category learning task. Our work bridges formal cognitive theories and neuroscientific models by illustrating that the same global similarity computations underlie processing in multiple cognitive domains. Moreover, by establishing a link between neural similarity and psychological memory strength, our findings suggest that there may be an isomorphism between psychological and neural representational spaces that can be exploited to test cognitive theories at both the neural and behavioral levels. PMID:24872552

  11. GluA1 Phosphorylation Alters Evoked Firing Pattern In Vivo

    Directory of Open Access Journals (Sweden)

    Balázs Barkóczi

    2012-01-01

    Full Text Available AMPA and NMDA receptors convey fast synaptic transmission in the CNS. Their relative contribution to synaptic output and phosphorylation state regulate synaptic plasticity. The AMPA receptor subunit GluA1 is central in synaptic plasticity. Phosphorylation of GluA1 regulates channel properties and trafficking. The firing rate averaged over several hundred ms is used to monitor cellular input. However, plasticity requires the timing of spiking within a few ms; therefore, it is important to understand how phosphorylation governs these events. Here, we investigate whether the GluA1 phosphorylation (p-GluA1 alters the spiking patterns of CA1 cells in vivo. The antidepressant Tianeptine was used for inducing p-GluA1, which resulted in enhanced AMPA-evoked spiking. By comparing the spiking patterns of AMPA-evoked activity with matched firing rates, we show that the spike-trains after Tianeptine application show characteristic features, distinguishing from spike-trains triggered by strong AMPA stimulation. The interspike-interval distributions are different between the two groups, suggesting that neuronal output may differ when new inputs are activated compared to increasing the gain of previously activated receptors. Furthermore, we also show that NMDA evokes spiking with different patterns to AMPA spike-trains. These results support the role of the modulation of NMDAR/AMPAR ratio and p-GluA1 in plasticity and temporal coding.

  12. New aspects of firing pattern autocontrol in oxytocin and vasopressin neurones.

    Science.gov (United States)

    Moos, F; Gouzènes, L; Brown, D; Dayanithi, G; Sabatier, N; Boissin, L; Rabié, A; Richard, P

    1998-01-01

    In the rat, oxytocin (OT) and vasopressin (AVP) neurones exhibit specific electrical activities which are controlled by OT and AVP released from soma and dendrites within the magnocellular hypothalamic nuclei. OT enhances amplitude and frequency of suckling-induced bursts, and changes basal firing characteristics: spike patterning becomes very irregular (spike clusters separated by long silences), firing rate is highly variable, oscillating before facilitated bursts. This unstable behaviour which markedly decreases during hyperosmotic stimulation (interrupting bursting) could be a prerequisite for bursting. The effects of AVP depend on the initial phasic pattern of AVP neurones: AVP excites weakly active neurones (increasing burst duration, decreasing silences) and inhibits highly active neurones; neurones with intermediate phasic activity are unaffected. Thus, AVP ensures all AVP neurones discharge with moderate phasic activity (bursts and silences lasting 20-40 s), known to optimise systemic AVP release. V1a-type receptors are involved in AVP actions. In conclusion, OT and AVP control their respective neurones in a complex manner to favour the patterns of activity which are the best suited for an efficient systemic hormone release.

  13. Neuromorphic Hardware Architecture Using the Neural Engineering Framework for Pattern Recognition.

    Science.gov (United States)

    Wang, Runchun; Thakur, Chetan Singh; Cohen, Gregory; Hamilton, Tara Julia; Tapson, Jonathan; van Schaik, Andre

    2017-06-01

    We present a hardware architecture that uses the neural engineering framework (NEF) to implement large-scale neural networks on field programmable gate arrays (FPGAs) for performing massively parallel real-time pattern recognition. NEF is a framework that is capable of synthesising large-scale cognitive systems from subnetworks and we have previously presented an FPGA implementation of the NEF that successfully performs nonlinear mathematical computations. That work was developed based on a compact digital neural core, which consists of 64 neurons that are instantiated by a single physical neuron using a time-multiplexing approach. We have now scaled this approach up to build a pattern recognition system by combining identical neural cores together. As a proof of concept, we have developed a handwritten digit recognition system using the MNIST database and achieved a recognition rate of 96.55%. The system is implemented on a state-of-the-art FPGA and can process 5.12 million digits per second. The architecture and hardware optimisations presented offer high-speed and resource-efficient means for performing high-speed, neuromorphic, and massively parallel pattern recognition and classification tasks.

  14. A new training algorithm using artificial neural networks to classify gender-specific dynamic gait patterns.

    Science.gov (United States)

    Andrade, Andre; Costa, Marcelo; Paolucci, Leopoldo; Braga, Antônio; Pires, Flavio; Ugrinowitsch, Herbert; Menzel, Hans-Joachim

    2015-01-01

    The aim of this study was to present a new training algorithm using artificial neural networks called multi-objective least absolute shrinkage and selection operator (MOBJ-LASSO) applied to the classification of dynamic gait patterns. The movement pattern is identified by 20 characteristics from the three components of the ground reaction force which are used as input information for the neural networks in gender-specific gait classification. The classification performance between MOBJ-LASSO (97.4%) and multi-objective algorithm (MOBJ) (97.1%) is similar, but the MOBJ-LASSO algorithm achieved more improved results than the MOBJ because it is able to eliminate the inputs and automatically select the parameters of the neural network. Thus, it is an effective tool for data mining using neural networks. From 20 inputs used for training, MOBJ-LASSO selected the first and second peaks of the vertical force and the force peak in the antero-posterior direction as the variables that classify the gait patterns of the different genders.

  15. Using genetic algorithm feature selection in neural classification systems for image pattern recognition

    Directory of Open Access Journals (Sweden)

    Margarita R. Gamarra A.

    2012-09-01

    Full Text Available Pattern recognition performance depends on variations during extraction, selection and classification stages. This paper presents an approach to feature selection by using genetic algorithms with regard to digital image recognition and quality control. Error rate and kappa coefficient were used for evaluating the genetic algorithm approach Neural networks were used for classification, involving the features selected by the genetic algorithms. The neural network approach was compared to a K-nearest neighbor classifier. The proposed approach performed better than the other methods.

  16. Modeling thalamocortical cell: impact of ca channel distribution and cell geometry on firing pattern.

    Science.gov (United States)

    Zomorrodi, Reza; Kröger, Helmut; Timofeev, Igor

    2008-01-01

    The influence of calcium channel distribution and geometry of the thalamocortical cell upon its tonic firing and the low threshold spike (LTS) generation was studied in a 3-compartment model, which represents soma, proximal and distal dendrites as well as in multi-compartment model using the morphology of a real reconstructed neuron. Using an uniform distribution of Ca(2+) channels, we determined the minimal number of low threshold voltage-activated calcium channels and their permeability required for the onset of LTS in response to a hyperpolarizing current pulse. In the 3-compartment model, we found that the channel distribution influences the firing pattern only in the range of 3% below the threshold value of total T-channel density. In the multi-compartmental model, the LTS could be generated by only 64% of unequally distributed T-channels compared to the minimal number of equally distributed T-channels. For a given channel density and injected current, the tonic firing frequency was found to be inversely proportional to the size of the cell. However, when the Ca(2+) channel density was elevated in soma or proximal dendrites, then the amplitude of LTS response and burst spike frequencies were determined by the ratio of total to threshold number of T-channels in the cell for a specific geometry.

  17. Neural Network Based Recognition of Signal Patterns in Application to Automatic Testing of Rails

    Directory of Open Access Journals (Sweden)

    Tomasz Ciszewski

    2006-01-01

    Full Text Available The paper describes the application of neural network for recognition of signal patterns in measuring data gathered by the railroad ultrasound testing car. Digital conversion of the measuring signal allows to store and process large quantities of data. The elaboration of smart, effective and automatic procedures recognizing the obtained patterns on the basisof measured signal amplitude has been presented. The test shows only two classes of pattern recognition. In authors’ opinion if we deliver big enough quantity of training data, presented method is applicable to a system that recognizes many classes.

  18. Entropy-based generation of supervised neural networks for classification of structured patterns.

    Science.gov (United States)

    Tsai, Hsien-Leing; Lee, Shie-Jue

    2004-03-01

    Sperduti and Starita proposed a new type of neural network which consists of generalized recursive neurons for classification of structures. In this paper, we propose an entropy-based approach for constructing such neural networks for classification of acyclic structured patterns. Given a classification problem, the architecture, i.e., the number of hidden layers and the number of neurons in each hidden layer, and all the values of the link weights associated with the corresponding neural network are automatically determined. Experimental results have shown that the networks constructed by our method can have a better performance, with respect to network size, learning speed, or recognition accuracy, than the networks obtained by other methods.

  19. Bayesian active learning of neural firing rate maps with transformed gaussian process priors.

    Science.gov (United States)

    Park, Mijung; Weller, J Patrick; Horwitz, Gregory D; Pillow, Jonathan W

    2014-08-01

    A firing rate map, also known as a tuning curve, describes the nonlinear relationship between a neuron's spike rate and a low-dimensional stimulus (e.g., orientation, head direction, contrast, color). Here we investigate Bayesian active learning methods for estimating firing rate maps in closed-loop neurophysiology experiments. These methods can accelerate the characterization of such maps through the intelligent, adaptive selection of stimuli. Specifically, we explore the manner in which the prior and utility function used in Bayesian active learning affect stimulus selection and performance. Our approach relies on a flexible model that involves a nonlinearly transformed gaussian process (GP) prior over maps and conditionally Poisson spiking. We show that infomax learning, which selects stimuli to maximize the information gain about the firing rate map, exhibits strong dependence on the seemingly innocuous choice of nonlinear transformation function. We derive an alternate utility function that selects stimuli to minimize the average posterior variance of the firing rate map and analyze the surprising relationship between prior parameterization, stimulus selection, and active learning performance in GP-Poisson models. We apply these methods to color tuning measurements of neurons in macaque primary visual cortex.

  20. Coexistence of reward and unsupervised learning during the operant conditioning of neural firing rates.

    Directory of Open Access Journals (Sweden)

    Robert R Kerr

    Full Text Available A fundamental goal of neuroscience is to understand how cognitive processes, such as operant conditioning, are performed by the brain. Typical and well studied examples of operant conditioning, in which the firing rates of individual cortical neurons in monkeys are increased using rewards, provide an opportunity for insight into this. Studies of reward-modulated spike-timing-dependent plasticity (RSTDP, and of other models such as R-max, have reproduced this learning behavior, but they have assumed that no unsupervised learning is present (i.e., no learning occurs without, or independent of, rewards. We show that these models cannot elicit firing rate reinforcement while exhibiting both reward learning and ongoing, stable unsupervised learning. To fix this issue, we propose a new RSTDP model of synaptic plasticity based upon the observed effects that dopamine has on long-term potentiation and depression (LTP and LTD. We show, both analytically and through simulations, that our new model can exhibit unsupervised learning and lead to firing rate reinforcement. This requires that the strengthening of LTP by the reward signal is greater than the strengthening of LTD and that the reinforced neuron exhibits irregular firing. We show the robustness of our findings to spike-timing correlations, to the synaptic weight dependence that is assumed, and to changes in the mean reward. We also consider our model in the differential reinforcement of two nearby neurons. Our model aligns more strongly with experimental studies than previous models and makes testable predictions for future experiments.

  1. Histomorphological patterns in osseous rests exposed at fire; Patrones histomorfologicos en restos oseos expuestos al fuego

    Energy Technology Data Exchange (ETDEWEB)

    Medina, C.; Tiesler, V. [Facultad de Ciencias Antropologicas, UADY, 97000 Merida, Yucatan (Mexico); Oliva, A.I.; Quintana, P. [CINVESTAV, IPN Unidad Merida, Depto. Fisica Aplicada, 97310 Merida (Mexico)

    2005-07-01

    Histomorphology as part of morphological research studies bony structure on the tissue level. Its methods are applied in this investigation to evaluate histomorphological impact patterns in heat-exposed bony material, particularly color changes, fissure patterns, volumetric reduction, and changes in the size of Haversian canals. These variables were evaluated in exposed thin sections of porcine long bones, obtained during two experimental series. The first one was conducted under stable thermal conditions in a furnace by measuring heat impact in stepped time (I to S hours) and temperature intervals (200 to 800 C). During a second experimental phase, bony samples were exposed to direct fire in defined time and heat intervals. The treated specimens were then sectioned and microscopically scrutinized. The results presented here were designed to offer new analytical, measurable standards in the investigation of forms of heat exposition of the human body, applicable in forensics and the study of ancient Maya posthumous body treatments. (Author)

  2. Subspace projection approaches to classification and visualization of neural network-level encoding patterns.

    Directory of Open Access Journals (Sweden)

    Remus Oşan

    2007-05-01

    Full Text Available Recent advances in large-scale ensemble recordings allow monitoring of activity patterns of several hundreds of neurons in freely behaving animals. The emergence of such high-dimensional datasets poses challenges for the identification and analysis of dynamical network patterns. While several types of multivariate statistical methods have been used for integrating responses from multiple neurons, their effectiveness in pattern classification and predictive power has not been compared in a direct and systematic manner. Here we systematically employed a series of projection methods, such as Multiple Discriminant Analysis (MDA, Principal Components Analysis (PCA and Artificial Neural Networks (ANN, and compared them with non-projection multivariate statistical methods such as Multivariate Gaussian Distributions (MGD. Our analyses of hippocampal data recorded during episodic memory events and cortical data simulated during face perception or arm movements illustrate how low-dimensional encoding subspaces can reveal the existence of network-level ensemble representations. We show how the use of regularization methods can prevent these statistical methods from over-fitting of training data sets when the trial numbers are much smaller than the number of recorded units. Moreover, we investigated the extent to which the computations implemented by the projection methods reflect the underlying hierarchical properties of the neural populations. Based on their ability to extract the essential features for pattern classification, we conclude that the typical performance ranking of these methods on under-sampled neural data of large dimension is MDA>PCA>ANN>MGD.

  3. Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder

    Directory of Open Access Journals (Sweden)

    Walshe Muriel

    2011-01-01

    Full Text Available Abstract Background Impairments in executive function and language processing are characteristic of both schizophrenia and bipolar disorder. Their functional neuroanatomy demonstrate features that are shared as well as specific to each disorder. Determining the distinct pattern of neural responses in schizophrenia and bipolar disorder may provide biomarkers for their diagnoses. Methods 104 participants underwent functional magnetic resonance imaging (fMRI scans while performing a phonological verbal fluency task. Subjects were 32 patients with schizophrenia in remission, 32 patients with bipolar disorder in an euthymic state, and 40 healthy volunteers. Neural responses to verbal fluency were examined in each group, and the diagnostic potential of the pattern of the neural responses was assessed with machine learning analysis. Results During the verbal fluency task, both patient groups showed increased activation in the anterior cingulate, left dorsolateral prefrontal cortex and right putamen as compared to healthy controls, as well as reduced deactivation of precuneus and posterior cingulate. The magnitude of activation was greatest in patients with schizophrenia, followed by patients with bipolar disorder and then healthy individuals. Additional recruitment in the right inferior frontal and right dorsolateral prefrontal cortices was observed in schizophrenia relative to both bipolar disorder and healthy subjects. The pattern of neural responses correctly identified individual patients with schizophrenia with an accuracy of 92%, and those with bipolar disorder with an accuracy of 79% in which mis-classification was typically of bipolar subjects as healthy controls. Conclusions In summary, both schizophrenia and bipolar disorder are associated with altered function in prefrontal, striatal and default mode networks, but the magnitude of this dysfunction is particularly marked in schizophrenia. The pattern of response to verbal fluency is highly

  4. Neural communication patterns underlying conflict detection, resolution, and adaptation.

    Science.gov (United States)

    Oehrn, Carina R; Hanslmayr, Simon; Fell, Juergen; Deuker, Lorena; Kremers, Nico A; Do Lam, Anne T; Elger, Christian E; Axmacher, Nikolai

    2014-07-30

    In an ever-changing environment, selecting appropriate responses in conflicting situations is essential for biological survival and social success and requires cognitive control, which is mediated by dorsomedial prefrontal cortex (DMPFC) and dorsolateral prefrontal cortex (DLPFC). How these brain regions communicate during conflict processing (detection, resolution, and adaptation), however, is still unknown. The Stroop task provides a well-established paradigm to investigate the cognitive mechanisms mediating such response conflict. Here, we explore the oscillatory patterns within and between the DMPFC and DLPFC in human epilepsy patients with intracranial EEG electrodes during an auditory Stroop experiment. Data from the DLPFC were obtained from 12 patients. Thereof four patients had additional DMPFC electrodes available for interaction analyses. Our results show that an early θ (4-8 Hz) modulated enhancement of DLPFC γ-band (30-100 Hz) activity constituted a prerequisite for later successful conflict processing. Subsequent conflict detection was reflected in a DMPFC θ power increase that causally entrained DLPFC θ activity (DMPFC to DLPFC). Conflict resolution was thereafter completed by coupling of DLPFC γ power to DMPFC θ oscillations. Finally, conflict adaptation was related to increased postresponse DLPFC γ-band activity and to θ coupling in the reverse direction (DLPFC to DMPFC). These results draw a detailed picture on how two regions in the prefrontal cortex communicate to resolve cognitive conflicts. In conclusion, our data show that conflict detection, control, and adaptation are supported by a sequence of processes that use the interplay of θ and γ oscillations within and between DMPFC and DLPFC. Copyright © 2014 the authors 0270-6474/14/3410438-15$15.00/0.

  5. Reduction of the dimension of neural network models in problems of pattern recognition and forecasting

    Science.gov (United States)

    Nasertdinova, A. D.; Bochkarev, V. V.

    2017-11-01

    Deep neural networks with a large number of parameters are a powerful tool for solving problems of pattern recognition, prediction and classification. Nevertheless, overfitting remains a serious problem in the use of such networks. A method of solving the problem of overfitting is proposed in this article. This method is based on reducing the number of independent parameters of a neural network model using the principal component analysis, and can be implemented using existing libraries of neural computing. The algorithm was tested on the problem of recognition of handwritten symbols from the MNIST database, as well as on the task of predicting time series (rows of the average monthly number of sunspots and series of the Lorentz system were used). It is shown that the application of the principal component analysis enables reducing the number of parameters of the neural network model when the results are good. The average error rate for the recognition of handwritten figures from the MNIST database was 1.12% (which is comparable to the results obtained using the "Deep training" methods), while the number of parameters of the neural network can be reduced to 130 times.

  6. Recurrent Neural Network For Forecasting Time Series With Long Memory Pattern

    Science.gov (United States)

    Walid; Alamsyah

    2017-04-01

    Recurrent Neural Network as one of the hybrid models are often used to predict and estimate the issues related to electricity, can be used to describe the cause of the swelling of electrical load which experienced by PLN. In this research will be developed RNN forecasting procedures at the time series with long memory patterns. Considering the application is the national electrical load which of course has a different trend with the condition of the electrical load in any country. This research produces the algorithm of time series forecasting which has long memory pattern using E-RNN after this referred to the algorithm of integrated fractional recurrent neural networks (FIRNN).The prediction results of long memory time series using models Fractional Integrated Recurrent Neural Network (FIRNN) showed that the model with the selection of data difference in the range of [-1,1] and the model of Fractional Integrated Recurrent Neural Network (FIRNN) (24,6,1) provides the smallest MSE value, which is 0.00149684.

  7. Scaling Pattern to Variations in Size during Development of the Vertebrate Neural Tube

    Science.gov (United States)

    Uygur, Aysu; Young, John; Huycke, Tyler R.; Koska, Mervenaz; Briscoe, James; Tabin, Clifford J.

    2016-01-01

    SUMMARY Anatomical proportions are robustly maintained in individuals that vary enormously in size, both within a species and between members of related taxa. However, the mechanisms underlying scaling are still poorly understood. We have examined this phenomenon in the context of the patterning of the ventral neural tube in response to a gradient of the morphogen Sonic hedgehog (SHH) in the chick and zebra finch, two species that differ in size during the time of neural tube patterning. We find that scaling is achieved, at least in part, by altering the sensitivity of the target cells to SHH and appears to be achieved by modulating the ratio of the repressive and activating transcriptional regulators, GLI2 and GLI3. This mechanism contrasts with previous experimental and theoretical analyses of morphogenic scaling that have focused on compensatory changes in the morphogen gradient itself. PMID:27093082

  8. Pattern Recognition and Classification of Fatal Traffic Accidents in Israel A Neural Network Approach

    DEFF Research Database (Denmark)

    Prato, Carlo Giacomo; Gitelman, Victoria; Bekhor, Shlomo

    2011-01-01

    This article provides a broad picture of fatal traffic accidents in Israel to answer an increasing need of addressing compelling problems, designing preventive measures, and targeting specific population groups with the objective of reducing the number of traffic fatalities. The analysis focuses...... on 1,793 fatal traffic accidents occurred during the period between 2003 and 2006 and applies Kohonen and feed-forward back-propagation neural networks with the objective of extracting from the data typical patterns and relevant factors. Kohonen neural networks reveal five compelling accident patterns......: (1) single-vehicle accidents of young drivers, (2) multiple-vehicle accidents between young drivers, (3) accidents involving motorcyclists or cyclists, (4) accidents where elderly pedestrians crossed in urban areas, and (5) accidents where children and teenagers cross major roads in small urban areas...

  9. Facilitation of memory encoding in primate hippocampus by a neuroprosthesis that promotes task-specific neural firing

    Science.gov (United States)

    Hampson, Robert E.; Song, Dong; Opris, Ioan; Santos, Lucas M.; Shin, Dae C.; Gerhardt, Greg A.; Marmarelis, Vasilis Z.; Berger, Theodore W.; Deadwyler, Sam A.

    2013-12-01

    Objective. Memory accuracy is a major problem in human disease and is the primary factor that defines Alzheimer’s, ageing and dementia resulting from impaired hippocampal function in the medial temporal lobe. Development of a hippocampal memory neuroprosthesis that facilitates normal memory encoding in nonhuman primates (NHPs) could provide the basis for improving memory in human disease states. Approach. NHPs trained to perform a short-term delayed match-to-sample (DMS) memory task were examined with multi-neuron recordings from synaptically connected hippocampal cell fields, CA1 and CA3. Recordings were analyzed utilizing a previously developed nonlinear multi-input multi-output (MIMO) neuroprosthetic model, capable of extracting CA3-to-CA1 spatiotemporal firing patterns during DMS performance. Main results. The MIMO model verified that specific CA3-to-CA1 firing patterns were critical for the successful encoding of sample phase information on more difficult DMS trials. This was validated by the delivery of successful MIMO-derived encoding patterns via electrical stimulation to the same CA1 recording locations during the sample phase which facilitated task performance in the subsequent, delayed match phase, on difficult trials that required more precise encoding of sample information. Significance. These findings provide the first successful application of a neuroprosthesis designed to enhance and/or repair memory encoding in primate brain.

  10. Facilitation and restoration of cognitive function in primate prefrontal cortex by a neuroprosthesis that utilizes minicolumn-specific neural firing

    Science.gov (United States)

    Hampson, Robert E.; Gerhardt, Greg A.; Marmarelis, Vasilis; Song, Dong; Opris, Ioan; Santos, Lucas; Berger, Theodore W.; Deadwyler, Sam A.

    2012-10-01

    Objective. Maintenance of cognitive control is a major concern for many human disease conditions; therefore, a major goal of human neuroprosthetics is to facilitate and/or recover the cognitive function when such circumstances impair appropriate decision making. Approach. Minicolumnar activity from the prefrontal cortex (PFC) was recorded from nonhuman primates trained to perform a delayed match to sample (DMS), via custom-designed conformal multielectrode arrays that provided inter-laminar recordings from neurons in the PFC layer 2/3 and layer 5. Such recordings were analyzed via a previously demonstrated nonlinear multi-input-multi-output (MIMO) neuroprosthesis in rodents, which extracted and characterized multicolumnar firing patterns during DMS performance. Main results. The MIMO model verified that the conformal recorded individual PFC minicolumns responded to entrained target selections in patterns critical for successful DMS performance. This allowed the substitution of task-related layer 5 neuron firing patterns with electrical stimulation in the same recording regions during columnar transmission from layer 2/3 at the time of target selection. Such stimulation improved normal task performance, but more importantly, recovered performance when applied as a neuroprosthesis following the pharmacological disruption of decision making in the same task. Significance. These findings provide the first successful application of neuroprosthesis in the primate brain designed specifically to restore or repair the disrupted cognitive function.

  11. Prefrontal Cortex HCN1 Channels Enable Intrinsic Persistent Neural Firing and Executive Memory Function

    OpenAIRE

    Thuault, Sébastien J.; Malleret, Gaël; Constantinople, Christine M.; Nicholls, Russell; Chen, Irene; Zhu, Judy; Panteleyev, Andrey; Vronskaya, Svetlana; Nolan, Matthew F.; Bruno, Randy; Siegelbaum, Steven A.; Kandel, Eric R.

    2013-01-01

    In many cortical neurons, HCN1 channels are the major contributors to I-h, the hyperpolarization-activated current, which regulates the intrinsic properties of neurons and shapes their integration of synaptic inputs, paces rhythmic activity, and regulates synaptic plasticity. Here, we examine the physiological role of I-h in deep layer pyramidal neurons in mouse prefrontal cortex (PFC), focusing on persistent activity, a form of sustained firing thought to be important for the behavioral func...

  12. Probabilistic and Other Neural Nets in Multi-Hole Probe Calibration and Flow Angularity Pattern Recognition

    Science.gov (United States)

    Baskaran, Subbiah; Ramachandran, Narayanan; Noever, David

    1998-01-01

    The use of probabilistic (PNN) and multilayer feed forward (MLFNN) neural networks are investigated for calibration of multi-hole pressure probes and the prediction of associated flow angularity patterns in test flow fields. Both types of networks are studied in detail for their calibration and prediction characteristics. The current formalism can be applied to any multi-hole probe, however the test results for the most commonly used five-hole Cone and Prism probe types alone are reported in this article.

  13. Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity.

    Science.gov (United States)

    Srinivasa, Narayan; Cho, Youngkwan

    2014-01-01

    A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI) network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI) is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns-both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity.

  14. Unsupervised Discrimination of Patterns in Spiking Neural Networks with Excitatory and Inhibitory Synaptic Plasticity

    Directory of Open Access Journals (Sweden)

    Narayan eSrinivasa

    2014-12-01

    Full Text Available A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns – both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity.

  15. Positive and Negative Ultrasonic Social Signals Elicit Opposing Firing Patterns in Rat Amygdala

    Science.gov (United States)

    Parsana, Ashwini J.; Li, Nanxin; Brown, Thomas H.

    2011-01-01

    Rat ultrasonic vocalizations (USVs) are ethologically-essential social signals. Under natural conditions, 22 kHz USVs and 50 kHz USVs are emitted in association with negative and positive emotional states, respectively. Our first experiment examined freezing behavior elicited in naïve Sprague-Dawley rats by a 22 kHz USV, a 50 kHz USV, and frequency-matched tones. None of the stimuli elicited freezing, which is the most commonly-used index of fear. The second experiment examined single-unit responses to these stimuli in the amygdala (AM), which is well-known for its role in innate and acquired fear responses. Among 127 well-discriminated single units, 82% were auditory-responsive. Elicited firing patterns were classified using a multidimensional scheme that included transient (phasic) responses to the stimulus onsets and/or offsets as well as sustained (tonic) responses during the stimulus. Tonic responses, which are not ordinarily evaluated in AM, were 4.4-times more common than phasic responses. The 22 kHz stimuli tended to elicit tonic increases in the firing rates, whereas the 50 kHz stimuli more often elicited tonic decreases in firing rates. These opposing tonic responses correspond with the ethological valence of USVs in the two frequency bands. Thus, a relatively-small sample of single-unit responses in AM furnished a more sensitive index of emotional valence than freezing behavior. Latency analysis suggested that stimuli in the two frequency bands are processed through different pathways to AM. One possible interpretation is that phasic responses in AM reflect the detection of a stimulus change, whereas tonic responses indicate the valence of the detected stimulus. PMID:21911010

  16. Spatio-temporal patterns of forest fires: a comprehensive application of the K-function

    Science.gov (United States)

    Tonini, Marj; Vega Orozco, Carmen; Kanevski, Mikhaïl; Conedera, Marco

    2013-04-01

    The spatial distribution of uncontrolled hazardous events, such as forest fires, is largely investigated from the scientific community with the purpose of finding out the more vulnerable areas. Mapping the location of spatio-temporal sequences for a given environmental dataset is of great impact; however, the majority of the studies miss the analysis of the aggregation over time. Nonetheless discovering unusual temporal pattern for a given time sequence is fundamental to understand the phenomena and underlying processes. The present study aims investigating both the spatial and the temporal cluster behaviour of forest fires occurrences registered in Canton Ticino (Switzerland) over a period of about 40 years and testing if space and time interact in generate clusters. To do this, the purely spatial, the time and the space-time extensions of the Ripley's K-function were applied. The Ripley's K-function is a statistic exploratory method which enables detecting whether or not a point process (e.g. the location of the ignition points) is randomly distributed. The purely spatial K-function K(r) is defined as the expected number of further events within an area of radius r around an arbitrary point of the pattern, divided by the intensity of the phenomenon. Under completely spatial randomness, the value of the K(r) is equal to the area around the point (=πr2), while observations above this theoretical value imply a clustering behaviour at the corresponding distance r. For the purely time analysis, the Ripley's K-function K(t) can be taught as a reformulation of the spatial version to detect unexpected aggregation of events over the temporal scale. For its computation, the value of the intensity used in K(r) is replaced by the total duration of the time sequence divided by the total number of observed events, and the distance r is replaced by the time interval t. Under time-regularity, K(t) equals 2t, whereas, observed measures above this theoretical value indicate a

  17. Firing pattern of bursting neurons under sinusoidal drive in mean-field modeling.

    Science.gov (United States)

    Wu, H; Kim, J W; Robinson, P A; Drysdale, P M

    2009-07-07

    Bursting has been observed in many sensory neurons, and is thought to be important in neural signaling, sleep, and some disorders of the brain. Bursting neurons have been studied via various types of conductance-based models at the single-neuron level. Important features of bursting have been reproduced by this type of model, but it is not certain how well the behavior of populations of bursting neurons can be represented solely by that of individual neurons. To study bursting neurons at the population level, a conductance-based model is incorporated into a mean-field model to yield a mean-field bursting model. The responses of the model to sinusoidal inputs are studied, showing that neurons with various different initial states are capable of phase-locked or intermittent firing, depending on their baseline voltage. Furthermore, depending on this voltage, the bursting frequency either slaves to the original unperturbed bursting frequency or approaches a steady value when the external driving frequency increases. Finally, use of white noise perturbations shows that the bursting frequency of the neurons remains the same even under a more general external stimulus.

  18. Change in sympathetic nerve firing pattern associated with dietary weight loss in the metabolic syndrome.

    Science.gov (United States)

    Lambert, Elisabeth; Straznicky, Nora E; Dawood, Tye; Ika-Sari, Carolina; Grima, Mariee; Esler, Murray D; Schlaich, Markus P; Lambert, Gavin W

    2011-01-01

    Sympathetic activation in subjects with the metabolic syndrome (MS) plays a role in the pathogenesis of cardiovascular disease development. Diet-induced weight loss decreases sympathetic outflow. However the mechanisms that account for sympathetic inhibition are not known. We sought to provide a detailed description of the sympathetic response to diet by analyzing the firing behavior of single-unit sympathetic nerve fibers. Fourteen subjects (57 ± 2 years, nine men, five females) fulfilling ATP III criteria for the MS underwent a 3-month low calorie diet. Metabolic profile, hemodynamic parameters, and multi-unit and single-unit muscle sympathetic nerve activity (MSNA, microneurography) were assessed prior to and at the end of the diet. Patients' weight dropped from 96 ± 4 to 88 ± 3 kg (P metabolic parameters (fasting glucose: -0.302.1 ± 0.118 mmol/l, total cholesterol: -0.564 ± 0.164 mmol/l, triglycerides: -0.414 ± 0.137 mmol/l, P metabolic parameters. The sympathoinhibition associated with weight loss involves marked changes, not only in the rate but also in the firing pattern of active vasoconstrictive fibers.

  19. Goal-Directed Modulation of Neural Memory Patterns: Implications for fMRI-Based Memory Detection.

    Science.gov (United States)

    Uncapher, Melina R; Boyd-Meredith, J Tyler; Chow, Tiffany E; Rissman, Jesse; Wagner, Anthony D

    2015-06-03

    Remembering a past event elicits distributed neural patterns that can be distinguished from patterns elicited when encountering novel information. These differing patterns can be decoded with relatively high diagnostic accuracy for individual memories using multivoxel pattern analysis (MVPA) of fMRI data. Brain-based memory detection--if valid and reliable--would have clear utility beyond the domain of cognitive neuroscience, in the realm of law, marketing, and beyond. However, a significant boundary condition on memory decoding validity may be the deployment of "countermeasures": strategies used to mask memory signals. Here we tested the vulnerability of fMRI-based memory detection to countermeasures, using a paradigm that bears resemblance to eyewitness identification. Participants were scanned while performing two tasks on previously studied and novel faces: (1) a standard recognition memory task; and (2) a task wherein they attempted to conceal their true memory state. Univariate analyses revealed that participants were able to strategically modulate neural responses, averaged across trials, in regions implicated in memory retrieval, including the hippocampus and angular gyrus. Moreover, regions associated with goal-directed shifts of attention and thought substitution supported memory concealment, and those associated with memory generation supported novelty concealment. Critically, whereas MVPA enabled reliable classification of memory states when participants reported memory truthfully, the ability to decode memory on individual trials was compromised, even reversing, during attempts to conceal memory. Together, these findings demonstrate that strategic goal states can be deployed to mask memory-related neural patterns and foil memory decoding technology, placing a significant boundary condition on their real-world utility. Copyright © 2015 the authors 0270-6474/15/358531-15$15.00/0.

  20. Cross-Coupled Eye Movement Supports Neural Origin of Pattern Strabismus

    Science.gov (United States)

    Ghasia, Fatema F.; Shaikh, Aasef G.; Jacobs, Jonathan; Walker, Mark F.

    2015-01-01

    Purpose. Pattern strabismus describes vertically incomitant horizontal strabismus. Conventional theories emphasized the role of orbital etiologies, such as abnormal fundus torsion and misaligned orbital pulleys as a cause of the pattern strabismus. Experiments in animal models, however, suggested the role of abnormal cross-connections between the neural circuits. We quantitatively assessed eye movements in patients with pattern strabismus with a goal to delineate the role of neural circuits versus orbital etiologies. Methods. We measured saccadic eye movements with high-precision video-oculography in 14 subjects with pattern strabismus, 5 with comitant strabismus, and 15 healthy controls. We assessed change in eye position in the direction orthogonal to that of the desired eye movement (cross-coupled responses). We used fundus photography to quantify the fundus torsion. Results. We found cross-coupling of saccades in all patients with pattern strabismus. The cross-coupled responses were in the same direction in both eyes, but larger in the nonviewing eye. All patients had clinically apparent inferior oblique overaction with abnormal excylotorsion. There was no correlation between the amount of the fundus torsion or the grade of oblique overaction and the severity of cross-coupling. The disconjugacy in the saccade direction and amplitude in pattern strabismics did not have characteristics predicted by clinically apparent inferior oblique overaction. Conclusions. Our results validated primate models of pattern strabismus in human patients. We found no correlation between ocular torsion or oblique overaction and cross-coupling. Therefore, we could not ascribe cross-coupling exclusively to the orbital etiology. Patients with pattern strabismus could have abnormalities in the saccade generators. PMID:26024072

  1. Automated target recognition and tracking using an optical pattern recognition neural network

    Science.gov (United States)

    Chao, Tien-Hsin

    1991-01-01

    The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

  2. Overlapping patterns of neural activity for different forms of novelty in fMRI

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    Colin Shaun Hawco

    2014-09-01

    Full Text Available When stimuli are presented multiple times, the neural response to repeated stimuli is reduced relative to novel stimuli (repetition suppression. Responses to different types of novelty were examined. Stimulus novelty was examined by contrasting first vs. second presentation of triads of objects during memory encoding. Semantic novelty was contrasted by comparing unrelated (semantically novel triads of objects to triads in which all three objects were related (e.g. all objects were tools. In recognition, associative novelty was examined by contrasting rearranged triads (previously seen objects in a new association with intact triads. Activity was observed in posterior regions (occipital and fusiform, with the largest extent of activity for stimulus novelty and smallest for associational novelty. Frontal activity was also observed in stimulus and semantic novelty. Additional analysis indicated that the hemodynamic response in voxels identified in the stimulus and semantic novelty contrasts was modulated by reaction time on a trial-by-trial basis. That is, the duration of the hemodynamic response was driven by reaction time. This was not the case for associative novelty. The high level of overlap across different forms of novelty suggests a similar mechanism for reduced neural activity, which may be related to reduced visual processing time. This is consistent with a facilitation model of repetition suppression, which posits a reduced peak and duration of neuronal firing for repeated stimuli.

  3. Adaptation in the visual cortex: influence of membrane trajectory and neuronal firing pattern on slow afterpotentials.

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    Vanessa F Descalzo

    Full Text Available The input/output relationship in primary visual cortex neurons is influenced by the history of the preceding activity. To understand the impact that membrane potential trajectory and firing pattern has on the activation of slow conductances in cortical neurons we compared the afterpotentials that followed responses to different stimuli evoking similar numbers of action potentials. In particular, we compared afterpotentials following the intracellular injection of either square or sinusoidal currents lasting 20 seconds. Both stimuli were intracellular surrogates of different neuronal responses to prolonged visual stimulation. Recordings from 99 neurons in slices of visual cortex revealed that for stimuli evoking an equivalent number of spikes, sinusoidal current injection activated a slow afterhyperpolarization of significantly larger amplitude (8.5 ± 3.3 mV and duration (33 ± 17 s than that evoked by a square pulse (6.4 ± 3.7 mV, 28 ± 17 s; p<0.05. Spike frequency adaptation had a faster time course and was larger during plateau (square pulse than during intermittent (sinusoidal depolarizations. Similar results were obtained in 17 neurons intracellularly recorded from the visual cortex in vivo. The differences in the afterpotentials evoked with both protocols were abolished by removing calcium from the extracellular medium or by application of the L-type calcium channel blocker nifedipine, suggesting that the activation of a calcium-dependent current is at the base of this afterpotential difference. These findings suggest that not only the spikes, but the membrane potential values and firing patterns evoked by a particular stimulation protocol determine the responses to any subsequent incoming input in a time window that spans for tens of seconds to even minutes.

  4. Circular antenna array pattern analysis using radial basis function neural network

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    Rama Sanjeeva Reddy, B.; Vakula, D.; Sarma, N. V. S. N.

    2013-04-01

    A method is proposed to design circular antenna array for the given gain and beam width using Artificial Neural Networks. In optimizing circular arrays, the parameters to be controlled are excitation of the elements, their separation, lengths and the circle radius. This paper deals about finding the parameters of radiation pattern of given uniform circular antenna array. Initially, the network is trained with a set of input-output data pairs. The trained network is used for testing. The training data set is generated from MATLAB simulation with number of elements N=5, 10, 15 and 20 elements of uniform circular array, respectively, distributed over a given circle, assuming 20 training cases. The number of input nodes, hidden nodes and output nodes are 20, 20 and 1, respectively. Predicted values of the neural network are compared with those of MATLAB simulation results and are found to be in agreement. This work establishes the application of Radial Basis Function Neural Network (RBFNN) for circular array pattern optimization. RBFNN is able to predict the output values with 97% of accuracy. This work proves that RBFNN can be used for circular antenna array design.

  5. Patterns of neural activity predict picture-naming performance of a patient with chronic aphasia.

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    Lee, Yune Sang; Zreik, Jihad T; Hamilton, Roy H

    2017-01-08

    Naming objects represents a substantial challenge for patients with chronic aphasia. This could be in part because the reorganized compensatory language networks of persons with aphasia may be less stable than the intact language systems of healthy individuals. Here, we hypothesized that the degree of stability would be instantiated by spatially differential neural patterns rather than either increased or diminished amplitudes of neural activity within a putative compensatory language system. We recruited a chronic aphasic patient (KL; 66 year-old male) who exhibited a semantic deficit (e.g., often said "milk" for "cow" and "pillow" for "blanket"). Over the course of four behavioral sessions involving a naming task performed in a mock scanner, we identified visual objects that yielded an approximately 50% success rate. We then conducted two fMRI sessions in which the patient performed a naming task for multiple exemplars of those objects. Multivoxel pattern analysis (MVPA) searchlight revealed differential activity patterns associated with correct and incorrect trials throughout intact brain regions. The most robust and largest cluster was found in the right occipito-temporal cortex encompassing fusiform cortex, lateral occipital cortex (LOC), and middle occipital cortex, which may account for the patient's propensity for semantic naming errors. None of these areas were found by a conventional univariate analysis. By using an alternative approach, we extend current evidence for compensatory naming processes that operate through spatially differential patterns within the reorganized language system. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator.

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    Hoellinger, Thomas; Petieau, Mathieu; Duvinage, Matthieu; Castermans, Thierry; Seetharaman, Karthik; Cebolla, Ana-Maria; Bengoetxea, Ana; Ivanenko, Yuri; Dan, Bernard; Cheron, Guy

    2013-01-01

    The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum, or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996) was recently modeled (Barliya et al., 2009) by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator (CPG) processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN) mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank, and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.

  7. Comparison of the sensitivity of landscape-fire-succession models to variation in terrain, fuel pattern, climate and weather.

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    Geoffrey J. Cary; Robert E. Keane; Robert H. Gardner; Sandra Lavorel; Mike D. Flannigan; Ian D. Davies; Chao Li; James M. Lenihan; T. Scott Rupp; Florent. Mouillot

    2006-01-01

    The purpose of this study was to compare the sensitivity of nlodelled area burned to environmental factors across a range of independently-developed landscape-fire-succession models. The sensitivity of area burned to variation in four factors, namely terrain (flat, undulating and mountainous), fuel pattern (finely and coarsely clumped), climate (observed, warmer &...

  8. Post-fire tree establishment patterns at the alpine treeline ecotone: Mount Rainier National Park, Washington, USA

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    Kirk M. Stueve; Dawna L. Cerney; Regina M. Rochefort; Laurie L. Kurth

    2009-01-01

    Questions: Does tree establishment: (1) occur at a treeline depressed by fire, (2) cause the forest line to ascend upslope, and/or (3) alter landscape heterogeneity? (4) What abiotic and biotic local site conditions are most important in structuring establishment patterns? (5) Does the abiotic setting become more important with increasing upslope distance from the...

  9. Fire patterns of South Eastern Queensland in a global context: A review

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    Philip Le C. F. Stewart; Patrick T. Moss

    2015-01-01

    Fire is an important driver in ecosystem evolution, composition, structure and distribution, and is vital for maintaining ecosystems of the Great Sandy Region (GSR). Charcoal records for the area dating back over 40, 000 years provide evidence of the great changes in vegetation composition, distribution and abundance in the region over time as a result of fire. Fires...

  10. Acute stress evokes sexually dimorphic, stressor-specific patterns of neural activation across multiple limbic brain regions in adult rats.

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    Sood, Ankit; Chaudhari, Karina; Vaidya, Vidita A

    2018-03-01

    Stress enhances the risk for psychiatric disorders such as anxiety and depression. Stress responses vary across sex and may underlie the heightened vulnerability to psychopathology in females. Here, we examined the influence of acute immobilization stress (AIS) and a two-day short-term forced swim stress (FS) on neural activation in multiple cortical and subcortical brain regions, implicated as targets of stress and in the regulation of neuroendocrine stress responses, in male and female rats using Fos as a neural activity marker. AIS evoked a sex-dependent pattern of neural activation within the cingulate and infralimbic subdivisions of the medial prefrontal cortex (mPFC), lateral septum (LS), habenula, and hippocampal subfields. The degree of neural activation in the mPFC, LS, and habenula was higher in males. Female rats exhibited reduced Fos positive cell numbers in the dentate gyrus hippocampal subfield, an effect not observed in males. We addressed whether the sexually dimorphic neural activation pattern noted following AIS was also observed with the short-term stress of FS. In the paraventricular nucleus of the hypothalamus and the amygdala, FS similar to AIS resulted in robust increases in neural activation in both sexes. The pattern of neural activation evoked by FS was distinct across sexes, with a heightened neural activation noted in the prelimbic mPFC subdivision and hippocampal subfields in females and differed from the pattern noted with AIS. This indicates that the sex differences in neural activation patterns observed within stress-responsive brain regions are dependent on the nature of stressor experience.

  11. Wildfire and Spatial Patterns in Forests in Northwestern Mexico: The United States Wishes It Had Similar Fire Problems

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    Scott L. Stephens

    2008-12-01

    Full Text Available Knowledge of the ecological effect of wildfire is important to resource managers, especially from forests in which past anthropogenic influences, e.g., fire suppression and timber harvesting, have been limited. Changes to forest structure and regeneration patterns were documented in a relatively unique old-growth Jeffrey pine-mixed conifer forest in northwestern Mexico after a July 2003 wildfire. This forested area has never been harvested and fire suppression did not begin until the 1970s. Fire effects were moderate especially considering that the wildfire occurred at the end of a severe, multi-year (1999-2003 drought. Shrub consumption was an important factor in tree mortality and the dominance of Jeffrey pine increased after fire. The Baja California wildfire enhanced or maintained a patchy forest structure; similar spatial heterogeneity should be included in US forest restoration plans. Most US forest restoration plans include thinning from below to separate tree crowns and attain a narrow range for residual basal area/ha. This essentially produces uniform forest conditions over broad areas that are in strong contrast to the resilient forests in northern Baja California. In addition to producing more spatial heterogeneity in restoration plans of forests that once experienced frequent, low-moderate intensity fire regimes, increased use of US wildfire management options such as wildland fire use as well as appropriate management responses to non-natural ignitions could also be implemented at broader spatial scales to increase the amount of burning in western US forests.

  12. Firing Rate Estimation Using Infinite Mixture Models and Its Application to Neural Decoding.

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    Shibue, Ryohei; Komaki, Fumiyasu

    2017-08-09

    Neural decoding is a framework for reconstructing external stimuli from spike trains recorded in brains. Kloosterman et al. (2014) proposed a new decoding method using marked point processes. This method does not require spike sorting and thereby improves decoding accuracy dramatically. In this method, they used kernel density estimation to estimate intensity functions of marked point processes. However, using kernel density estimation causes problems. To overcome these problems, we propose a new decoding method using infinite mixture models to estimate intensity. The proposed method improves decoding performance in terms of accuracy and computation speed. We apply the proposed method to simulation and experimental data to verify its performance. Copyright © 2016, Journal of Neurophysiology.

  13. Organization of anti-phase synchronization pattern in neural networks: what are the key factors?

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    Dong eLi

    2011-12-01

    Full Text Available Anti-phase oscillation has been widely observed in cortical neuralnetwork. Elucidating the mechanism underlying the organization ofanti-phase pattern is of significance for better understanding morecomplicated pattern formations in brain networks. In dynamicalsystems theory, the organization of anti-phase oscillation patternhas usually been considered to relate to time-delay in coupling.This is consistent to conduction delays in real neural networks inthe brain due to finite propagation velocity of action potentials.However, other structural factors in cortical neural network, suchas modular organization (connection density and the coupling types(excitatory or inhibitory, could also play an important role. Inthis work, we investigate the anti-phase oscillation patternorganized on a two-module network of either neuronal cell model orneural mass model, and analyze the impact of the conduction delaytimes, the connection densities, and coupling types. Our resultsshow that delay times and coupling types can play key roles in thisorganization. The connection densities may have an influence on thestability if an anti-phase pattern exists due to the other factors.Furthermore, we show that anti-phase synchronization of slowoscillations can be achieved with small delay times if there isinteraction between slow and fast oscillations. These results aresignificant for further understanding more realistic spatiotemporaldynamics of cortico-cortical communications.

  14. 3D reconstitution of the patterned neural tube from embryonic stem cells.

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    Meinhardt, Andrea; Eberle, Dominic; Tazaki, Akira; Ranga, Adrian; Niesche, Marco; Wilsch-Bräuninger, Michaela; Stec, Agnieszka; Schackert, Gabriele; Lutolf, Matthias; Tanaka, Elly M

    2014-12-09

    Inducing organogenesis in 3D culture is an important aspect of stem cell research. Anterior neural structures have been produced from large embryonic stem cell (ESC) aggregates, but the steps involved in patterning such complex structures have been ill defined, as embryoid bodies typically contained many cell types. Here we show that single mouse ESCs directly embedded in Matrigel or defined synthetic matrices under neural induction conditions can clonally form neuroepithelial cysts containing a single lumen in 3D. Untreated cysts were uniformly dorsal and could be ventralized to floor plate (FP). Retinoic acid posteriorized cysts to cervical levels and induced localize FP formation yielding full patterning along the dorsal/ventral (DV) axis. Correct spatial organization of motor neurons, interneurons, and dorsal interneurons along the DV axis was observed. This system serves as a valuable tool for studying morphogen action in 3D and as a source of patterned spinal cord tissue. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  15. Relationships between dendritic morphology, spatial distribution and firing patterns in rat layer 1 neurons

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    D.V.V. Santos

    2012-12-01

    Full Text Available The cortical layer 1 contains mainly small interneurons, which have traditionally been classified according to their axonal morphology. The dendritic morphology of these cells, however, has received little attention and remains ill defined. Very little is known about how the dendritic morphology and spatial distribution of these cells may relate to functional neuronal properties. We used biocytin labeling and whole cell patch clamp recordings, associated with digital reconstruction and quantitative morphological analysis, to assess correlations between dendritic morphology, spatial distribution and membrane properties of rat layer 1 neurons. A total of 106 cells were recorded, labeled and subjected to morphological analysis. Based on the quantitative patterns of their dendritic arbor, cells were divided into four major morphotypes: horizontal, radial, ascendant, and descendant cells. Descendant cells exhibited a highly distinct spatial distribution in relation to other morphotypes, suggesting that they may have a distinct function in these cortical circuits. A significant difference was also found in the distribution of firing patterns between each morphotype and between the neuronal populations of each sublayer. Passive membrane properties were, however, statistically homogeneous among all subgroups. We speculate that the differences observed in active membrane properties might be related to differences in the synaptic input of specific types of afferent fibers and to differences in the computational roles of each morphotype in layer 1 circuits. Our findings provide new insights into dendritic morphology and neuronal spatial distribution in layer 1 circuits, indicating that variations in these properties may be correlated with distinct physiological functions.

  16. Wireless hippocampal neural recording via a multiple input RF receiver to construct place-specific firing fields.

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    Lee, Seung Bae; Manns, Joseph R; Ghovanloo, Maysam

    2012-01-01

    This paper reports scientifically meaningful in vivo experiments using a 32-channel wireless neural recording system (WINeR). The WINeR system is divided into transmitter (Tx) and receiver (Rx) parts. On the Tx side, we had WINeR-6, a system-on-a-chip (SoC) that operated based on time division multiplexing (TDM) of pulse width modulated (PWM) samples. The chip was fabricated in a 0.5-µm CMOS process, occupying 4.9 × 3.3 mm(2) and consuming 15 mW from ±1.5V supplies. The Rx used two antennas with separate pathways to down-convert the RF signal from a large area. A time-to-digital converter (TDC) in an FPGA converted the PWM pulses into digitized samples. In order to further increase the wireless coverage area and eliminate blind spots within a large experimental arena, two receivers were synchronized. The WINeR system was used to record epileptic activities from a rat that was injected with tetanus toxin (TT) in the dorsal hippocampus. In a different in vivo experiment, place-specific firing fields of place cells, which are parts of the hippocampal-dependent memory, were mapped from a series of behavioral experiments from a rat running in a circular track. Results from the same animal were compared against a commercial hard-wired recording system to evaluate the quality of the wireless recordings.

  17. The Effects of Topographical Patterns and Sizes on Neural Stem Cell Behavior

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    Qi, Lin; Li, Ning; Huang, Rong; Song, Qin; Wang, Long; Zhang, Qi; Su, Ruigong; Kong, Tao; Tang, Mingliang; Cheng, Guosheng

    2013-01-01

    Engineered topographical manipulation, a paralleling approach with conventional biochemical cues, has recently attracted the growing interests in utilizations to control stem cell fate. In this study, effects of topological parameters, pattern and size are emphasized on the proliferation and differentiation of adult neural stem cells (ANSCs). We fabricate micro-scale topographical Si wafers with two different feature sizes. These topographical patterns present linear micro-pattern (LMP), circular micro-pattern (CMP) and dot micro-pattern (DMP). The results show that the three topography substrates are suitable for ANSC growth, while they all depress ANSC proliferation when compared to non-patterned substrates (control). Meanwhile, LMP and CMP with two feature sizes can both significantly enhance ANSC differentiation to neurons compared to control. The smaller the feature size is, the better upregulation applies to ANSC for the differentiated neurons. The underlying mechanisms of topography-enhanced neuronal differentiation are further revealed by directing suppression of mitogen-activated protein kinase/extracellular signaling-regulated kinase (MAPK/Erk) signaling pathway in ANSC using U0126, known to inhibit the activation of Erk. The statistical results suggest MAPK/Erk pathway is partially involved in topography-induced differentiation. These observations provide a better understanding on the different roles of topographical cues on stem cell behavior, especially on the selective differentiation, and facilitate to advance the field of stem cell therapy. PMID:23527077

  18. Neural patterning of human induced pluripotent stem cells in 3-D cultures for studying biomolecule-directed differential cellular responses.

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    Yan, Yuanwei; Bejoy, Julie; Xia, Junfei; Guan, Jingjiao; Zhou, Yi; Li, Yan

    2016-09-15

    Appropriate neural patterning of human induced pluripotent stem cells (hiPSCs) is critical to generate specific neural cells/tissues and even mini-brains that are physiologically relevant to model neurological diseases. However, the capacity of signaling factors that regulate 3-D neural tissue patterning in vitro and differential responses of the resulting neural populations to various biomolecules have not yet been fully understood. By tuning neural patterning of hiPSCs with small molecules targeting sonic hedgehog (SHH) signaling, this study generated different 3-D neuronal cultures that were mainly comprised of either cortical glutamatergic neurons or motor neurons. Abundant glutamatergic neurons were observed following the treatment with an antagonist of SHH signaling, cyclopamine, while Islet-1 and HB9-expressing motor neurons were enriched by an SHH agonist, purmorphamine. In neurons derived with different neural patterning factors, whole-cell patch clamp recordings showed similar voltage-gated Na(+)/K(+) currents, depolarization-evoked action potentials and spontaneous excitatory post-synaptic currents. Moreover, these different neuronal populations exhibited differential responses to three classes of biomolecules, including (1) matrix metalloproteinase inhibitors that affect extracellular matrix remodeling; (2) N-methyl-d-aspartate that induces general neurotoxicity; and (3) amyloid β (1-42) oligomers that cause neuronal subtype-specific neurotoxicity. This study should advance our understanding of hiPSC self-organization and neural tissue development and provide a transformative approach to establish 3-D models for neurological disease modeling and drug discovery. Appropriate neural patterning of human induced pluripotent stem cells (hiPSCs) is critical to generate specific neural cells, tissues and even mini-brains that are physiologically relevant to model neurological diseases. However, the capability of sonic hedgehog-related small molecules to tune

  19. Neural Conversion and Patterning of Human Pluripotent Stem Cells: A Developmental Perspective

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    Alexandra Zirra

    2016-01-01

    Full Text Available Since the reprogramming of adult human terminally differentiated somatic cells into induced pluripotent stem cells (hiPSCs became a reality in 2007, only eight years have passed. Yet over this relatively short period, myriad experiments have revolutionized previous stem cell dogmata. The tremendous promise of hiPSC technology for regenerative medicine has fuelled rising expectations from both the public and scientific communities alike. In order to effectively harness hiPSCs to uncover fundamental mechanisms of disease, it is imperative to first understand the developmental neurobiology underpinning their lineage restriction choices in order to predictably manipulate cell fate to desired derivatives. Significant progress in developmental biology provides an invaluable resource for rationalising directed differentiation of hiPSCs to cellular derivatives of the nervous system. In this paper we begin by reviewing core developmental concepts underlying neural induction in order to provide context for how such insights have guided reductionist in vitro models of neural conversion from hiPSCs. We then discuss early factors relevant in neural patterning, again drawing upon crucial knowledge gained from developmental neurobiological studies. We conclude by discussing open questions relating to these concepts and how their resolution might serve to strengthen the promise of pluripotent stem cells in regenerative medicine.

  20. Fuzzy logic and neural networks in artificial intelligence and pattern recognition

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    Sanchez, Elie

    1991-10-01

    With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

  1. Stability of Neural Firing in the Trigeminal Nuclei under Mechanical Whisker Stimulation

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    Valeri A. Makarov

    2010-01-01

    Full Text Available Sensory information handling is an essentially nonstationary process even under a periodic stimulation. We show how the time evolution of ridges in the wavelet spectrum of spike trains can be used for quantification of the dynamical stability of the neuronal responses to a stimulus. We employ this method to study neuronal responses in trigeminal nuclei of the rat provoked by tactile whisker stimulation. Neurons from principalis (Pr5 and interpolaris (Sp5i show the maximal stability at the intermediate (50 ms stimulus duration, whereas Sp5o cells “prefer” shorter (10 ms stimulation. We also show that neurons in all three nuclei can perform as stimulus frequency filters. The response stability of about 33% of cells exhibits low-pass frequency dynamics. About 57% of cells have band-pass dynamics with the optimal frequency at 5 Hz for Pr5 and Sp5i, and 4 Hz for Sp5o, and the remaining 10% show no prominent dependence on the stimulus frequency. This suggests that the neural coding scheme in trigeminal nuclei is not fixed, but instead it adapts to the stimulus characteristics.

  2. Comparison of chaparral regrowth patterns between Santa Ana wind-driven and non-Santa Ana fire areas

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    Rachels, Diane Helen

    Wildfires are a common occurrence in California shrublands and island forests. Fire has a fundamental role in maintaining the ecosystem functions in chaparral where fire intensity and severity play important roles in the regeneration of species. In San Diego, the Cedar Fire that occurred in the fall of 2003 was unique in that one side was burned with wildfire fueled by dry, strong easterly Santa Ana winds that later died down, burning the remainder of the area under a mild westerly wind, allowing fuel-fed conditions. The objective of this study was to understand the connection between vegetation type and structure and environmental response to extreme fire events by analyzing life form regrowth in chaparral communities from the Santa Ana wind driven, Santa Ana backing, and non-Santa Ana fire types. Environmental factors of slope angle, aspect, elevation and soils were investigated in an effort to isolate shrub regrowth patterns. Fire burn characteristics, anthropogenic disturbance, fire history, and moisture availability were also analyzed to identify additional factors that may have influenced shrub regrowth. Shrub extents before the fire and six year after the fire were examined per slope aspect, slope angle, elevation, and fire characteristic categories. The closed canopy and natural features of the chaparral environment make ground based mapping very difficult. Remote sensing data and methods can be very helpful to evaluate the health of the vegetation and condition of the watershed for flood, erosion, and fire control. This study used high spatial resolution aerial imagery and a machine learning algorithm with a spatial contextual classifier to map three different areas from within the Cedar Fire perimeter. Geographic information science (GIS), field mapping, and image interpretation methods were used to identify vegetation samples for the classification and accuracy assessment of the vegetation maps. Object-based image samples were selected for the classifier

  3. Fire history of the San Francisco East Bay region and implications for landscape patterns

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    Keeley, J.E.

    2005-01-01

    The San Francisco East Bay landscape is a rich mosaic of grasslands, shrublands and woodlands that is experiencing losses of grassland due to colonization by shrubs and succession towards woodland associations. The instability of these grasslands is apparently due to their disturbance-dependent nature coupled with 20th century changes in fire and grazing activity. This study uses fire history records to determine the potential for fire in this region and for evidence of changes in the second half of the 20th century that would account for shrubland expansion. This region has a largely anthropogenic fire regime with no lightning-ignited fires in most years. Fire suppression policy has not excluded fire from this region; however, it has been effective at maintaining roughly similar burning levels in the face of increasing anthropogenic fires, and effective at decreasing the size of fires. Fire frequency parallels increasing population growth until the latter part of the 20th century, when it reached a plateau. Fire does not appear to have been a major factor in the shrub colonization of grasslands, and cessation of grazing is a more likely immediate cause. Because grasslands are not under strong edaphic control, rather their distribution appears to be disturbance-dependent, and natural lightning ignitions are rare in the region, I hypothesize that, before the entrance of people into the region, grasslands were of limited extent. Native Americans played a major role in creation of grasslands through repeated burning and these disturbance-dependent grasslands were maintained by early European settlers through overstocking of these range lands with cattle and sheep. Twentieth century reduction in grazing, coupled with a lack of natural fires and effective suppression of anthropogenic fires, have acted in concert to favor shrubland expansion.

  4. Classification of epileptiform and wicket spike of EEG pattern using backpropagation neural network

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    Puspita, Juni Wijayanti; Jaya, Agus Indra; Gunadharma, Suryani

    2017-03-01

    Epilepsy is characterized by recurrent seizures that is resulted by permanent brain abnormalities. One of tools to support the diagnosis of epilepsy is Electroencephalograph (EEG), which describes the recording of brain electrical activity. Abnormal EEG patterns in epilepsy patients consist of Spike and Sharp waves. While both waves, there is a normal pattern that sometimes misinterpreted as epileptiform by electroenchepalographer (EEGer), namely Wicket Spike. The main difference of the three waves are on the time duration that related to the frequency. In this study, we proposed a method to classify a EEG wave into Sharp wave, Spike wave or Wicket spike group using Backpropagation Neural Network based on the frequency and amplitude of each wave. The results show that the proposed method can classifies the three group of waves with good accuracy.

  5. Neural correlates of pre-attentive processing of pattern deviance in professional musicians.

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    Habermeyer, Benedikt; Herdener, Marcus; Esposito, Fabrizio; Hilti, Caroline C; Klarhöfer, Markus; di Salle, Francesco; Wetzel, Stephan; Scheffler, Klaus; Cattapan-Ludewig, Katja; Seifritz, Erich

    2009-11-01

    Pre-attentive registration of aberrations in predictable sound patterns is attributed to the temporal cortex. However, electrophysiology suggests that frontal areas become more important when deviance complexity increases. To play an instrument in an ensemble, professional musicians have to rely on the ability to detect even slight deviances from expected musical patterns and therefore have highly trained aural skills. Here, we aimed to identify the neural correlates of experience-driven plasticity related to the processing of complex sound features. We used functional magnetic resonance imaging in combination with an event-related oddball paradigm and compared brain activity in professional musicians and non-musicians during pre-attentive processing of melodic contour variations. The melodic pattern consisted of a sequence of five tones each lasting 50 ms interrupted by silent interstimulus intervals of 50 ms. Compared to non-musicians, the professional musicians showed enhanced activity in the left middle and superior temporal gyri, the left inferior frontal gyrus and in the right ventromedial prefrontal cortex in response to pattern deviation. This differential brain activity pattern was correlated with behaviorally tested musical aptitude. Our results thus support an experience-related role of the left temporal cortex in fast melodic contour processing and suggest involvement of the prefrontal cortex.

  6. Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition.

    Science.gov (United States)

    Brunton, Bingni W; Johnson, Lise A; Ojemann, Jeffrey G; Kutz, J Nathan

    2016-01-30

    There is a broad need in neuroscience to understand and visualize large-scale recordings of neural activity, big data acquired by tens or hundreds of electrodes recording dynamic brain activity over minutes to hours. Such datasets are characterized by coherent patterns across both space and time, yet existing computational methods are typically restricted to analysis either in space or in time separately. Here we report the adaptation of dynamic mode decomposition (DMD), an algorithm originally developed for studying fluid physics, to large-scale neural recordings. DMD is a modal decomposition algorithm that describes high-dimensional dynamic data using coupled spatial-temporal modes. The algorithm is robust to variations in noise and subsampling rate; it scales easily to very large numbers of simultaneously acquired measurements. We first validate the DMD approach on sub-dural electrode array recordings from human subjects performing a known motor task. Next, we combine DMD with unsupervised clustering, developing a novel method to extract spindle networks during sleep. We uncovered several distinct sleep spindle networks identifiable by their stereotypical cortical distribution patterns, frequency, and duration. DMD is closely related to principal components analysis (PCA) and discrete Fourier transform (DFT). We may think of DMD as a rotation of the low-dimensional PCA space such that each basis vector has coherent dynamics. The resulting analysis combines key features of performing PCA in space and power spectral analysis in time, making it particularly suitable for analyzing large-scale neural recordings. Copyright © 2015 Elsevier B.V. All rights reserved.

  7. The Impact of Stimulation Induced Short Term Synaptic Plasticity on Firing Patterns in the Globus Pallidus of the Rat

    Directory of Open Access Journals (Sweden)

    Jenia eBugaysen

    2011-03-01

    Full Text Available Electrical stimulation in the globus pallidus (GP leads to complex modulations of neuronal activity in the stimulated nucleus. Multiple in-vivo studies have demonstrated the modulation of both firing rates and patterns during and immediately following the GP stimulation. Previous in-vitro studies, together with computational studies, have suggested the involvement of short-term synaptic plasticity (STP during the stimulation. The aim of the current study was to explore in-vitro the effects of STP on neuronal activity of GP neurons during local repetitive stimulation. We recorded synaptic potentials and assessed the modulations of spontaneous firing in a postsynaptic neuron in acute brain slices via a whole-cell pipette. Low-frequency repetitive stimulation locked the firing of the neuron to the stimulus. However, high-frequency repetitive stimulation in the GP generated a biphasic modulation of the firing frequency consisting of inhibitory and excitatory phases. Using blockers of synaptic transmission, we show that GABAergic synapses mediated the inhibitory and glutamatergic synapses the excitatory part of the response. Furthermore, we report that at high stimulation frequencies both types of synapses undergo short-term depression leading to a time dependent modulation of the neuronal firing. These findings indicate that STP modulates the dynamic responses of pallidal activity during electrical stimulation, and may contribute to a better understanding of the mechanism underlying deep brain stimulation (DBS like protocols.

  8. Acceleration of spiking neural network based pattern recognition on NVIDIA graphics processors.

    Science.gov (United States)

    Han, Bing; Taha, Tarek M

    2010-04-01

    There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and generally utilize more accurate neuron models, such as the Izhikevich and the Hodgkin-Huxley models, in favor of the more popular integrate and fire model. We examine the feasibility of using graphics processing units (GPUs) to accelerate a spiking neural network based character recognition network to enable such large scale systems. Two versions of the network utilizing the Izhikevich and Hodgkin-Huxley models are implemented. Three NVIDIA general-purpose (GP) GPU platforms are examined, including the GeForce 9800 GX2, the Tesla C1060, and the Tesla S1070. Our results show that the GPGPUs can provide significant speedup over conventional processors. In particular, the fastest GPGPU utilized, the Tesla S1070, provided a speedup of 5.6 and 84.4 over highly optimized implementations on the fastest central processing unit (CPU) tested, a quadcore 2.67 GHz Xeon processor, for the Izhikevich and the Hodgkin-Huxley models, respectively. The CPU implementation utilized all four cores and the vector data parallelism offered by the processor. The results indicate that GPUs are well suited for this application domain.

  9. Fire patterns in piñon and juniper land cover types in the Semiarid Western United States from 1984 through 2013

    Science.gov (United States)

    David I. Board; Jeanne C. Chambers; Richard F. Miller; Peter J. Weisberg

    2018-01-01

    Increases in area burned and fire size have been reported across a wide range of forest and shrubland types in the Western United States in recent decades, but little is known about potential changes in fire regimes of piñon and juniper land cover types. We evaluated spatio-temporal patterns of fire in piñon and juniper land cover types from the National Gap Analysis...

  10. Neural avalanches at the critical point between replay and non-replay of spatiotemporal patterns.

    Directory of Open Access Journals (Sweden)

    Silvia Scarpetta

    Full Text Available We model spontaneous cortical activity with a network of coupled spiking units, in which multiple spatio-temporal patterns are stored as dynamical attractors. We introduce an order parameter, which measures the overlap (similarity between the activity of the network and the stored patterns. We find that, depending on the excitability of the network, different working regimes are possible. For high excitability, the dynamical attractors are stable, and a collective activity that replays one of the stored patterns emerges spontaneously, while for low excitability, no replay is induced. Between these two regimes, there is a critical region in which the dynamical attractors are unstable, and intermittent short replays are induced by noise. At the critical spiking threshold, the order parameter goes from zero to one, and its fluctuations are maximized, as expected for a phase transition (and as observed in recent experimental results in the brain. Notably, in this critical region, the avalanche size and duration distributions follow power laws. Critical exponents are consistent with a scaling relationship observed recently in neural avalanches measurements. In conclusion, our simple model suggests that avalanche power laws in cortical spontaneous activity may be the effect of a network at the critical point between the replay and non-replay of spatio-temporal patterns.

  11. A Pattern Construction Scheme for Neural Network-Based Cognitive Communication

    Directory of Open Access Journals (Sweden)

    Ozgur Orcay

    2011-01-01

    Full Text Available Inefficient utilization of the frequency spectrum due to conventional regulatory limitations and physical performance limiting factors, mainly the Signal to Noise Ratio (SNR, are prominent restrictions in digital wireless communication. Pattern Based Communication System (PBCS is an adaptive and perceptual communication method based on a Cognitive Radio (CR approach. It intends an SNR oriented cognition mechanism in the physical layer for improvement of Link Spectral Efficiency (LSE. The key to this system is construction of optimal communication signals, which consist of encoded data in different pattern forms (waveforms depending on spectral availabilities. The signals distorted in the communication medium are recovered according to the pre-trained pattern glossary by the perceptual receiver. In this study, we have shown that it is possible to improve the bandwidth efficiency when largely uncorrelated signal patterns are chosen in order to form a glossary that represents symbols for different length data groups and the information can be recovered by the Artificial Neural Network (ANN in the receiver site.

  12. Fire in the Vegetation and Peatlands of Borneo, 1997-2007: Patterns, Drivers and Emissions from Biomass Burning

    Science.gov (United States)

    Spessa, Allan; Weber, Ulrich; Langner, Andreas; Siegert, Florian; Heil, Angelika

    2010-05-01

    The peatland forests of equatorial SE Asia cover over 20 Mha with most located in Indonesia. Indonesian peatlands are globally one of the largest near-surface reserves of terrestrial organic carbon, with peat deposits of up to 20m thick and an estimated carbon storage of 55-61 Gt. The destructive fires in Indonesia during the exceptionally strong drought of late 1997 and early 1998 mark some of the largest peak emissions events in recorded history of global fires. Past studies estimate that about 1Gt of carbon was released to the atmosphere from the Indonesian fires in 1997- equivalent to 14% of the average global annual fossil fuel emissions released during the 1990s. Previous studies have established a non-linear negative correlation between fires and antecedent rainfall in Borneo, with ENSO-driven droughts being identified as the main cause of below-average rainfall events over the past decade or so. However, while these studies suggest that this non-linear relationship is mediated by ignitions associated with land use and land cover change (LULCC), they have not demonstrated it. A clear link between fires and logging in Borneo has been reported, but this work was restricted to eastern Kalimantan and the period 1997-98. The relationship between fires, emissions, rainfall and LULCC across the island of Borneo therefore remains to be examined using available fine resolution data over a multi-year period. Using rainfall data, up-to-date peat maps and state-of-the art satellite sensor data to determine burnt area and deforestation patterns over the decade 1997-2007, we show at a pixel working resolution of 0.25 degrees the following: Burning across Borneo predominated in southern Kalimantan. Fire activity is negatively and non-linearly correlated to rainfall mainly in pixels that have undergone a significant reduction in forest cover, and that the bigger the reduction, the stronger the correlation. Such pixels occur overwhelmingly in southern Kalimantan. These

  13. Cross-Modal Decoding of Neural Patterns Associated with Working Memory: Evidence for Attention-Based Accounts of Working Memory.

    Science.gov (United States)

    Majerus, Steve; Cowan, Nelson; Péters, Frédéric; Van Calster, Laurens; Phillips, Christophe; Schrouff, Jessica

    2016-01-01

    Recent studies suggest common neural substrates involved in verbal and visual working memory (WM), interpreted as reflecting shared attention-based, short-term retention mechanisms. We used a machine-learning approach to determine more directly the extent to which common neural patterns characterize retention in verbal WM and visual WM. Verbal WM was assessed via a standard delayed probe recognition task for letter sequences of variable length. Visual WM was assessed via a visual array WM task involving the maintenance of variable amounts of visual information in the focus of attention. We trained a classifier to distinguish neural activation patterns associated with high- and low-visual WM load and tested the ability of this classifier to predict verbal WM load (high-low) from their associated neural activation patterns, and vice versa. We observed significant between-task prediction of load effects during WM maintenance, in posterior parietal and superior frontal regions of the dorsal attention network; in contrast, between-task prediction in sensory processing cortices was restricted to the encoding stage. Furthermore, between-task prediction of load effects was strongest in those participants presenting the highest capacity for the visual WM task. This study provides novel evidence for common, attention-based neural patterns supporting verbal and visual WM. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  14. Cross-Modal Decoding of Neural Patterns Associated with Working Memory: Evidence for Attention-Based Accounts of Working Memory

    Science.gov (United States)

    Majerus, Steve; Cowan, Nelson; Péters, Frédéric; Van Calster, Laurens; Phillips, Christophe; Schrouff, Jessica

    2016-01-01

    Recent studies suggest common neural substrates involved in verbal and visual working memory (WM), interpreted as reflecting shared attention-based, short-term retention mechanisms. We used a machine-learning approach to determine more directly the extent to which common neural patterns characterize retention in verbal WM and visual WM. Verbal WM was assessed via a standard delayed probe recognition task for letter sequences of variable length. Visual WM was assessed via a visual array WM task involving the maintenance of variable amounts of visual information in the focus of attention. We trained a classifier to distinguish neural activation patterns associated with high- and low-visual WM load and tested the ability of this classifier to predict verbal WM load (high–low) from their associated neural activation patterns, and vice versa. We observed significant between-task prediction of load effects during WM maintenance, in posterior parietal and superior frontal regions of the dorsal attention network; in contrast, between-task prediction in sensory processing cortices was restricted to the encoding stage. Furthermore, between-task prediction of load effects was strongest in those participants presenting the highest capacity for the visual WM task. This study provides novel evidence for common, attention-based neural patterns supporting verbal and visual WM. PMID:25146374

  15. Neural code alterations and abnormal time patterns in Parkinson’s disease

    Science.gov (United States)

    Andres, Daniela Sabrina; Cerquetti, Daniel; Merello, Marcelo

    2015-04-01

    Objective. The neural code used by the basal ganglia is a current question in neuroscience, relevant for the understanding of the pathophysiology of Parkinson’s disease. While a rate code is known to participate in the communication between the basal ganglia and the motor thalamus/cortex, different lines of evidence have also favored the presence of complex time patterns in the discharge of the basal ganglia. To gain insight into the way the basal ganglia code information, we studied the activity of the globus pallidus pars interna (GPi), an output node of the circuit. Approach. We implemented the 6-hydroxydopamine model of Parkinsonism in Sprague-Dawley rats, and recorded the spontaneous discharge of single GPi neurons, in head-restrained conditions at full alertness. Analyzing the temporal structure function, we looked for characteristic scales in the neuronal discharge of the GPi. Main results. At a low-scale, we observed the presence of dynamic processes, which allow the transmission of time patterns. Conversely, at a middle-scale, stochastic processes force the use of a rate code. Regarding the time patterns transmitted, we measured the word length and found that it is increased in Parkinson’s disease. Furthermore, it showed a positive correlation with the frequency of discharge, indicating that an exacerbation of this abnormal time pattern length can be expected, as the dopamine depletion progresses. Significance. We conclude that a rate code and a time pattern code can co-exist in the basal ganglia at different temporal scales. However, their normal balance is progressively altered and replaced by pathological time patterns in Parkinson’s disease.

  16. Patterns of Canopy and Surface Layer Consumption in a Boreal Forest Fire from Repeat Airborne Lidar

    Science.gov (United States)

    Alonzo, Michael; Morton, Douglas C.; Cook, Bruce D.; Andersen, Hans-Erik; Babcock, Chad; Pattison, Robert

    2017-01-01

    Fire in the boreal region is the dominant agent of forest disturbance with direct impacts on ecosystem structure, carbon cycling, and global climate. Global and biome-scale impacts are mediated by burn severity, measured as loss of forest canopy and consumption of the soil organic layer. To date, knowledge of the spatial variability in burn severity has been limited by sparse field sampling and moderate resolution satellite data. Here, we used pre- and post-fire airborne lidar data to directly estimate changes in canopy vertical structure and surface elevation for a 2005 boreal forest fire on Alaskas Kenai Peninsula. We found that both canopy and surface losses were strongly linked to pre-fire species composition and exhibited important fine-scale spatial variability at sub-30m resolution. The fractional reduction in canopy volume ranged from 0.61 in lowland black spruce stands to 0.27 in mixed white spruce and broad leaf forest. Residual structure largely reflects standing dead trees, highlighting the influence of pre-fire forest structure on delayed carbon losses from above ground biomass, post-fire albedo, and variability in understory light environments. Median loss of surface elevation was highest in lowland black spruce stands (0.18 m) but much lower in mixed stands (0.02 m), consistent with differences in pre-fire organic layer accumulation. Spatially continuous depth-of-burn estimates from repeat lidar measurements provide novel information to constrain carbon emissions from the surface organic layer and may inform related research on post-fire successional trajectories. Spectral measures of burn severity from Landsat were correlated with canopy (r = 0.76) and surface (r = -0.71) removal in black spruce stands but captured less of the spatial variability in fire effects for mixed stands (canopy r = 0.56, surface r = -0.26), underscoring the difficulty in capturing fire effects in heterogeneous boreal forest landscapes using proxy measures of burn severity

  17. Neural crest cells pattern the surface cephalic ectoderm during FEZ formation.

    Science.gov (United States)

    Hu, Diane; Marcucio, Ralph S

    2012-04-01

    Multiple fibroblast growth factor (Fgf) ligands are expressed in the forebrain and facial ectoderm, and vascular endothelial growth factor (VEGF) is expressed in the facial ectoderm. Both pathways activate the MAP kinase cascade and can be suppressed by SU5402. We placed a bead soaked in SU5402 into the brain after emigration of neural crest cells was complete. Within 24 hr we observed reduced pMEK and pERK staining that persisted for at least 48 hr. This was accompanied by significant apoptosis in the face. By day 15, the upper beaks were truncated. Molecular changes in the FNP were also apparent. Normally, Shh is expressed in the frontonasal ectodermal zone and controls patterned growth of the upper jaw. In treated embryos, Shh expression was reduced. Both the structural and molecular deficits were mitigated after transplantation of FNP-derived mesenchymal cells. Thus, mesenchymal cells actively participate in signaling interactions of the face, and the absence of neural crest cells in neurocristopathies may not be merely structural. Copyright © 2012 Wiley Periodicals, Inc.

  18. Neural crest cells pattern the surface cephalic ectoderm during FEZ formation

    Science.gov (United States)

    Hu, Diane; Marcucio, Ralph S.

    2012-01-01

    Multiple Fibroblast growth factor (Fgf) ligands are expressed in the forebrain and facial ectoderm, and Vascular Endothelial Growth Factor (VEGF) is expressed in the facial ectoderm. Both pathways activate the MAP kinase cascade and can be suppressed by SU5402. We placed a bead soaked in SU5402 into the brain after emigration of neural crest cells was complete. Within 24 hours we observed reduced pMEK and pERK staining that persisted for at least 48 hours. This was accompanied by significant apoptosis in the face. By day 15 the upper beaks were truncated. Molecular changes in the FNP were also apparent. Normally, Shh is expressed in the Frontonasal Ectodermal Zone and controls patterned growth of the upper jaw. In treated embryos Shh expression was reduced. Both the structural and molecular deficits were mitigated after transplantation of FNP-derived mesenchymal cells. Thus, mesenchymal cells actively participate in signaling interactions of the face, and the absence of neural crest cells in neurocristopathies may not be merely structural. PMID:22411554

  19. An Effective and Novel Neural Network Ensemble for Shift Pattern Detection in Control Charts

    Directory of Open Access Journals (Sweden)

    Mahmoud Barghash

    2015-01-01

    Full Text Available Pattern recognition in control charts is critical to make a balance between discovering faults as early as possible and reducing the number of false alarms. This work is devoted to designing a multistage neural network ensemble that achieves this balance which reduces rework and scrape without reducing productivity. The ensemble under focus is composed of a series of neural network stages and a series of decision points. Initially, this work compared using multidecision points and single-decision point on the performance of the ANN which showed that multidecision points are highly preferable to single-decision points. This work also tested the effect of population percentages on the ANN and used this to optimize the ANN’s performance. Also this work used optimized and nonoptimized ANNs in an ensemble and proved that using nonoptimized ANN may reduce the performance of the ensemble. The ensemble that used only optimized ANNs has improved performance over individual ANNs and three-sigma level rule. In that respect using the designed ensemble can help in reducing the number of false stops and increasing productivity. It also can be used to discover even small shifts in the mean as early as possible.

  20. Impacts of fire and fire surrogate treatments on ecosystem nitrogen storage patterns: similarities and differences between forests of eastern and western North America

    Science.gov (United States)

    R.E.J. Boerner; J. Huang; S.C. Hart

    2009-01-01

    The Fire and Fire Surrogates (FFS) network is composed of 12 forest sites that span the continental United States, all of which historically had frequent low-severity fire. The goal of the FFS study was to assess the efficacy of three management treatments (prescribed fire, mechanical thinning, and their combination) in reducing wildfire hazard and increasing ecosystem...

  1. Effect of the size of an artificial neural network used as pattern identifier

    Energy Technology Data Exchange (ETDEWEB)

    Reynoso V, M.R.; Vega C, J.J. [ININ, 52045 Ocoyoacac, Estado de Mexico (Mexico)

    2003-07-01

    A novel way to extract relevant parameters associated with the outgoing ions from nuclear reactions, obtained by digitizing the signals provided by a Bragg curve spectrometer (BCS) is presented. This allowed the implementation of a more thorough pulse-shape analysis. Due to the complexity of this task, it was required to take advantage of new and more powerful computational paradigms. This was fulfilled using a back-propagation artificial neural network (ANN) as a pattern identifier. Over training of ANNs is a common problem during the training stage. In the performance of the ANN there is a compromise between its size and the size of the training set. Here, this effect will be illustrated in relation to the problem of Bragg Curve (BC) identification. (Author)

  2. Pattern matching in high energy physics by using neural network and genetic algorithm

    CERN Document Server

    Castellano, M G; Bevilacqua, V; Nappi, E

    2000-01-01

    In this paper two different approaches to provide information from events by high energy physics experiments are shown. Usually the representations produced in such experiments are spot-composed and the classical algorithms to be needed for data analysis are time consuming. For this reason the possibility to speed up pattern recognition tasks by soft computing approach with parallel algorithms has been investigated. The first scheme shown in the following is a two-layer neural network with forward connections, the second one consists of an evolutionary algorithm with elitistic strategy and mutation and cross-over adaptive probability. Test results of these approaches have been carried out analysing a set of images produced by an optical ring imaging Cherenkov (RICH) detector at CERN. (10 refs).

  3. Electrocardiogram Pattern Recognition and Analysis Based on Artificial Neural Networks and Support Vector Machines: A Review

    Directory of Open Access Journals (Sweden)

    Mario Sansone

    2013-01-01

    Full Text Available Computer systems for Electrocardiogram (ECG analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units or in prompt detection of dangerous events (e.g., ventricular fibrillation. Together with clinical applications (arrhythmia detection and heart rate variability analysis, ECG is currently being investigated in biometrics (human identification, an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned.

  4. Image analysis of neural stem cell division patterns in the zebrafish brain.

    Science.gov (United States)

    Lupperger, Valerio; Buggenthin, Felix; Chapouton, Prisca; Marr, Carsten

    2017-11-10

    Proliferating stem cells in the adult body are the source of constant regeneration. In the brain, neural stem cells (NSCs) divide to maintain the stem cell population and generate neural progenitor cells that eventually replenish mature neurons and glial cells. How much spatial coordination of NSC division and differentiation is present in a functional brain is an open question. To quantify the patterns of stem cell divisions, one has to (i) identify the pool of NSCs that have the ability to divide, (ii) determine NSCs that divide within a given time window, and (iii) analyze the degree of spatial coordination. Here, we present a bioimage informatics pipeline that automatically identifies GFP expressing NSCs in three-dimensional image stacks of zebrafish brain from whole-mount preparations. We exploit the fact that NSCs in the zebrafish hemispheres are located on a two-dimensional surface and identify between 1,500 and 2,500 NSCs in six brain hemispheres. We then determine the position of dividing NSCs in the hemisphere by EdU incorporation into cells undergoing S-phase and calculate all pairwise NSC distances with three alternative metrics. Finally, we fit a probabilistic model to the observed spatial patterns that accounts for the non-homogeneous distribution of NSCs. We find a weak positive coordination between dividing NSCs irrespective of the metric and conclude that neither strong inhibitory nor strong attractive signals drive NSC divisions in the adult zebrafish brain. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

  5. Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.

    Science.gov (United States)

    Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque

    2017-01-01

    Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

  6. Development of a computational model on the neural activity patterns of a visual working memory in a hierarchical feedforward Network

    Science.gov (United States)

    An, Soyoung; Choi, Woochul; Paik, Se-Bum

    2015-11-01

    Understanding the mechanism of information processing in the human brain remains a unique challenge because the nonlinear interactions between the neurons in the network are extremely complex and because controlling every relevant parameter during an experiment is difficult. Therefore, a simulation using simplified computational models may be an effective approach. In the present study, we developed a general model of neural networks that can simulate nonlinear activity patterns in the hierarchical structure of a neural network system. To test our model, we first examined whether our simulation could match the previously-observed nonlinear features of neural activity patterns. Next, we performed a psychophysics experiment for a simple visual working memory task to evaluate whether the model could predict the performance of human subjects. Our studies show that the model is capable of reproducing the relationship between memory load and performance and may contribute, in part, to our understanding of how the structure of neural circuits can determine the nonlinear neural activity patterns in the human brain.

  7. Stability and plasticity in neural encoding of linguistically relevant pitch patterns.

    Science.gov (United States)

    Xie, Zilong; Reetzke, Rachel; Chandrasekaran, Bharath

    2017-03-01

    While lifelong language experience modulates subcortical encoding of pitch patterns, there is emerging evidence that short-term training introduced in adulthood also shapes subcortical pitch encoding. Here we use a cross-language design to examine the stability of language experience-dependent subcortical plasticity over multiple days. We then examine the extent to which behavioral relevance induced by sound-to-category training leads to plastic changes in subcortical pitch encoding in adulthood relative to adolescence, a period of ongoing maturation of subcortical and cortical auditory processing. Frequency-following responses (FFRs), which reflect phase-locked activity from subcortical neural ensembles, were elicited while participants passively listened to pitch patterns reflective of Mandarin tones. In experiment 1 , FFRs were recorded across three consecutive days from native Chinese-speaking ( n = 10) and English-speaking ( n = 10) adults. In experiment 2 , FFRs were recorded from native English-speaking adolescents ( n = 20) and adults ( n = 15) before, during, and immediately after a session of sound-to-category training, as well as a day after training ceased. Experiment 1 demonstrated the stability of language experience-dependent subcortical plasticity in pitch encoding across multiple days of passive exposure to linguistic pitch patterns. In contrast, experiment 2 revealed an enhancement in subcortical pitch encoding that emerged a day after the sound-to-category training, with some developmental differences observed. Taken together, these findings suggest that behavioral relevance is a critical component for the observation of plasticity in the subcortical encoding of pitch. NEW & NOTEWORTHY We examine the timescale of experience-dependent auditory plasticity to linguistically relevant pitch patterns. We find extreme stability in lifelong experience-dependent plasticity. We further demonstrate that subcortical function in adolescents and adults is

  8. The specific features and pattern of febrile infection-related epilepsy syndrome (FIRES) in children

    OpenAIRE

    L. V. Shalkevich; O. A. Lvova; Kudlach, A.I.; V. V. Komir

    2014-01-01

    The paper considers the etiology, pathogenesis, clinical presentations, diagnosis and treatment in children with febrile infection-related epilepsy syndrome (FIRES) and the aspects of identifying this disease as an individual nosological entity. It details a study of the possible etiological factors of FIRES, such as metabolic, genetic, and immunological disorders, aseptic inflammatory processes, as well as a search for a certain infectious agent by inoculations of different biological enviro...

  9. Fire alters patterns of genetic diversity among 3 lizard species in Florida Scrub habitat.

    Science.gov (United States)

    Schrey, Aaron W; Ashton, Kyle G; Heath, Stacy; McCoy, Earl D; Mushinsky, Henry R

    2011-01-01

    The Florida Sand Skink (Plestiodon reynoldsi), the Florida Scrub Lizard (Sceloporus woodi), and the Six-lined Racerunner (Aspidoscelis sexlineata) occur in the threatened and fire-maintained Florida scrub habitat. Fire may have different consequences to local genetic diversity of these species because they each have different microhabitat preference. We collected tissue samples of each species from 3 sites with different time-since-fire: Florida Sand Skink n = 73, Florida Scrub Lizard n = 70, and Six-lined Racerunner n = 66. We compared the effect of fire on genetic diversity at microsatellite loci for each species. We screened 8 loci for the Florida Sand Skink, 6 loci for the Florida Scrub Lizard, and 6 loci for the Six-lined Racerunner. We also tested 2 potential driving mechanisms for the observed change in genetic diversity, a metapopulation source/sink model and a local demographic model. Genetic diversity varied with fire history, and significant genetic differentiation occurred among sites. The Florida Scrub Lizard had highest genetic variation at more recently burned sites, whereas the Florida Sand Skink and the Six-lined Racerunner had highest genetic variation at less recently burned sites. Habitat preferences of the Florida Sand Skink and the Florida Scrub Lizard may explain their discordant results, and the Six-lined Racerunner may have a more complicated genetic response to fire or is acted on at a different geographic scale than we have investigated. Our results indicate that these species may respond to fire in a more complicated manner than predicted by our metapopulation model or local demographic model. Our results show that the population-level responses in genetic diversity to fire are species-specific mandating conservation management of habitat diversity through a mosaic of burn frequencies.

  10. Revisiting the Neural Basis of Acquired Amusia: Lesion Patterns and Structural Changes Underlying Amusia Recovery

    Directory of Open Access Journals (Sweden)

    Aleksi J. Sihvonen

    2017-07-01

    Full Text Available Although, acquired amusia is a common deficit following stroke, relatively little is still known about its precise neural basis, let alone to its recovery. Recently, we performed a voxel-based lesion-symptom mapping (VLSM and morphometry (VBM study which revealed a right lateralized lesion pattern, and longitudinal gray matter volume (GMV and white matter volume (WMV changes that were specifically associated with acquired amusia after stroke. In the present study, using a larger sample of stroke patients (N = 90, we aimed to replicate and extend the previous structural findings as well as to determine the lesion patterns and volumetric changes associated with amusia recovery. Structural MRIs were acquired at acute and 6-month post-stroke stages. Music perception was behaviorally assessed at acute and 3-month post-stroke stages using the Scale and Rhythm subtests of the Montreal Battery of Evaluation of Amusia (MBEA. Using these scores, the patients were classified as non-amusic, recovered amusic, and non-recovered amusic. The results of the acute stage VLSM analyses and the longitudinal VBM analyses converged to show that more severe and persistent (non-recovered amusia was associated with an extensive pattern of lesions and GMV/WMV decrease in right temporal, frontal, parietal, striatal, and limbic areas. In contrast, less severe and transient (recovered amusia was linked to lesions specifically in left inferior frontal gyrus as well as to a GMV decrease in right parietal areas. Separate continuous analyses of MBEA Scale and Rhythm scores showed extensively overlapping lesion pattern in right temporal, frontal, and subcortical structures as well as in the right insula. Interestingly, the recovered pitch amusia was related to smaller GMV decreases in the temporoparietal junction whereas the recovered rhythm amusia was associated to smaller GMV decreases in the inferior temporal pole. Overall, the results provide a more comprehensive picture of

  11. Patterns of canopy and surface layer consumption in a boreal forest fire from repeat airborne lidar

    Science.gov (United States)

    Alonzo, Michael; Morton, Douglas C.; Cook, Bruce D.; Andersen, Hans-Erik; Babcock, Chad; Pattison, Robert

    2017-05-01

    Fire in the boreal region is the dominant agent of forest disturbance with direct impacts on ecosystem structure, carbon cycling, and global climate. Global and biome-scale impacts are mediated by burn severity, measured as loss of forest canopy and consumption of the soil organic layer. To date, knowledge of the spatial variability in burn severity has been limited by sparse field sampling and moderate resolution satellite data. Here, we used pre- and post-fire airborne lidar data to directly estimate changes in canopy vertical structure and surface elevation for a 2005 boreal forest fire on Alaska’s Kenai Peninsula. We found that both canopy and surface losses were strongly linked to pre-fire species composition and exhibited important fine-scale spatial variability at sub-30 m resolution. The fractional reduction in canopy volume ranged from 0.61 in lowland black spruce stands to 0.27 in mixed white spruce and broadleaf forest. Residual structure largely reflects standing dead trees, highlighting the influence of pre-fire forest structure on delayed carbon losses from aboveground biomass, post-fire albedo, and variability in understory light environments. Median loss of surface elevation was highest in lowland black spruce stands (0.18 m) but much lower in mixed stands (0.02 m), consistent with differences in pre-fire organic layer accumulation. Spatially continuous depth-of-burn estimates from repeat lidar measurements provide novel information to constrain carbon emissions from the surface organic layer and may inform related research on post-fire successional trajectories. Spectral measures of burn severity from Landsat were correlated with canopy (r = 0.76) and surface (r = -0.71) removal in black spruce stands but captured less of the spatial variability in fire effects for mixed stands (canopy r = 0.56, surface r = -0.26), underscoring the difficulty in capturing fire effects in heterogeneous boreal forest landscapes using proxy measures of burn

  12. Global Characterization of Biomass-Burning Patterns using Satellite Measurements of Fire Radiative Energy

    Science.gov (United States)

    Ichoku, Charles; Giglio, Louis; Wooster, Martin J.; Remer, Lorraine A.

    2008-01-01

    Remote sensing is the most practical means of measuring energy release from large open-air biomass burning. Satellite measurement of fire radiative energy (FRE) release rate or power (FRP) enables distinction between fires of different strengths. Based on a 1-km resolution fire data acquired globally by the MODerate-resolution Imaging Spectro-radiometer (MODIS) sensor aboard the Terra and Aqua satellites from 2000 to 2006, instanteaneous FRP values ranged between 0.02 MW and 1866 MW, with global daily means ranging between 20 and 40 MW. Regionally, at the Aqua-MODIS afternoon overpass, the mean FRP values for Alaska, Western US, Western Australia, Quebec and the rest of Canada are significantly higher than these global means, with Quebec having the overall highest value of 85 MW. Analysis of regional mean FRP per unit area of land (FRP flux) shows that a peak fire season in certain regions, fires can be responsible for up to 0.2 W/m(sup 2) at peak time of day. Zambia has the highest regional monthly mean FRP flux of approximately 0.045 W/m(sup 2) at peak time of day and season, while the Middle East has the lowest value of approximately 0.0005 W/m(sup 2). A simple scheme based on FRP has been devised to classify fires into five categories, to facilitate fire rating by strength, similar to earthquakes and hurricanes. The scheme uses MODIS measurements of FRP at 1-km resolution as follows: catagory 1 (less than 100 MW), category 2 (100 to less than 500 MW), category 3 (500 to less than 1000 MW), category 4 (1000 to less than 1500 MW), catagory 5 (greater than or equal to 1500 MW). In most regions of the world, over 90% of fires fall into category 1, while only less than 1% fall into each of categories 3 to 5, although these proportions may differ significantly from day to day and by season. The frequency of occurence of the larger fires is region specific, and could not be explained by ecosystem type alone. Time-series analysis of the propertions of higher category

  13. Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

    Directory of Open Access Journals (Sweden)

    Liangji Zhou

    2017-01-01

    Full Text Available As a typical deep-learning model, Convolutional Neural Networks (CNNs can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases.

  14. Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis.

    Science.gov (United States)

    Christodoulidis, Stergios; Anthimopoulos, Marios; Ebner, Lukas; Christe, Andreas; Mougiakakou, Stavroula

    2017-01-01

    Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns, and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem. In this study, we present an improved method for training the proposed network by transferring knowledge from the similar domain of general texture classification. Six publicly available texture databases are used to pretrain networks with the proposed architecture, which are then fine-tuned on the lung tissue data. The resulting CNNs are combined in an ensemble and their fused knowledge is compressed back to a network with the original architecture. The proposed approach resulted in an absolute increase of about 2% in the performance of the proposed CNN. The results demonstrate the potential of transfer learning in the field of medical image analysis, indicate the textural nature of the problem and show that the method used for training a network can be as important as designing its architecture.

  15. Oscillatory neural network for pattern recognition: trajectory based classification and supervised learning.

    Science.gov (United States)

    Miller, Vonda H; Jansen, Ben H

    2008-12-01

    Computer algorithms that match human performance in recognizing written text or spoken conversation remain elusive. The reasons why the human brain far exceeds any existing recognition scheme to date in the ability to generalize and to extract invariant characteristics relevant to category matching are not clear. However, it has been postulated that the dynamic distribution of brain activity (spatiotemporal activation patterns) is the mechanism by which stimuli are encoded and matched to categories. This research focuses on supervised learning using a trajectory based distance metric for category discrimination in an oscillatory neural network model. Classification is accomplished using a trajectory based distance metric. Since the distance metric is differentiable, a supervised learning algorithm based on gradient descent is demonstrated. Classification of spatiotemporal frequency transitions and their relation to a priori assessed categories is shown along with the improved classification results after supervised training. The results indicate that this spatiotemporal representation of stimuli and the associated distance metric is useful for simple pattern recognition tasks and that supervised learning improves classification results.

  16. Pattern recognition in lithology classification: modeling using neural networks, self-organizing maps and genetic algorithms

    Science.gov (United States)

    Sahoo, Sasmita; Jha, Madan K.

    2017-03-01

    Effective characterization of lithology is vital for the conceptualization of complex aquifer systems, which is a prerequisite for the development of reliable groundwater-flow and contaminant-transport models. However, such information is often limited for most groundwater basins. This study explores the usefulness and potential of a hybrid soft-computing framework; a traditional artificial neural network with gradient descent-momentum training (ANN-GDM) and a traditional genetic algorithm (GA) based ANN (ANN-GA) approach were developed and compared with a novel hybrid self-organizing map (SOM) based ANN (SOM-ANN-GA) method for the prediction of lithology at a basin scale. This framework is demonstrated through a case study involving a complex multi-layered aquifer system in India, where well-log sites were clustered on the basis of sand-layer frequencies; within each cluster, subsurface layers were reclassified into four depth classes based on the maximum drilling depth. ANN models for each depth class were developed using each of the three approaches. Of the three, the hybrid SOM-ANN-GA models were able to recognize incomplete geologic pattern more reasonably, followed by ANN-GA and ANN-GDM models. It is concluded that the hybrid soft-computing framework can serve as a promising tool for characterizing lithology in groundwater basins with missing lithologic patterns.

  17. Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

    Science.gov (United States)

    Huo, Guanying

    2017-01-01

    As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

  18. Contrasting patterns of connectivity among endemic and widespread fire coral species ( Millepora spp.) in the tropical Southwestern Atlantic

    Science.gov (United States)

    de Souza, Júlia N.; Nunes, Flávia L. D.; Zilberberg, Carla; Sanchez, Juan A.; Migotto, Alvaro E.; Hoeksema, Bert W.; Serrano, Xaymara M.; Baker, Andrew C.; Lindner, Alberto

    2017-09-01

    Fire corals are the only branching corals in the South Atlantic and provide an important ecological role as habitat-builders in the region. With three endemic species ( Millepora brazilensis, M. nitida and M. laboreli) and one amphi-Atlantic species ( M. alcicornis), fire coral diversity in the Brazilian Province rivals that of the Caribbean Province. Phylogenetic relationships and patterns of population genetic structure and diversity were investigated in all four fire coral species occurring in the Brazilian Province to understand patterns of speciation and biogeography in the genus. A total of 273 colonies from the four species were collected from 17 locations spanning their geographic ranges. Sequences from the 16S ribosomal DNA (rDNA) were used to evaluate phylogenetic relationships. Patterns in genetic diversity and connectivity were inferred by measures of molecular diversity, analyses of molecular variance, pairwise differentiation, and by spatial analyses of molecular variance. Morphometrics of the endemic species M. braziliensis and M. nitida were evaluated by discriminant function analysis; macro-morphological characters were not sufficient to distinguish the two species. Genetic analyses showed that, although they are closely related, each species forms a well-supported clade. Furthermore, the endemic species characterized a distinct biogeographic barrier: M. braziliensis is restricted to the north of the São Francisco River, whereas M. nitida occurs only to the south. Millepora laboreli is restricted to a single location and has low genetic diversity. In contrast, the amphi-Atlantic species M. alcicornis shows high genetic connectivity within the Brazilian Province, and within the Caribbean Province (including Bermuda), despite low levels of gene flow between these populations and across the tropical Atlantic. These patterns reflect the importance of the Amazon-Orinoco Plume and the Mid-Atlantic Barrier as biogeographic barriers, and suggest that

  19. Comparison of the dynamics of neural interactions in integrate-and-fire networks with current-based and conductance-based synapses

    Directory of Open Access Journals (Sweden)

    Stefano eCavallari

    2014-03-01

    Full Text Available Models of networks of Leaky Integrate-and-Fire neurons (LIF are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single-neuron and neural population dynamics of conductance-based networks (COBN and current-based networks (CUBN of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity. However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-sensitive in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, COBN showed stronger neuronal population synchronization in the gamma band, and their spectral information about the network input was higher and spread over a broader range of frequencies. These results suggest that second order properties of network dynamics depend strongly on the choice of synaptic model.

  20. Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks.

    Science.gov (United States)

    Cavallari, Stefano; Panzeri, Stefano; Mazzoni, Alberto

    2014-01-01

    Models of networks of Leaky Integrate-and-Fire (LIF) neurons are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single neuron and neural population dynamics of conductance-based networks (COBNs) and current-based networks (CUBNs) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-modulated in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, the network activity of COBN showed stronger synchronization in the gamma band, and spectral information about the input higher and spread over a broader range of frequencies. These results suggest that the second order statistics of network dynamics depend strongly on the choice of synaptic model.

  1. Design of AN Intelligent Individual Evacuation Model for High Rise Building Fires Based on Neural Network Within the Scope of 3d GIS

    Science.gov (United States)

    Atila, U.; Karas, I. R.; Turan, M. K.; Rahman, A. A.

    2013-09-01

    One of the most dangerous disaster threatening the high rise and complex buildings of today's world including thousands of occupants inside is fire with no doubt. When we consider high population and the complexity of such buildings it is clear to see that performing a rapid and safe evacuation seems hard and human being does not have good memories in case of such disasters like world trade center 9/11. Therefore, it is very important to design knowledge based realtime interactive evacuation methods instead of classical strategies which lack of flexibility. This paper presents a 3D-GIS implementation which simulates the behaviour of an intelligent indoor pedestrian navigation model proposed for a self -evacuation of a person in case of fire. The model is based on Multilayer Perceptron (MLP) which is one of the most preferred artificial neural network architecture in classification and prediction problems. A sample fire scenario following through predefined instructions has been performed on 3D model of the Corporation Complex in Putrajaya (Malaysia) and the intelligent evacuation process has been realized within a proposed 3D-GIS based simulation.

  2. NMDA receptors control cue-outcome selectivity and plasticity of orbitofrontal firing patterns during associative stimulus-reward learning.

    Science.gov (United States)

    van Wingerden, Marijn; Vinck, Martin; Tijms, Vincent; Ferreira, Irene R S; Jonker, Allert J; Pennartz, Cyriel M A

    2012-11-21

    Neural activity in orbitofrontal cortex has been linked to flexible representations of stimulus-outcome associations. Such value representations are known to emerge with learning, but the neural mechanisms supporting this phenomenon are not well understood. Here, we provide evidence for a causal role for NMDA receptors (NMDARs) in mediating spike pattern discriminability, neural plasticity, and rhythmic synchronization in relation to evaluative stimulus processing and decision making. Using tetrodes, single-unit spike trains and local field potentials were recorded during local, unilateral perfusion of an NMDAR blocker in rat OFC. In the absence of behavioral effects, NMDAR blockade severely hampered outcome-selective spike pattern formation to olfactory cues, relative to control perfusions. Moreover, NMDAR blockade shifted local rhythmic synchronization to higher frequencies and degraded its linkage to stimulus-outcome selective coding. These results demonstrate the importance of NMDARs for cue-outcome associative coding in OFC during learning and illustrate how NMDAR blockade disrupts network dynamics. Copyright © 2012 Elsevier Inc. All rights reserved.

  3. Contemporary patterns of burn severity heterogeneity from fires in the Northwestern U.S.

    Science.gov (United States)

    R. Travis Belote

    2015-01-01

    Historically, frequent, low-severity fires maintained opengrown structure of dry ponderosa pine forests (Hessburg and Agee 2003). Thus, an open forest structure may be a reasonable template for ecological restoration in those particular forest types (Allen and others 2002). In contrast, setting goals for ecosystem management and restoration targets in the vast majority...

  4. Oak decline in the Boston Mountains, Arkansas, USA: Spatial and temporal patterns under two fire regimes

    Science.gov (United States)

    Martin A. Spetich; Hong S. He

    2008-01-01

    A spatially explicit forest succession and disturbance model is used to delineate the extent and dispersion of oak decline under two fire regimes over a 150-year period. The objectives of this study are to delineate potential current and future oak decline areas using species composition and age structure data in combination with ecological land types, and to...

  5. Spatial patterning of fuels and fire hazard across a central U.S. deciduous forest region

    Science.gov (United States)

    Michael C. Stambaugh; Daniel C. Dey; Richard P. Guyette; Hong S. He; Joseph M. Marschall

    2011-01-01

    Information describing spatial and temporal variability of forest fuel conditions is essential to assessing overall fire hazard and risk. Limited information exists describing spatial characteristics of fuels in the eastern deciduous forest region, particularly in dry oak-dominated regions that historically burned relatively frequently. From an extensive fuels survey...

  6. Current and future patterns of fire-induced forest degradation in Amazonia

    Science.gov (United States)

    De Faria, Bruno L.; Brando, Paulo M.; Macedo, Marcia N.; Panday, Prajjwal K.; Soares-Filho, Britaldo S.; Coe, Michael T.

    2017-09-01

    Amazon droughts directly increase forest flammability by reducing forest understory air and fuel moisture. Droughts also increase forest flammability indirectly by decreasing soil moisture, triggering leaf shedding, branch loss, and tree mortality—all of which contribute to increased fuel loads. These direct and indirect effects can cause widespread forest fires that reduce forest carbon stocks in the Amazon, with potentially important consequences for the global carbon cycle. These processes are expected to become more widespread, common, and intense as global climate changes, yet the mechanisms linking droughts, wildfires, and associated changes in carbon stocks remain poorly understood. Here, we expanded the capabilities of a dynamic forest carbon model to better represent (1) drought effects on carbon and fuel dynamics and (2) understory fire behavior and severity. We used the refined model to quantify changes in Pan-Amazon live carbon stocks as a function of the maximum climatological water deficit (MCWD) and fire intensity, under both historical and future climate conditions. We found that the 2005 and 2010 droughts increased potential fire intensity by 226 kW m‑1 and 494 kW m‑1, respectively. These increases were due primarily to increased understory dryness (109 kW m‑1 in 2005; 124 kW m‑1 in 2010) and altered forest structure (117 kW m‑1 in 2005; 370 kW m‑1 in 2010) effects. Combined, these historic droughts drove total simulated reductions in live carbon stocks of 0.016 (2005) and 0.027 (2010) PgC across the Amazon Basin. Projected increases in future fire intensity increased simulated carbon losses by up to 90% per unit area burned, compared with modern climate. Increased air temperature was the primary driver of changes in simulated future fire intensity, while reduced precipitation was secondary, particularly in the eastern portion of the Basin. Our results show that fire-drought interactions strongly affect live carbon stocks and that

  7. Striatal fast-spiking interneurons: from firing patterns to postsynaptic impact

    Directory of Open Access Journals (Sweden)

    Andreas eKlaus

    2011-07-01

    Full Text Available In the striatal microcircuit, fast-spiking (FS interneurons have an important role in mediating inhibition onto neighboring medium spiny (MS projection neurons. In this study, we combined computational modeling with in vitro and in vivo electrophysiological measurements to investigate FS cells in terms of their discharge properties and their synaptic efficacies onto MS neurons. In vivo firing of striatal FS interneurons is characterized by a high firing variability. It is not known, however, if this variability results from the input that FS cells receive, or if it is promoted by the stuttering spike behavior of these neurons. Both our model and measurements in vitro show that FS neurons that exhibit random stuttering discharge in response to steady depolarization, do not show the typical stuttering behavior when they receive fluctuating input. Importantly, our model predicts that electrically coupled FS cells show substantial spike synchronization only when they are in the stuttering regime. Therefore, together with the lack of synchronized firing of striatal FS interneurons that has been reported in vivo, these results suggest that neighboring FS neurons are not in the stuttering regime simultaneously and that in vivo FS firing variability is more likely determined by the input fluctuations. Furthermore, the variability in FS firing is translated to variability in the postsynaptic amplitudes in MS neurons due to the strong synaptic depression of the FS-to-MS synapse. Our results support the idea that these synapses operate over a wide range from strongly depressed to almost fully recovered. The strong inhibitory effects that FS cells can impose on their postsynaptic targets, and the fact that the FS-to-MS synapse model showed substantial depression over extended periods of time might indicate the importance of cooperative effects of multiple presynaptic FS interneurons and the precise orchestration of their activity.

  8. Sensory renal innervation: a kidney-specific firing activity due to a unique expression pattern of voltage-gated sodium channels?

    Science.gov (United States)

    Freisinger, Wolfgang; Schatz, Johannes; Ditting, Tilmann; Lampert, Angelika; Heinlein, Sonja; Lale, Nena; Schmieder, Roland; Veelken, Roland

    2013-03-01

    Sensory neurons with afferent axons from the kidney are extraordinary in their response to electrical stimulation. More than 50% exhibit a tonic firing pattern, i.e., sustained action potential firing throughout depolarizing, pointing to an increased excitability, whereas nonrenal neurons show mainly a phasic response, i.e., less than five action potentials. Here we investigated whether these peculiar firing characteristics of renal afferent neurons are due to differences in the expression of voltage-gated sodium channels (Navs). Dorsal root ganglion (DRG) neurons from rats (Th11-L2) were recorded by the current-clamp technique and distinguished as "tonic" or "phasic." In voltage-clamp recordings, Navs were characterized by their tetrodotoxoxin (TTX) sensitivity, and their molecular identity was revealed by RT-PCR. The firing pattern of 66 DRG neurons (41 renal and 25 nonrenal) was investigated. Renal neurons exhibited more often a tonic firing pattern (56.1 vs. 12%). Tonic neurons showed a more positive threshold (-21.75 ± 1.43 vs.-29.33 ± 1.63 mV; P sodium currents. Interestingly, mRNA expression of TTX-resistant sodium channels was significantly increased in renal, predominantly tonic, DRG neurons. Hence, under physiological conditions, renal sensory neurons exhibit predominantly a firing pattern associated with higher excitability. Our findings support that this is due to an increased expression and activation of TTX-resistant Navs.

  9. Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity

    Science.gov (United States)

    Bennett, James E. M.; Bair, Wyeth

    2015-01-01

    Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli. PMID:26308406

  10. Synthesis of neural networks for spatio-temporal spike pattern recognition and processing

    Directory of Open Access Journals (Sweden)

    Jonathan C Tapson

    2013-08-01

    Full Text Available The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks. The Neural Engineering Framework offers one such synthesis, but it is most effective for a spike rate representation of neural information, and it requires a large number of neurons to implement simple functions. We describe a neural network synthesis method that generates synaptic connectivity for neurons which process time-encoded neural signals, and which makes very sparse use of neurons. The method allows the user to specify – arbitrarily - neuronal characteristics such as axonal and dendritic delays, and synaptic transfer functions, and then solves for the optimal input-output relationship using computed dendritic weights. The method may be used for batch or online learning and has an extremely fast optimization process. We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times.

  11. Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network.

    Science.gov (United States)

    Anthimopoulos, Marios; Christodoulidis, Stergios; Ebner, Lukas; Christe, Andreas; Mougiakakou, Stavroula

    2016-05-01

    Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2 × 2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance ( ~ 85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.

  12. Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters

    Directory of Open Access Journals (Sweden)

    Amir LAKZIAN

    2010-09-01

    Full Text Available This paper presents the comparison of three different approaches to estimate soil water content at defined values of soil water potential based on selected parameters of soil solid phase. Forty different sampling locations in northeast of Iran were selected and undisturbed samples were taken to measure the water content at field capacity (FC, -33 kPa, and permanent wilting point (PWP, -1500 kPa. At each location solid particle of each sample including the percentage of sand, silt and clay were measured. Organic carbon percentage and soil texture were also determined for each soil sample at each location. Three different techniques including pattern recognition approach (k nearest neighbour, k-NN, Artificial Neural Network (ANN and pedotransfer functions (PTF were used to predict the soil water at each sampling location. Mean square deviation (MSD and its components, index of agreement (d, root mean square difference (RMSD and normalized RMSD (RMSDr were used to evaluate the performance of all the three approaches. Our results showed that k-NN and PTF performed better than ANN in prediction of water content at both FC and PWP matric potential. Various statistics criteria for simulation performance also indicated that between kNN and PTF, the former, predicted water content at PWP more accurate than PTF, however both approach showed a similar accuracy to predict water content at FC.

  13. Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network.

    Science.gov (United States)

    Zhai, Xiaolong; Jelfs, Beth; Chan, Rosa H M; Tin, Chung

    2017-01-01

    Hand movement classification based on surface electromyography (sEMG) pattern recognition is a promising approach for upper limb neuroprosthetic control. However, maintaining day-to-day performance is challenged by the non-stationary nature of sEMG in real-life operation. In this study, we propose a self-recalibrating classifier that can be automatically updated to maintain a stable performance over time without the need for user retraining. Our classifier is based on convolutional neural network (CNN) using short latency dimension-reduced sEMG spectrograms as inputs. The pretrained classifier is recalibrated routinely using a corrected version of the prediction results from recent testing sessions. Our proposed system was evaluated with the NinaPro database comprising of hand movement data of 40 intact and 11 amputee subjects. Our system was able to achieve ~10.18% (intact, 50 movement types) and ~2.99% (amputee, 10 movement types) increase in classification accuracy averaged over five testing sessions with respect to the unrecalibrated classifier. When compared with a support vector machine (SVM) classifier, our CNN-based system consistently showed higher absolute performance and larger improvement as well as more efficient training. These results suggest that the proposed system can be a useful tool to facilitate long-term adoption of prosthetics for amputees in real-life applications.

  14. Connectivity strategies for higher-order neural networks applied to pattern recognition

    Science.gov (United States)

    Spirkovska, Lilly; Reid, Max B.

    1990-01-01

    Different strategies for non-fully connected HONNs (higher-order neural networks) are discussed, showing that by using such strategies an input field of 128 x 128 pixels can be attained while still achieving in-plane rotation and translation-invariant recognition. These techniques allow HONNs to be used with the larger input scenes required for practical pattern-recognition applications. The number of interconnections that must be stored has been reduced by a factor of approximately 200,000 in a T/C case and about 2000 in a Space Shuttle/F-18 case by using regional connectivity. Third-order networks have been simulated using several connection strategies. The method found to work best is regional connectivity. The main advantages of this strategy are the following: (1) it considers features of various scales within the image and thus gets a better sample of what the image looks like; (2) it is invariant to shape-preserving geometric transformations, such as translation and rotation; (3) the connections are predetermined so that no extra computations are necessary during run time; and (4) it does not require any extra storage for recording which connections were formed.

  15. Textural Classification of Mammographic Parenchymal Patterns with the SONNET Selforganizing Neural Network

    Directory of Open Access Journals (Sweden)

    Daniel Howard

    2008-01-01

    Full Text Available In nationwide mammography screening, thousands of mammography examinations must be processed. Each consists of two standard views of each breast, and each mammogram must be visually examined by an experienced radiologist to assess it for any anomalies. The ability to detect an anomaly in mammographic texture is important to successful outcomes in mammography screening and, in this study, a large number of mammograms were digitized with a highly accurate scanner; and textural features were derived from the mammograms as input data to a SONNET selforganizing neural network. The paper discusses how SONNET was used to produce a taxonomic organization of the mammography archive in an unsupervised manner. This process is subject to certain choices of SONNET parameters, in these numerical experiments using the craniocaudal view, and typically produced O(10, for example, 39 mammogram classes, by analysis of features from O(103 mammogram images. The mammogram taxonomy captured typical subtleties to discriminate mammograms, and it is submitted that this may be exploited to aid the detection of mammographic anomalies, for example, by acting as a preprocessing stage to simplify the task for a computational detection scheme, or by ordering mammography examinations by mammogram taxonomic class prior to screening in order to encourage more successful visual examination during screening. The resulting taxonomy may help train screening radiologists and conceivably help to settle legal cases concerning a mammography screening examination because the taxonomy can reveal the frequency of mammographic patterns in a population.

  16. Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Xiaolong Zhai

    2017-07-01

    Full Text Available Hand movement classification based on surface electromyography (sEMG pattern recognition is a promising approach for upper limb neuroprosthetic control. However, maintaining day-to-day performance is challenged by the non-stationary nature of sEMG in real-life operation. In this study, we propose a self-recalibrating classifier that can be automatically updated to maintain a stable performance over time without the need for user retraining. Our classifier is based on convolutional neural network (CNN using short latency dimension-reduced sEMG spectrograms as inputs. The pretrained classifier is recalibrated routinely using a corrected version of the prediction results from recent testing sessions. Our proposed system was evaluated with the NinaPro database comprising of hand movement data of 40 intact and 11 amputee subjects. Our system was able to achieve ~10.18% (intact, 50 movement types and ~2.99% (amputee, 10 movement types increase in classification accuracy averaged over five testing sessions with respect to the unrecalibrated classifier. When compared with a support vector machine (SVM classifier, our CNN-based system consistently showed higher absolute performance and larger improvement as well as more efficient training. These results suggest that the proposed system can be a useful tool to facilitate long-term adoption of prosthetics for amputees in real-life applications.

  17. Minimum Constructive Back Propagation Neural Network Based on Fuzzy Logic for Pattern Recognition of Electronic Nose System

    Directory of Open Access Journals (Sweden)

    Radi Radi

    2011-08-01

    Full Text Available Constructive Back Propagation Neural Network (CBPNN is a kind of back propagation neural network trained with constructive algorithm. Training of CBPNN is mainly conducted by developing the network’s architecture which commonly done by adding a number of new neuron units on learning process. Training of the network usually implements fixed method to develop its structure gradually by adding new units constantly. Although this method is simple and able to create an adaptive network for data pattern complexity, but it is wasteful and inefficient for computing. New unit addition affects directly to the computational load of training, speed of convergence, and structure of the final neural network. While increases training load significantly, excessive addition of units also tends to generate a large size of final network. Moreover, addition pattern with small unit number tends to drop off the adaptability of the network and extends time of training. Therefore, there is important to design an adaptive structure development pattern for CBPNN in order to minimize computing load of training. This study proposes Fuzzy Logic (FL algorithm to manage and develop structure of CBPNN. FL method was implemented on two models of CBPNN, i.e. designed with one and two hidden layers, used to recognize aroma patterns on an electronic nose system. The results showed that this method is effective to be applied due to its capability to minimize time of training, to reduce load of computational learning, and generate small size of network.

  18. Using artificial bat sonar neural networks for complex pattern recognition: recognizing faces and the speed of a moving target.

    Science.gov (United States)

    Dror, I E; Florer, F L; Rios, D; Zagaeski, M

    1996-04-01

    Two sets of studies examined the viability of using bat-like sonar input for artificial neural networks in complex pattern recognition tasks. In the first set of studies, a sonar neural network was required to perform two face recognition tasks. In the first task, the network was trained to recognize different faces regardless of facial expressions. Following training, the network was tested on its ability to generalize and correctly recognize faces using echoes of novel facial expressions that were not included in the training set. The neural network was able to recognize novel echoes of faces almost perfectly (above 96% accuracy) when it was required to recognize up to five faces. In the second face recognition task, a sonar neural network was trained to recognize the sex of 16 faces (eight males and eight females). After training, the network was able to correctly recognize novel echoes of those faces as 'male' or as 'female' faces with accuracy levels of 88%. However, the network was not able to recognize novel faces as 'male' or 'female' faces. In the second set of studies, a sonar neural network was required to learn to recognize the speed of a target that was moving towards the viewer. During training, the target was presented in a variety of orientations, and the network's performance was evaluated when the target was presented in novel orientations that were not included in the training set. The different orientations dramatically affected the amplitude and the frequency composition of the echoes. The neural network was able to learn and recognize the speed of a moving target, and to generalize to new orientations of the target. However, the network was not able to generalize to new speeds that were not included in the training set. The potential and limitations of using bat-like sonar as input for artifical neural networks are discussed.

  19. Noise exposure alters long-term neural firing rates and synchrony in primary auditory and rostral belt cortices following bimodal stimulation.

    Science.gov (United States)

    Takacs, Joseph D; Forrest, Taylor J; Basura, Gregory J

    2017-12-01

    We previously demonstrated that bimodal stimulation (spinal trigeminal nucleus [Sp5] paired with best frequency tone) altered neural tone-evoked and spontaneous firing rates (SFRs) in primary auditory cortex (A1) 15 min after pairing in guinea pigs with and without noise-induced tinnitus. Neural responses were enhanced (+10 ms) or suppressed (0 ms) based on the bimodal pairing interval. Here we investigated whether bimodal stimulation leads to long-term (up to 2 h) changes in tone-evoked and SFRs and neural synchrony (correlate of tinnitus) and if the long-term bimodal effects are altered following noise exposure. To obviate the effects of permanent hearing loss on the results, firing rates and neural synchrony were measured three weeks following unilateral (left ear) noise exposure and a temporary threshold shift. Simultaneous extra-cellular single-unit recordings were made from contralateral (to noise) A1 and dorsal rostral belt (RB); an associative auditory cortical region thought to influence A1, before and after bimodal stimulation (pairing intervals of 0 ms; simultaneous Sp5-tone and +10 ms; Sp5 precedes tone). Sixty and 120 min after 0 ms pairing tone-evoked and SFRs were suppressed in sham A1; an effect only preserved 120 min following pairing in noise. Stimulation at +10 ms only affected SFRs 120 min after pairing in sham and noise-exposed A1. Within sham RB, pairing at 0 and +10 ms persistently suppressed tone-evoked and SFRs, while 0 ms pairing in noise markedly enhanced tone-evoked and SFRs up to 2 h. Together, these findings suggest that bimodal stimulation has long-lasting effects in A1 that also extend to the associative RB that is altered by noise and may have persistent implications for how noise damaged brains process multi-sensory information. Moreover, prior to bimodal stimulation, noise damage increased neural synchrony in A1, RB and between A1 and RB neurons. Bimodal stimulation led to persistent changes in neural synchrony in

  20. Wildland fire and climate variability impacts on annual streamflow in watersheds across the continental United States: Regional patterns and attribution analysis

    Science.gov (United States)

    Hallema, D. W.; Sun, G.; Caldwell, P. V.; Norman, S. P.; Cohen, E. C.; Liu, Y.; McNulty, S. G.

    2016-12-01

    The magnitude of wildland fire impacts on water resources varies regionally depending on fire severity, topography, vegetation and climate. An assessment of the potential threat that wildland fire poses to water supplies across the conterminous United States (CONUS) is critically important because forests supply 50% of consumed water. In our assessment, we first performed a double mass analysis of streamflow (GAGES-II) vs. precipitation (PRISM) data from 170 burned watersheds to identify changes in average water yield in the first five years following wildland fire (MTBS burn severity dataset), which were positive in 52 watersheds (Chow test p0.1). Subsequently, we separated the respective contributions of fire and climate variability to changes in annual runoff (dQ) by fitting linear climate elasticity models (CEMs), yielding acceptable CEMs (coefficient pstates with low severity prescribed (Rx) or wildfires. MdQ increased by +11% in 44 watersheds with BAR >10%, notwithstanding overall declining P. These watersheds were for the greatest part located in the western CONUS, where dQ was correlated with burn severity (R2>0.53, variable per severity class) and PET (R2=0.73). The most severe impacts were observed in Arizona (2005 Cave Creek Complex, 2004 Edge Complex and 2004 Willow Fires), with BARs >39% and dQ>+160%, while hydrologic response in the east was much less extreme with only 10 cases where post-fire dQ increased >+10%. The clear regional patterns in post-fire Q together with evidence showing that downward trends in P can mask flow enhancing effects of fire disturbance (24 watersheds), underline the importance of the combined analysis of wildland fire and climate impacts in national scale assessments. Research funded by the USDA Forest Service Southern Research Station, Joint Fire Science Program (#14-1-06-18), and Oak Ridge Institute for Science and Education (U.S. Department of Energy).

  1. Sharp-Wave Ripples Orchestrate the Induction of Synaptic Plasticity during Reactivation of Place Cell Firing Patterns in the Hippocampus

    Directory of Open Access Journals (Sweden)

    Josef H.L.P. Sadowski

    2016-03-01

    Full Text Available Place cell firing patterns reactivated during hippocampal sharp-wave ripples (SWRs in rest or sleep are thought to induce synaptic plasticity and thereby promote the consolidation of recently encoded information. However, the capacity of reactivated spike trains to induce plasticity has not been directly tested. Here, we show that reactivated place cell firing patterns simultaneously recorded from CA3 and CA1 of rat dorsal hippocampus are able to induce long-term potentiation (LTP at synapses between CA3 and CA1 cells but only if accompanied by SWR-associated synaptic activity and resulting dendritic depolarization. In addition, we show that the precise timing of coincident CA3 and CA1 place cell spikes in relation to SWR onset is critical for the induction of LTP and predictive of plasticity generated by reactivation. Our findings confirm an important role for SWRs in triggering and tuning plasticity processes that underlie memory consolidation in the hippocampus during rest or sleep.

  2. Sharp-Wave Ripples Orchestrate the Induction of Synaptic Plasticity during Reactivation of Place Cell Firing Patterns in the Hippocampus

    Science.gov (United States)

    Sadowski, Josef H.L.P.; Jones, Matthew W.; Mellor, Jack R.

    2016-01-01

    Summary Place cell firing patterns reactivated during hippocampal sharp-wave ripples (SWRs) in rest or sleep are thought to induce synaptic plasticity and thereby promote the consolidation of recently encoded information. However, the capacity of reactivated spike trains to induce plasticity has not been directly tested. Here, we show that reactivated place cell firing patterns simultaneously recorded from CA3 and CA1 of rat dorsal hippocampus are able to induce long-term potentiation (LTP) at synapses between CA3 and CA1 cells but only if accompanied by SWR-associated synaptic activity and resulting dendritic depolarization. In addition, we show that the precise timing of coincident CA3 and CA1 place cell spikes in relation to SWR onset is critical for the induction of LTP and predictive of plasticity generated by reactivation. Our findings confirm an important role for SWRs in triggering and tuning plasticity processes that underlie memory consolidation in the hippocampus during rest or sleep. PMID:26904941

  3. The specific features and pattern of febrile infection-related epilepsy syndrome (FIRES in children

    Directory of Open Access Journals (Sweden)

    L. V. Shalkevich

    2014-01-01

    Full Text Available The paper considers the etiology, pathogenesis, clinical presentations, diagnosis and treatment in children with febrile infection-related epilepsy syndrome (FIRES and the aspects of identifying this disease as an individual nosological entity. It details a study of the possible etiological factors of FIRES, such as metabolic, genetic, and immunological disorders, aseptic inflammatory processes, as well as a search for a certain infectious agent by inoculations of different biological environments of the body and by polymerase chain reaction; the diagnostic characteristics of FIRES at the present stage, including the use of electroencephalography, positron emission tomography, and magnetic resonance imaging; different approaches to drug therapy for FIRES at the onset stages of its clinical manifestations, protracted status epilepticus, and drugresistant epilepsy. The issues of the predictable outcome of this disease, including survival and the probability of further development of epilepsy and maintenance of cognitive functions, are also viewed. Diagnostic criteria for the syndrome, such as age at its onset 3 to 15 years in previously healthy children; acute onset as fever to develop high-frequency focal seizures several days later; the absence of the identified disease pathogen detected by the examinations of cerebrospinal fluid, serum, and other environments of the body; the development of drug-resistant epilepsy and severe permanent cognitive and motor deficits after the completion of an acute period in most cases are presented. The paper is clinically exemplified by the authors’ observation of an 11-year-old boy who meets the above criteria for the syndrome, but has a relatively favorable course, without developing severe drug-resistant epilepsy.

  4. Nerve growth factor regulates the firing patterns and synaptic composition of motoneurons.

    Science.gov (United States)

    Davis-López de Carrizosa, María A; Morado-Díaz, Camilo J; Morcuende, Sara; de la Cruz, Rosa R; Pastor, Angel M

    2010-06-16

    Target-derived neurotrophins exert powerful synaptotrophic actions in the adult brain and are involved in the regulation of different forms of synaptic plasticity. Target disconnection produces a profound synaptic stripping due to the lack of trophic support. Consequently, target reinnervation leads to synaptic remodeling and restoration of cellular functions. Extraocular motoneurons are unique in that they normally express the TrkA neurotrophin receptor in the adult, a feature not seen in other cranial or spinal motoneurons, except after lesions such as axotomy or in neurodegenerative diseases like amyotrophic lateral sclerosis. We investigated the effects of nerve growth factor (NGF) by retrogradely delivering this neurotrophin to abducens motoneurons of adult cats. Axotomy reduced the density of somatic boutons and the overall tonic and phasic firing modulation. Treatment with NGF restored synaptic inputs and firing modulation in axotomized motoneurons. When K252a, a selective inhibitor of tyrosine kinase activity, was applied to specifically test TrkA effects, the NGF-mediated restoration of synapses and firing-related parameters was abolished. Discharge variability and recruitment threshold were, however, increased by NGF compared with control or axotomized motoneurons. Interestingly, these parameters returned to normal following application of REX, an antibody raised against neurotrophin receptor p75 (p75(NTR)). In conclusion, NGF, acting retrogradely through TrkA receptors, supports afferent boutons and regulates the burst and tonic signals correlated with eye movements. On the other hand, p75(NTR) activation regulates recruitment threshold, which impacts on firing regularity. To our knowledge, this is the first report showing powerful synaptotrophic effects of NGF on motoneurons in vivo.

  5. Neural-Based Pattern Matching for Selection of Biophysical Model Meteorological Forcings

    Science.gov (United States)

    Coleman, A. M.; Wigmosta, M. S.; Li, H.; Venteris, E. R.; Skaggs, R. J.

    2011-12-01

    matching method using neural-network based Self-Organizing Maps (SOM) and GIS-based spatial modeling. This method pattern matches long-term mean monthly meteorology at an individual site to a series of CLIGEN stations within a user-defined proximal distance. The time-series data signatures of the selected stations are competed against one another using a SOM-generated similarity metric to determine the closest pattern match to the spatially distributed PRISM meteorology at the site of interest. This method overcomes issues with topographic dispersion of meteorology stations and existence of microclimates where the nearest meteorology station may not be the most representative.

  6. Assessment of the horizontal, fore-aft component of the ground reaction force from insole pressure patterns by using artificial neural networks

    NARCIS (Netherlands)

    Dr Hans C.C.M. Savelberg; Dr. ir. A. de Lange

    1999-01-01

    Objective. In this study it was investigated whether an artificial neural network can be used to determine the horizontal, fore-aft component of the ground reaction force from insole pressure patterns. Design. An artificial neural network was applied to map insole pressures and ground reaction

  7. Human Brain Basis of Musical Rhythm Perception: Common and Distinct Neural Substrates for Meter, Tempo, and Pattern

    Directory of Open Access Journals (Sweden)

    Michael H. Thaut

    2014-06-01

    Full Text Available Rhythm as the time structure of music is composed of distinct temporal components such as pattern, meter, and tempo. Each feature requires different computational processes: meter involves representing repeating cycles of strong and weak beats; pattern involves representing intervals at each local time point which vary in length across segments and are linked hierarchically; and tempo requires representing frequency rates of underlying pulse structures. We explored whether distinct rhythmic elements engage different neural mechanisms by recording brain activity of adult musicians and non-musicians with positron emission tomography (PET as they made covert same-different discriminations of (a pairs of rhythmic, monotonic tone sequences representing changes in pattern, tempo, and meter, and (b pairs of isochronous melodies. Common to pattern, meter, and tempo tasks were focal activities in right, or bilateral, areas of frontal, cingulate, parietal, prefrontal, temporal, and cerebellar cortices. Meter processing alone activated areas in right prefrontal and inferior frontal cortex associated with more cognitive and abstract representations. Pattern processing alone recruited right cortical areas involved in different kinds of auditory processing. Tempo processing alone engaged mechanisms subserving somatosensory and premotor information (e.g., posterior insula, postcentral gyrus. Melody produced activity different from the rhythm conditions (e.g., right anterior insula and various cerebellar areas. These exploratory findings suggest the outlines of some distinct neural components underlying the components of rhythmic structure.

  8. Human brain basis of musical rhythm perception: common and distinct neural substrates for meter, tempo, and pattern.

    Science.gov (United States)

    Thaut, Michael H; Trimarchi, Pietro Davide; Parsons, Lawrence M

    2014-06-17

    Rhythm as the time structure of music is composed of distinct temporal components such as pattern, meter, and tempo. Each feature requires different computational processes: meter involves representing repeating cycles of strong and weak beats; pattern involves representing intervals at each local time point which vary in length across segments and are linked hierarchically; and tempo requires representing frequency rates of underlying pulse structures. We explored whether distinct rhythmic elements engage different neural mechanisms by recording brain activity of adult musicians and non-musicians with positron emission tomography (PET) as they made covert same-different discriminations of (a) pairs of rhythmic, monotonic tone sequences representing changes in pattern, tempo, and meter, and (b) pairs of isochronous melodies. Common to pattern, meter, and tempo tasks were focal activities in right, or bilateral, areas of frontal, cingulate, parietal, prefrontal, temporal, and cerebellar cortices. Meter processing alone activated areas in right prefrontal and inferior frontal cortex associated with more cognitive and abstract representations. Pattern processing alone recruited right cortical areas involved in different kinds of auditory processing. Tempo processing alone engaged mechanisms subserving somatosensory and premotor information (e.g., posterior insula, postcentral gyrus). Melody produced activity different from the rhythm conditions (e.g., right anterior insula and various cerebellar areas). These exploratory findings suggest the outlines of some distinct neural components underlying the components of rhythmic structure.

  9. Denoising by coupled partial differential equations and extracting phase by backpropagation neural networks for electronic speckle pattern interferometry.

    Science.gov (United States)

    Tang, Chen; Lu, Wenjing; Chen, Song; Zhang, Zhen; Li, Botao; Wang, Wenping; Han, Lin

    2007-10-20

    We extend and refine previous work [Appl. Opt. 46, 2907 (2007)]. Combining the coupled nonlinear partial differential equations (PDEs) denoising model with the ordinary differential equations enhancement method, we propose the new denoising and enhancing model for electronic speckle pattern interferometry (ESPI) fringe patterns. Meanwhile, we propose the backpropagation neural networks (BPNN) method to obtain unwrapped phase values based on a skeleton map instead of traditional interpolations. We test the introduced methods on the computer-simulated speckle ESPI fringe patterns and experimentally obtained fringe pattern, respectively. The experimental results show that the coupled nonlinear PDEs denoising model is capable of effectively removing noise, and the unwrapped phase values obtained by the BPNN method are much more accurate than those obtained by the well-known traditional interpolation. In addition, the accuracy of the BPNN method is adjustable by changing the parameters of networks such as the number of neurons.

  10. Optimization of patterns of control bars using neural networks; Optimizacion de patrones de barras de control usando redes neuronales

    Energy Technology Data Exchange (ETDEWEB)

    Mejia S, D.M. [IPN, ESFM, Depto. de Ingenieria Nuclear, 07738 Mexico D.F. (Mexico); Ortiz S, J.J. [ININ, 52045 Ocoyoacac, Estado de Mexico (Mexico)]. e-mail: dulcema6715@hotmail.com

    2005-07-01

    In this work the RENOPBC system that is based on a recurrent multi state neural network, for the optimization of patterns of control bars in a cycle of balance of a boiling water reactor (BWR for their initials in English) is presented. The design of patterns of bars is based on the execution of operation thermal limits, to maintain criticizes the reactor and that the axial profile of power is adjusted to one predetermined along several steps of burnt. The patterns of control bars proposed by the system are comparable to those proposed by human experts with many hour-man of experience. These results are compared with those proposed by other techniques as genetic algorithms, colonies of ants and tabu search for the same operation cycle. As consequence it is appreciated that the proposed patterns of control bars, have bigger operation easiness that those proposed by the other techniques. (Author)

  11. From neural plate to cortical arousal-a neuronal network theory of sleep derived from in vitro "model" systems for primordial patterns of spontaneous bioelectric activity in the vertebrate central nervous system.

    Science.gov (United States)

    Corner, Michael A

    2013-05-22

    In the early 1960s intrinsically generated widespread neuronal discharges were discovered to be the basis for the earliest motor behavior throughout the animal kingdom. The pattern generating system is in fact programmed into the developing nervous system, in a regionally specific manner, already at the early neural plate stage. Such rhythmically modulated phasic bursts were next discovered to be a general feature of developing neural networks and, largely on the basis of experimental interventions in cultured neural tissues, to contribute significantly to their morpho-physiological maturation. In particular, the level of spontaneous synchronized bursting is homeostatically regulated, and has the effect of constraining the development of excessive network excitability. After birth or hatching, this "slow-wave" activity pattern becomes sporadically suppressed in favor of sensory oriented "waking" behaviors better adapted to dealing with environmental contingencies. It nevertheless reappears periodically as "sleep" at several species-specific points in the diurnal/nocturnal cycle. Although this "default" behavior pattern evolves with development, its essential features are preserved throughout the life cycle, and are based upon a few simple mechanisms which can be both experimentally demonstrated and simulated by computer modeling. In contrast, a late onto- and phylogenetic aspect of sleep, viz., the intermittent "paradoxical" activation of the forebrain so as to mimic waking activity, is much less well understood as regards its contribution to brain development. Some recent findings dealing with this question by means of cholinergically induced "aroused" firing patterns in developing neocortical cell cultures, followed by quantitative electrophysiological assays of immediate and longterm sequelae, will be discussed in connection with their putative implications for sleep ontogeny.

  12. Emergent bimodal firing patterns implement different encoding strategies during gamma-band oscillations

    Directory of Open Access Journals (Sweden)

    Belén eDe Sancristóbal

    2013-03-01

    Full Text Available Upon sensory stimulation, primary cortical areas readily engage in narrow-band rhythmic activity between 30 to 90 Hz, the so-called gamma oscillations. Here we show that, when embedded in a balanced network, type-I excitable neurons entrained to the collective rhythm show a discontinuity in their firing rates between a slow and a fast spiking mode. This jump in the spiking frequencies is characteristic to type II neurons, but is not present in the frequency-current curve (f-I curve of isolated type I neurons. Therefore, this rate bimodality arises as an emerging network property in type I population models. We have studied the mechanisms underlying the generation of these two firing modes, in order to reproduce the spiking activity of in vivo cortical recordings, which is known to be highly irregular and sparse. We have also analyzed the relation between afferent inputs and the single unit activity, and between the latter and the LFP phase, in order to establish how the collective dynamics modulates the spiking activity of the individual neurons. Our results reveal that the inhibitory-excitatory balance allows two encoding mechanisms, phase and rate code, to coexist within the network.

  13. Physiological modulators of Kv3.1 channels adjust firing patterns of auditory brain stem neurons

    Science.gov (United States)

    Brown, Maile R.; El-Hassar, Lynda; Zhang, Yalan; Alvaro, Giuseppe; Large, Charles H.

    2016-01-01

    Many rapidly firing neurons, including those in the medial nucleus of the trapezoid body (MNTB) in the auditory brain stem, express “high threshold” voltage-gated Kv3.1 potassium channels that activate only at positive potentials and are required for stimuli to generate rapid trains of actions potentials. We now describe the actions of two imidazolidinedione derivatives, AUT1 and AUT2, which modulate Kv3.1 channels. Using Chinese hamster ovary cells stably expressing rat Kv3.1 channels, we found that lower concentrations of these compounds shift the voltage of activation of Kv3.1 currents toward negative potentials, increasing currents evoked by depolarization from typical neuronal resting potentials. Single-channel recordings also showed that AUT1 shifted the open probability of Kv3.1 to more negative potentials. Higher concentrations of AUT2 also shifted inactivation to negative potentials. The effects of lower and higher concentrations could be mimicked in numerical simulations by increasing rates of activation and inactivation respectively, with no change in intrinsic voltage dependence. In brain slice recordings of mouse MNTB neurons, both AUT1 and AUT2 modulated firing rate at high rates of stimulation, a result predicted by numerical simulations. Our results suggest that pharmaceutical modulation of Kv3.1 currents represents a novel avenue for manipulation of neuronal excitability and has the potential for therapeutic benefit in the treatment of hearing disorders. PMID:27052580

  14. Morphogens, modeling and patterning the neural tube: an interview with James Briscoe.

    Science.gov (United States)

    Briscoe, James

    2015-01-20

    James Briscoe has a BSc in Microbiology and Virology (from the University of Warwick, UK) and a PhD in Molecular and Cellular Biology (from the Imperial Cancer Research Fund, London, now Cancer Research UK). He started working on the development of the neural tube in the lab of Tom Jessel as a postdoctoral fellow, establishing that there was graded sonic hedgehog signaling in the ventral neural tube. He is currently a group leader and Head of Division in Developmental Biology at the MRC National Institute for Medical Research (which will become part of the Francis Crick Institute in April 2015). He is working to understand the molecular and cellular mechanisms of graded signaling in the vertebrate neural tube.We interviewed him about the development of ideas on morphogenetic gradients and his own work on modeling the development of the neural tube for our series on modeling in biology.

  15. Adaptive inverse control of neural spatiotemporal spike patterns with a reproducing kernel Hilbert space (RKHS) framework.

    Science.gov (United States)

    Li, Lin; Park, Il Memming; Brockmeier, Austin; Chen, Badong; Seth, Sohan; Francis, Joseph T; Sanchez, Justin C; Príncipe, José C

    2013-07-01

    The precise control of spiking in a population of neurons via applied electrical stimulation is a challenge due to the sparseness of spiking responses and neural system plasticity. We pose neural stimulation as a system control problem where the system input is a multidimensional time-varying signal representing the stimulation, and the output is a set of spike trains; the goal is to drive the output such that the elicited population spiking activity is as close as possible to some desired activity, where closeness is defined by a cost function. If the neural system can be described by a time-invariant (homogeneous) model, then offline procedures can be used to derive the control procedure; however, for arbitrary neural systems this is not tractable. Furthermore, standard control methodologies are not suited to directly operate on spike trains that represent both the target and elicited system response. In this paper, we propose a multiple-input multiple-output (MIMO) adaptive inverse control scheme that operates on spike trains in a reproducing kernel Hilbert space (RKHS). The control scheme uses an inverse controller to approximate the inverse of the neural circuit. The proposed control system takes advantage of the precise timing of the neural events by using a Schoenberg kernel defined directly in the space of spike trains. The Schoenberg kernel maps the spike train to an RKHS and allows linear algorithm to control the nonlinear neural system without the danger of converging to local minima. During operation, the adaptation of the controller minimizes a difference defined in the spike train RKHS between the system and the target response and keeps the inverse controller close to the inverse of the current neural circuit, which enables adapting to neural perturbations. The results on a realistic synthetic neural circuit show that the inverse controller based on the Schoenberg kernel outperforms the decoding accuracy of other models based on the conventional rate

  16. Global Neural Pattern Similarity as a Common Basis for Categorization and Recognition Memory

    OpenAIRE

    Davis, Tyler; Xue, Gui; Love, Bradley C.; Preston, Alison. R.; Poldrack, Russell A

    2014-01-01

    Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categoriza...

  17. Neural correlates of generation and inhibition of verbal association patterns in mood disorders

    OpenAIRE

    Piguet, Camille; Desseilles, Martin; Cojan, Yann; Sterpenich, Virginie; Dayer, Alexandre; Bertschy, Gilles; Vuilleumier, Patrik

    2014-01-01

    OBJECTIVES: Thought disorders such as rumination or flight of ideas are frequent in patients with mood disorders, and not systematically linked to mood state. These symptoms point to anomalies in cognitive processes mediating the generation and control of thoughts; for example, associative thinking and inhibition. However, their neural substrates are not known. METHOD: To obtain an ecological measure of neural processes underlying the generation and suppression of spontaneous thoughts, we des...

  18. Dynamic excitation states and firing patterns are controlled by sodium channel kinetics in myenteric neurons: a simulation study.

    Science.gov (United States)

    Korogod, Sergiy M; Osorio, Nancy; Kulagina, Iryna B; Delmas, Patrick

    2014-01-01

    Enteric neurons located in the gastro-intestinal tract are of particular importance to control digestive functions such as motility and secretion. In our recent publication, we showed that mouse myenteric neurons exhibit 2 types of tetrodotoxin-resistant Na(+) currents: a fast inactivating Na(+) current produced by Nav1.5 channels, present in nearly all myenteric neurons, and a persistent Na(+) current attributed to Nav1.9 channels, restricted to the intrinsic primary afferent neurons (IPANs). By combination of experimental recording and computer simulation we found that Nav1.5 contributed to the upstroke velocity of action potentials (APs), whereas Nav1.9 opposed AP repolarization. Here, we detailed the Na(+), Ca(2+) and K(+) currents used in our computational model of IPAN. We refined the prototype cell to reproduce the sustained firing pattern recorded in situ. As shown in experimental conditions we demonstrated that Nav1.9 channels critically determine the up-state life-time and thus, are essential to sustain tonic firing.

  19. When do correlations increase with firing rates in recurrent networks?

    Directory of Open Access Journals (Sweden)

    Andrea K Barreiro

    2017-04-01

    Full Text Available A central question in neuroscience is to understand how noisy firing patterns are used to transmit information. Because neural spiking is noisy, spiking patterns are often quantified via pairwise correlations, or the probability that two cells will spike coincidentally, above and beyond their baseline firing rate. One observation frequently made in experiments, is that correlations can increase systematically with firing rate. Theoretical studies have determined that stimulus-dependent correlations that increase with firing rate can have beneficial effects on information coding; however, we still have an incomplete understanding of what circuit mechanisms do, or do not, produce this correlation-firing rate relationship. Here, we studied the relationship between pairwise correlations and firing rates in recurrently coupled excitatory-inhibitory spiking networks with conductance-based synapses. We found that with stronger excitatory coupling, a positive relationship emerged between pairwise correlations and firing rates. To explain these findings, we used linear response theory to predict the full correlation matrix and to decompose correlations in terms of graph motifs. We then used this decomposition to explain why covariation of correlations with firing rate-a relationship previously explained in feedforward networks driven by correlated input-emerges in some recurrent networks but not in others. Furthermore, when correlations covary with firing rate, this relationship is reflected in low-rank structure in the correlation matrix.

  20. Artificial Neural Network approach to develop unique Classification and Raga identification tools for Pattern Recognition in Carnatic Music

    Science.gov (United States)

    Srimani, P. K.; Parimala, Y. G.

    2011-12-01

    A unique approach has been developed to study patterns in ragas of Carnatic Classical music based on artificial neural networks. Ragas in Carnatic music which have found their roots in the Vedic period, have grown on a Scientific foundation over thousands of years. However owing to its vastness and complexities it has always been a challenge for scientists and musicologists to give an all encompassing perspective both qualitatively and quantitatively. Cognition, comprehension and perception of ragas in Indian classical music have always been the subject of intensive research, highly intriguing and many facets of these are hitherto not unravelled. This paper is an attempt to view the melakartha ragas with a cognitive perspective using artificial neural network based approach which has given raise to very interesting results. The 72 ragas of the melakartha system were defined through the combination of frequencies occurring in each of them. The data sets were trained using several neural networks. 100% accurate pattern recognition and classification was obtained using linear regression, TLRN, MLP and RBF networks. Performance of the different network topologies, by varying various network parameters, were compared. Linear regression was found to be the best performing network.

  1. A Combination of Central Pattern Generator-based and Reflex-based Neural Networks for Dynamic, Adaptive, Robust Bipedal Locomotion

    DEFF Research Database (Denmark)

    Di Canio, Giuliano; Larsen, Jørgen Christian; Wörgötter, Florentin

    2016-01-01

    Robotic systems inspired from humans have always been lightening up the curiosity of engineers and scientists. Of many challenges, human locomotion is a very difficult one where a number of different systems needs to interact in order to generate a correct and balanced pattern. To simulate the in...... network to generate basic walking patterns of a dynamic bipedal walking robot (DACBOT) and then a CPG-based neural network to ensure robust walking behavior......Robotic systems inspired from humans have always been lightening up the curiosity of engineers and scientists. Of many challenges, human locomotion is a very difficult one where a number of different systems needs to interact in order to generate a correct and balanced pattern. To simulate...... the interaction of these systems, implementations with reflexbased or central pattern generator (CPG)-based controllers have been tested on bipedal robot systems. In this paper we will combine the two controller types, into a controller that works with both reflex and CPG signals. We use a reflex-based neural...

  2. Multiple remote sensing data sources to assess spatio-temporal patterns of fire incidence over Campos Amazônicos Savanna Vegetation Enclave (Brazilian Amazon).

    Science.gov (United States)

    Alves, Daniel Borini; Pérez-Cabello, Fernando

    2017-12-01

    Fire activity plays an important role in the past, present and future of Earth system behavior. Monitoring and assessing spatial and temporal fire dynamics have a fundamental relevance in the understanding of ecological processes and the human impacts on different landscapes and multiple spatial scales. This work analyzes the spatio-temporal distribution of burned areas in one of the biggest savanna vegetation enclaves in the southern Brazilian Amazon, from 2000 to 2016, deriving information from multiple remote sensing data sources (Landsat and MODIS surface reflectance, TRMM pluviometry and Vegetation Continuous Field tree cover layers). A fire scars database with 30 m spatial resolution was generated using a Landsat time series. MODIS daily surface reflectance was used for accurate dating of the fire scars. TRMM pluviometry data were analyzed to dynamically establish time limits of the yearly dry season and burning periods. Burned area extent, frequency and recurrence were quantified comparing the results annually/seasonally. Additionally, Vegetation Continuous Field tree cover layers were used to analyze fire incidence over different types of tree cover domains. In the last seventeen years, 1.03millionha were burned within the study area, distributed across 1432 fire occurrences, highlighting 2005, 2010 and 2014 as the most affected years. Middle dry season fires represent 86.21% of the total burned areas and 32.05% of fire occurrences, affecting larger amount of higher density tree surfaces than other burning periods. The results provide new insights into the analysis of burned areas of the neotropical savannas, spatially and statistically reinforcing important aspects linked to the seasonality patterns of fire incidence in this landscape. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Analysis of chemical signals in red fire ants by gas chromatography and pattern recognition techniques

    Science.gov (United States)

    The combination of gas chromatography and pattern recognition (GC/PR) analysis is a powerful tool for investigating complicated biological problems. Clustering, mapping, discriminant development, etc. are necessary to analyze realistically large chromatographic data sets and to seek meaningful relat...

  4. G-protein-coupled receptors and localized signaling in the primary cilium during ventral neural tube patterning.

    Science.gov (United States)

    Hwang, Sun-Hee; Mukhopadhyay, Saikat

    2015-01-01

    The primary cilium is critical in sonic hedgehog (Shh)-dependent ventral patterning of the vertebrate neural tube. Most mutants that cause disruption of the cilium result in decreased Shh signaling in the neural tube. In contrast, mutations in the intraflagellar complex A (IFT-A) and the tubby family protein, Tulp3, result in increased Shh signaling in the neural tube. Proteomic analysis of Tulp3-binding proteins first pointed to the role of the IFT-A complex in trafficking Tulp3 into the cilia. Tulp3 directs trafficking of rhodopsin family G-protein-coupled receptors (GPCRs) to the cilia, suggesting the role of a GPCR in mediating the paradoxical effects of the Tulp3/IFT-A complex in causing increased Shh signaling. Gpr161 has recently been identified as a Tulp3/IFT-A-regulated GPCR that localizes to the primary cilium. A null knock-out mouse model of Gpr161 phenocopies Tulp3 and IFT-A mutants, and causes increased Shh signaling throughout the neural tube. In the absence of Shh, the bifunctional Gli transcription factors are proteolytically processed into repressor forms in a protein kinase A (PKA) -dependent and cilium-dependent manner. Gpr161 activity results in increased cAMP levels in a Gαs -coupled manner, and determines processing of Gli3. Shh signaling also results in removal of Gpr161 from the cilia, suggesting that Gpr161 functions in a positive feedback loop in the Shh pathway. As PKA-null and Gαs mutant embryos also exhibit increased Shh signaling in the neural tube, Gpr161 is a strong candidate for a GPCR that regulates ciliary cAMP levels, and activates PKA in close proximity to the cilia. © 2014 Wiley Periodicals, Inc.

  5. The Right Delay: Detecting Specific Spike Patterns with STDP and

    NARCIS (Netherlands)

    Datadien, A.H.R.; Haselager, W.F.G.; Sprinkhuizen-Kuyper, I.G.; Dobnikar, A.; Lotric, U.; Ster, B.

    2011-01-01

    Axonal conduction delays should not be ignored in simulations of spiking neural networks. Here it is shown that by using axonal conduction delays, neurons can display sensitivity to a specific spatio-temporal spike pattern. By using delays that complement the firing times in a pattern, spikes can

  6. Addition of deep brain stimulation signal to a local field potential driven Izhikevich model masks the pathological firing pattern of an STN neuron.

    Science.gov (United States)

    Michmizos, Kostis P; Nikita, Konstantina S

    2011-01-01

    The crucial engagement of the subthalamic nucleus (STN) with the neurosurgical procedure of deep brain stimulation (DBS) that alleviates medically intractable Parkinsonian tremor augments the need to refine our current understanding of STN. To enhance the efficacy of DBS as a result of precise targeting, STN boundaries are accurately mapped using extracellular microelectrode recordings (MERs). We utilized the intranuclear MER to acquire the local field potential (LFP) and drive an Izhikevich model of an STN neuron. Using the model as the test bed for clinically acquired data, we demonstrated that stimulation of the STN neuron produces excitatory responses that tonically increase its average firing rate and alter the pattern of its neuronal activity. We also found that the spiking rhythm increases linearly with the increase of amplitude, frequency, and duration of the DBS pulse, inside the clinical range. Our results are in agreement with the current hypothesis that DBS increases the firing rate of STN and masks its pathological bursting firing pattern.

  7. Parameter estimation of breast tumour using dynamic neural network from thermal pattern

    Directory of Open Access Journals (Sweden)

    Elham Saniei

    2016-11-01

    Full Text Available This article presents a new approach for estimating the depth, size, and metabolic heat generation rate of a tumour. For this purpose, the surface temperature distribution of a breast thermal image and the dynamic neural network was used. The research consisted of two steps: forward and inverse. For the forward section, a finite element model was created. The Pennes bio-heat equation was solved to find surface and depth temperature distributions. Data from the analysis, then, were used to train the dynamic neural network model (DNN. Results from the DNN training/testing confirmed those of the finite element model. For the inverse section, the trained neural network was applied to estimate the depth temperature distribution (tumour position from the surface temperature profile, extracted from the thermal image. Finally, tumour parameters were obtained from the depth temperature distribution. Experimental findings (20 patients were promising in terms of the model’s potential for retrieving tumour parameters.

  8. Assessing neural tuning for object perception in schizophrenia and bipolar disorder with multivariate pattern analysis of fMRI data.

    Science.gov (United States)

    Reavis, Eric A; Lee, Junghee; Wynn, Jonathan K; Engel, Stephen A; Cohen, Mark S; Nuechterlein, Keith H; Glahn, David C; Altshuler, Lori L; Green, Michael F

    2017-01-01

    Deficits in visual perception are well-established in schizophrenia and are linked to abnormal activity in the lateral occipital complex (LOC). Related deficits may exist in bipolar disorder. LOC contains neurons tuned to object features. It is unknown whether neural tuning in LOC or other visual areas is abnormal in patients, contributing to abnormal perception during visual tasks. This study used multivariate pattern analysis (MVPA) to investigate perceptual tuning for objects in schizophrenia and bipolar disorder. Fifty schizophrenia participants, 51 bipolar disorder participants, and 47 matched healthy controls completed five functional magnetic resonance imaging (fMRI) runs of a perceptual task in which they viewed pictures of four different objects and an outdoor scene. We performed classification analyses designed to assess the distinctiveness of activity corresponding to perception of each stimulus in LOC (a functionally localized region of interest). We also performed similar classification analyses throughout the brain using a searchlight technique. We compared classification accuracy and patterns of classification errors across groups. Stimulus classification accuracy was significantly above chance in all groups in LOC and throughout visual cortex. Classification errors were mostly within-category confusions (e.g., misclassifying one chair as another chair). There were no group differences in classification accuracy or patterns of confusion. The results show for the first time MVPA can be used successfully to classify individual perceptual stimuli in schizophrenia and bipolar disorder. However, the results do not provide evidence of abnormal neural tuning in schizophrenia and bipolar disorder.

  9. Temporal and spatial patterns in fire occurrence during the establishment of mixed-oak forests in eastern North America

    Science.gov (United States)

    Ryan W. McEwan; Todd F. Hutchinson; Robert P. Long; Robert D. Ford; Brian C. McCarthy

    2007-01-01

    What was the role of fire during the establishment of the current overstory (ca. 1870-1940) in mixed-oak forests of eastern North America? Nine sites representing a 240-km latitudinal gradient on the Allegheny and Cumberland Plateaus of eastern North America. Basal cross-sections were collected from 225 trees. Samples were surfaced, and fire scars were dated. Fire...

  10. Prediction of PM10 grades in Seoul, Korea using a neural network model based on synoptic patterns

    Science.gov (United States)

    Hur, S. K.; Oh, H. R.; Ho, C. H.; Kim, J.; Song, C. K.; Chang, L. S.; Lee, J. B.

    2016-12-01

    As of November 2014, the Korean Ministry of Environment (KME) started forecasting the level of ambient particulate matter with diameters ≤ 10 μm (PM10) as four grades: low (PM10 ≤ 30 μg m-3), moderate (30 150 μg m-3). Due to short history of forecast, overall performance of the operational forecasting system and its hit rate for the four PM10 grades are difficult to evaluate. In attempt to provide a statistical reference for the current air quality forecasting system, we hindcasted the four PM10 grades for the cold seasons (October-March) of 2001-2014 in Seoul, Korea using a neural network model based on the synoptic patterns of meteorological fields such as geopotential height, air temperature, relative humidity, and wind. In the form of cosine similarity, the distinctive synoptic patterns for each PM10 grades are well quantified as predictors to train the neural network model. Using these fields as predictors and considering the PM10 concentration in Seoul from the day before prediction as an additional predictor, an overall hit rate of 69% was achieved; the hit rates for the low, moderate, high, and very high PM10 grades were 33%, 83%, 45%, and 33%, respectively. This study reveals that the synoptic patterns of meteorological fields are useful predictors for the identification of favorable conditions for each PM10 grade, and the associated transboundary transport and local accumulation of PM10 from the industrialized regions of China. Consequently, the assessments of predictability obtained from the neural network model in this study are reliable to use as a statistical reference for the current air quality forecasting system.

  11. Identification of Abnormal System Noise Temperature Patterns in Deep Space Network Antennas Using Neural Network Trained Fuzzy Logic

    Science.gov (United States)

    Lu, Thomas; Pham, Timothy; Liao, Jason

    2011-01-01

    This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.

  12. Differential neural activity patterns for spatial relations in humans: a MEG study.

    Science.gov (United States)

    Scott, Nicole M; Leuthold, Arthur; Sera, Maria D; Georgopoulos, Apostolos P

    2016-02-01

    Children learn the words for above-below relations earlier than for left-right relations, despite treating these equally well in a simple visual categorization task. Even as adults--conflicts in congruency, such as when a stimulus is depicted in a spatially incongruent manner with respect to salient global cues--can be challenging. Here we investigated the neural correlates of encoding and maintaining in working memory above-below and left-right relational planes in 12 adults using magnetoencephalography in order to discover whether above-below relations are represented by the brain differently than left-right relations. Adults performed perfectly on the task behaviorally, so any differences in neural activity were attributed to the stimuli's cognitive attributes. In comparing above-below to left-right relations during stimulus encoding, we found the greatest differences in neural activity in areas associated with space and movement. In comparing congruent to incongruent trials, we found the greatest differential activity in premotor areas. For both contrasts, brain areas involved in the encoding phase were also involved in the maintenance phase, which provides evidence that those brain areas are particularly important in representing the relational planes or congruency types throughout the trial. When comparing neural activity associated with the relational planes during working memory, additional right posterior areas were implicated, whereas the congruent-incongruent contrast implicated additional bilateral frontal and temporal areas. These findings are consistent with the hypothesis left-right relations are represented differently than above-below relations.

  13. Dissociable Patterns of Neural Activity during Response Inhibition in Depressed Adolescents with and without Suicidal Behavior

    Science.gov (United States)

    Pan, Lisa A.; Batezati-Alves, Silvia C.; Almeida, Jorge R. C.; Segreti, AnnaMaria; Akkal, Dalila; Hassel, Stefanie; Lakdawala, Sara; Brent, David A.; Phillips, Mary L.

    2011-01-01

    Objectives: Impaired attentional control and behavioral control are implicated in adult suicidal behavior. Little is known about the functional integrity of neural circuitry supporting these processes in suicidal behavior in adolescence. Method: Functional magnetic resonance imaging was used in 15 adolescent suicide attempters with a history of…

  14. Sustained neural activity patterns during working memory in the human medial temporal lobe.

    NARCIS (Netherlands)

    Axmacher, N.; Mormann, F.; Fernandez, G.; Cohen, M.X.; Elger, C.E.; Fell, J.

    2007-01-01

    In contrast to classical findings that the medial temporal lobe (MTL) specifically underlies long-term memory, previous data suggest that MTL structures may also contribute to working memory (WM). However, the neural mechanisms by which the MTL supports WM have remained unknown. Here, we exploit

  15. The effect of caffeine citrate on neural breathing pattern in preterm infants.

    Science.gov (United States)

    Parikka, Vilhelmiina; Beck, Jennifer; Zhai, Qian; Leppäsalo, Juha; Lehtonen, Liisa; Soukka, Hanna

    2015-10-01

    Caffeine citrate is widely used to prevent and treat prematurity-associated apnea. The aim of this study was to characterize the effect of caffeine citrate on the neural control of breathing, especially central apnea, in premature infants. Preterm infants were evaluated for 30min before and 30min after caffeine citrate loading (20mg/kg). A feeding tube including miniaturized sensors was used to measure the diaphragm electrical activity (Edi) waveform. Central apnea was defined as any period where the Edi waveform was flat for >5s. Seventeen preterm infants with a mean age of three days and mean birth weight of 900 grams were evaluated. In addition to central apnea, several parameters including neural inspiratory time, neural respiratory rate, peak Edi, delta inspiratory change in Edi (phasic Edi) and minimum Edi on exhalation were measured. The majority of the apnea were short (5 to 10s) and the number of apnea correlated with birth weight (p=0.039). Caffeine citrate reduced significantly the number of 5-to-10-second-long central apnea during the 30-minute periods (12±11 to 7±7; p=0.02). Caffeine citrate increased both peak and phasic Edi leading to a significant increase in the diaphragm energy expenditure. Edi signal can be reliably measured and processed to study changes in premature infants' neural breathing. The beneficial effect of caffeine citrate on the reduction of the number of apnea is mediated through stimulated neural breathing increasing the diaphragm energy expenditure. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  16. A Cognition-Related Neural Oscillation Pattern, Generated in the Prelimbic Cortex, Can Control Operant Learning in Rats.

    Science.gov (United States)

    Hernández-González, Samuel; Andreu-Sánchez, Celia; Martín-Pascual, Miguel Ángel; Gruart, Agnès; Delgado-García, José María

    2017-06-14

    The prelimbic (PrL) cortex constitutes one of the highest levels of cortical hierarchy dedicated to the execution of adaptive behaviors. We have identified a specific local field potential (LFP) pattern generated in the PrL cortex and associated with cognition-related behaviors. We used this pattern to trigger the activation of a visual display on a touch screen as part of an operant conditioning task. Rats learned to increase the presentation rate of the selected θ to β-γ (θ/β-γ) transition pattern across training sessions. The selected LFP pattern appeared to coincide with a significant decrease in the firing of PrL pyramidal neurons and did not seem to propagate to other cortical or subcortical areas. An indication of the PrL cortex's cognitive nature is that the experimental disruption of this θ/β-γ transition pattern prevented the proper performance of the acquired task without affecting the generation of other motor responses. The use of this LFP pattern to trigger an operant task evoked only minor changes in its electrophysiological properties. Thus, the PrL cortex has the capability of generating an oscillatory pattern for dealing with environmental constraints. In addition, the selected θ/β-γ transition pattern could be a useful tool to activate the presentation of external cues or to modify the current circumstances.SIGNIFICANCE STATEMENT Brain-machine interfaces represent a solution for physically impaired people to communicate with external devices. We have identified a specific local field potential pattern generated in the prelimbic cortex and associated with goal-directed behaviors. We used the pattern to trigger the activation of a visual display on a touch screen as part of an operant conditioning task. Rats learned to increase the presentation rate of the selected field potential pattern across training. The selected pattern was not modified when used to activate the touch screen. Electrical stimulation of the recording site prevented

  17. Evaluation of DGVMs in tropical areas: linking patterns of vegetation cover, climate and fire to ecological processes

    Science.gov (United States)

    D'Onofrio, Donatella; von Hardenberg, Jost; Baudena, Mara

    2017-04-01

    Many current Dynamic Global Vegetation Models (DGVMs), including those incorporated into Earth System Models (ESMs), are able to realistically reproduce the distribution of the most worldwide biomes. However, they display high uncertainty in predicting the forest, savanna and grassland distributions and the transitions between them in tropical areas. These biomes are the most productive terrestrial ecosystems, and owing to their different biogeophysical and biogeochemical characteristics, future changes in their distributions could have also impacts on climate states. In particular, expected increasing temperature and CO2, modified precipitation regimes, as well as increasing land-use intensity could have large impacts on global biogeochemical cycles and precipitation, affecting the land-climate interactions. The difficulty of the DGVMs in simulating tropical vegetation, especially savanna structure and occurrence, has been associated with the way they represent the ecological processes and feedbacks between biotic and abiotic conditions. The inclusion of appropriate ecological mechanisms under present climatic conditions is essential for obtaining reliable future projections of vegetation and climate states. In this work we analyse observed relationships of tree and grass cover with climate and fire, and the current ecological understanding of the mechanisms driving the forest-savanna-grassland transition in Africa to evaluate the outcomes of a current state-of-the-art DGVM and to assess which ecological processes need to be included or improved within the model. Specifically, we analyse patterns of woody and herbaceous cover and fire return times from MODIS satellite observations, rainfall annual average and seasonality from TRMM satellite measurements and tree phenology information from the ESA global land cover map, comparing them with the outcomes of the LPJ-GUESS DGVM, also used by the EC-Earth global climate model. The comparison analysis with the LPJ

  18. Burst firing in a motion-sensitive neural pathway correlates with expansion properties of looming objects that evoke avoidance behaviours

    Directory of Open Access Journals (Sweden)

    Glyn Allan McMillan

    2015-12-01

    Full Text Available The locust visual system contains a well-defined motion-sensitive pathway that transfers visual input to motor centers involved in predator evasion and collision avoidance. One interneuron in this pathway, the descending contralateral movement detector (DCMD, is typically described as using rate coding; edge expansion of approaching objects causes an increased rate of neuronal firing that peaks after a certain retinal threshold angle is exceeded. However, evidence of intrinsic DCMD bursting properties combined with observable oscillations in mean firing rates and tight clustering of spikes in raw traces, suggest that bursting may be important for motion detection. Sensory neuron bursting provides important timing information about dynamic stimuli in many model systems, yet no studies have rigorously investigated if bursting occurs in the locust DCMD during object approach. We presented repetitions of 30 looming stimuli known to generate behavioural responses to each of 20 locusts in order to identify and quantify putative bursting activity in the DCMD. Overall, we found a bimodal distribution of inter-spike intervals (ISI with peaks of more frequent and shorter ISIs occurring from 1-8 ms and longer less frequent ISIs occurring from 40-50 ms. Subsequent analysis identified bursts and isolated single spikes from the responses. Bursting frequency increased in the latter phase of an approach and peaked at the time of collision, while isolated spiking was predominant during the beginning of stimulus approach. We also found that the majority of inter-burst intervals occurred at 40-50 ms (or 20-25 bursts/s. Bursting also occurred across varied stimulus parameters and suggests that burst timing may be a key component of looming detection. Our findings suggest that the DCMD uses two modes of coding to transmit information about looming stimuli and that these modes change dynamically with a changing stimulus at a behaviourally-relevant time.

  19. Burst Firing in a Motion-Sensitive Neural Pathway Correlates with Expansion Properties of Looming Objects that Evoke Avoidance Behaviors.

    Science.gov (United States)

    McMillan, Glyn A; Gray, John R

    2015-01-01

    The locust visual system contains a well-defined motion-sensitive pathway that transfers visual input to motor centers involved in predator evasion and collision avoidance. One interneuron in this pathway, the descending contralateral movement detector (DCMD), is typically described as using rate coding; edge expansion of approaching objects causes an increased rate of neuronal firing that peaks after a certain retinal threshold angle is exceeded. However, evidence of intrinsic DCMD bursting properties combined with observable oscillations in mean firing rates and tight clustering of spikes in raw traces, suggest that bursting may be important for motion detection. Sensory neuron bursting provides important timing information about dynamic stimuli in many model systems, yet no studies have rigorously investigated if bursting occurs in the locust DCMD during object approach. We presented repetitions of 30 looming stimuli known to generate behavioral responses to each of 20 locusts in order to identify and quantify putative bursting activity in the DCMD. Overall, we found a bimodal distribution of inter-spike intervals (ISI) with peaks of more frequent and shorter ISIs occurring from 1-8 ms and longer less frequent ISIs occurring from 40-50 ms. Subsequent analysis identified bursts and isolated single spikes from the responses. Bursting frequency increased in the latter phase of an approach and peaked at the time of collision, while isolated spiking was predominant during the beginning of stimulus approach. We also found that the majority of inter-burst intervals (IBIs) occurred at 40-50 ms (or 20-25 bursts/s). Bursting also occurred across varied stimulus parameters and suggests that burst timing may be a key component of looming detection. Our findings suggest that the DCMD uses two modes of coding to transmit information about looming stimuli and that these modes change dynamically with a changing stimulus at a behaviorally-relevant time.

  20. Neural correlates of variable working memory load across adult age and skill: dissociative patterns within the fronto-parietal network.

    Science.gov (United States)

    Nyberg, Lars; Dahlin, Erika; Stigsdotter Neely, Anna; Bäckman, Lars

    2009-02-01

    We examined neural changes related to variations in working memory load by using an n-back task with three levels and functional magnetic resonance imaging. Younger adults were divided into high- and low-performing groups (Young-High; Young-Low) and compared with older adults. Relative to Young-High, capacity-constraints in working memory were apparent between load 1-2 for the elderly and between load 2-3 for Young-Low. Capacity-constraints in neural activity followed this pattern by showing a monotonically increasing response in parietal cortex and thalamus for Young-High, whereas activity leveled off at 1-back for the elderly and at 2-back for Young-Low. The response in dorsal frontal cortex followed a similar pattern with the addition that the magnitude of activation differed within capacity limitations (Old > Young at 1-back; Young-Low > Young-High at 2-back). These findings indicate that an important determinant of WM capacity is the ability to keep the frontal cortex adequately engaged in relation to current task demands.

  1. The role of the anterior neural ridge and Fgf-8 in early forebrain patterning and regionalization in Xenopus laevis.

    Science.gov (United States)

    Eagleson, Gerald W; Dempewolf, Ryan D

    2002-05-01

    The tissue, cellular and molecular mechanisms that regulate early regional specification of the vertebrate forebrain are largely unknown. We studied the expression patterns of Xbf-1, an anterior (and telencephalon) neural-specific winged helix transcription factor and Fgf-8, an early-secreted factor. This study looked at Xbf-1 and Fgf-8 expression in combination with embryonic grafting experiments and also used beads containing the recombinant Fgf-8 protein to determine these factors' effects upon forebrain patterning events. We provide evidence that additional Fgf-8 displaces Xbf-1 expression posteriorly, suggesting a concentration dependence of Fgf-8 for the early distinct regionalization of the telencephalic primordia. Also, additional stage 15 mid-anterior neural ridge (mANR) transplants inhibited telencephalon development, whereas lateral ANR transplants facilitated increased areas of telencephalon development. In both cases, these transplantations promoted ectopic expression of Xbf-1. These studies suggested that the distinct regionalization of the forebrain primordia involves the inhibitory actions of the mANR towards a telencephalon development and maintaining bilateral telencephali. These telencephalic primordia are initially localized by optimal Fgf-8 expression. The anterior mANR will eventually become the anterior and rostral diencephalic tissue. This in vivo study demonstrated Fgf-8 and the mANR are important in forebrain regionalization.

  2. Patterning and predicting aquatic insect richness in four West-African coastal rivers using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Edia E.O.

    2010-10-01

    Full Text Available Despite their importance in stream management, the aquatic insect assemblages are still little known in West Africa. This is particularly true in South-Eastern Ivory Coast, where aquatic insect assemblages were hardly studied. We therefore aimed at characterising aquatic insect assemblages on four coastal rivers in South-Eastern Ivory Coast. Patterning aquatic insect assemblages was achieved using a Self-Organizing Map (SOM, an unsupervised Artificial Neural Networks (ANN method. This method was applied to pattern the samples based on the richness of five major orders of aquatic insects (Diptera, Ephemeroptera, Coleoptera, Trichoptera and Odonata. This permitted to identify three clusters that were mainly related to the local environmental status of sampling sites. Then, we used the environmental characteristics of the sites to predict, using a multilayer perceptron neural network (MLP, trained by BackPropagation algorithm (BP, a supervised ANN, the richness of the five insect orders. The BP showed high predictability (0.90 for both Diptera and Trichoptera, 0.84 for both Coleoptera and Odonata, 0.69 for Ephemeroptera. The most contributing variables in predicting the five insect order richness were pH, conductivity, total dissolved solids, water temperature, percentage of rock and the canopy. This underlines the crucial influence of both instream characteristics and riparian context.

  3. Subjective and Neural Responses to Intravenous Alcohol in Young Adults with Light and Heavy Drinking Patterns

    OpenAIRE

    Gilman, Jodi M; Ramchandani, Vijay A.; Crouss, Tess; Hommer, Daniel W.

    2011-01-01

    Heavy alcohol consumption during young adulthood is a risk factor for the development of serious alcohol use disorders. Research has shown that individual differences in subjective responses to alcohol may affect individuals' vulnerability to developing alcoholism. Studies comparing the subjective and objective response to alcohol between light and heavy drinkers (HDs), however, have yielded inconsistent results, and neural responses to alcohol in these groups have not been characterized. We ...

  4. Behavioral and neural concordance in parent-child dyadic sleep patterns

    Directory of Open Access Journals (Sweden)

    Tae-Ho Lee

    2017-08-01

    Full Text Available Sleep habits developed in adolescence shape long-term trajectories of psychological, educational, and physiological well-being. Adolescents’ sleep behaviors are shaped by their parents’ sleep at both the behavioral and biological levels. In the current study, we sought to examine how neural concordance in resting-state functional connectivity between parent-child dyads is associated with dyadic concordance in sleep duration and adolescents’ sleep quality. To this end, we scanned both parents and their child (N = 28 parent-child dyads; parent Mage = 42.8 years; adolescent Mage = 14.9 years; 14.3% father; 46.4% female adolescent as they each underwent a resting-state scan. Using daily diaries, we also assessed dyadic concordance in sleep duration across two weeks. Our results show that greater daily concordance in sleep behavior is associated with greater neural concordance in default-mode network connectivity between parents and children. Moreover, greater neural and behavioral concordances in sleep is associated with more optimal sleep quality in adolescents. The current findings expand our understanding of dyadic concordance by providing a neurobiological mechanism by which parents and children share daily sleep behaviors.

  5. Application of computational neural networks in predicting atmospheric pollutant concentrations due to fossil-fired electric power generation

    Energy Technology Data Exchange (ETDEWEB)

    El-Hawary, F. [BH Engineering Systems & Technical Univ. of Nova Scotia (Canada)

    1995-12-31

    The ability to accurately predict the behavior of a dynamic system is of essential importance in monitoring and control of complex processes. In this regard recent advances in neural-net based system identification represent a significant step toward development and design of a new generation of control tools for increased system performance and reliability. The enabling functionality is the one of accurate representation of a model of a nonlinear and nonstationary dynamic system. This functionality provides valuable new opportunities including: (1) The ability to predict future system behavior on the basis of actual system observations, (2) On-line evaluation and display of system performance and design of early warning systems, and (3) Controller optimization for improved system performance. In this presentation, we discuss the issues involved in definition and design of learning control systems and their impact on power system control. Several numerical examples are provided for illustrative purpose.

  6. Estimation of Scale Deposition in the Water Walls of an Operating Indian Coal Fired Boiler: Predictive Modeling Approach Using Artificial Neural Networks

    Science.gov (United States)

    Kumari, Amrita; Das, Suchandan Kumar; Srivastava, Prem Kumar

    2016-04-01

    Application of computational intelligence for predicting industrial processes has been in extensive use in various industrial sectors including power sector industry. An ANN model using multi-layer perceptron philosophy has been proposed in this paper to predict the deposition behaviors of oxide scale on waterwall tubes of a coal fired boiler. The input parameters comprises of boiler water chemistry and associated operating parameters, such as, pH, alkalinity, total dissolved solids, specific conductivity, iron and dissolved oxygen concentration of the feed water and local heat flux on boiler tube. An efficient gradient based network optimization algorithm has been employed to minimize neural predictions errors. Effects of heat flux, iron content, pH and the concentrations of total dissolved solids in feed water and other operating variables on the scale deposition behavior have been studied. It has been observed that heat flux, iron content and pH of the feed water have a relatively prime influence on the rate of oxide scale deposition in water walls of an Indian boiler. Reasonably good agreement between ANN model predictions and the measured values of oxide scale deposition rate has been observed which is corroborated by the regression fit between these values.

  7. Fuel treatment prescriptions alter spatial patterns of fire severity around the wildland-urban interface during the Wallow Fire, Arizona, USA

    Science.gov (United States)

    Maureen C. Kennedy; Morris C. Johnson

    2014-01-01

    Fuel reduction treatments are implemented in the forest surrounding the wildland–urban interface (WUI) to provide defensible space and safe opportunity for the protection of homes during a wildfire. The 2011 Wallow Fire in Arizona USA burned through recently implemented fuel treatments in the wildland surrounding residential communities in the WUI, and those fuel...

  8. Microbial biomass estimated by phospholipid fatty acids (PLFA pattern in a soil with different post-fire treatments (seeding, mulching one year after the experimental fire

    Directory of Open Access Journals (Sweden)

    A. Lombao

    2013-01-01

    Full Text Available The soil microbial community in a field experiment with different post-fire treatments (seeding, mulching was characterized by means of phospholipids fatty acids analyses (PLFA. The soil was a Leptosol developed over granite with a slope of 38-54%, located in the N.W. (Spain, within the Atlantic humid temperate zone. The total biomass and the biomass of specific microbial groups (fungi, bacteria, actinomycetes, gram-positive bacteria, gram-negative-bacteria was assessed in soil samples taken from the A horizon (0-5 cm depth at different sampling times over one year. The results showed that the total microbial biomass and the biomass of specific microbial groups in burnt soils were slightly lower than in the corresponding unburnt control. The fire decreased significantly the fungal:bacterial and gram-negative:gram-positive ratios. Differences in physiological state of the microbial communities were observed as consequence of the medium-term prescribed fire impact; the higher values in the ratios of saturated to unsaturated fatty acids and cyclopropyl fatty acids to monoicoc precursors exhibited by the burnt soils as compared with the unburnt soil suggested that the microbial communities were stressed by the experimental fire. The data also showed the absence of any medium-term response of microbial community to the seeding and mulching treatments.

  9. Qualitatively different bifurcation scenarios observed in the firing of identical nerve fibers

    Energy Technology Data Exchange (ETDEWEB)

    Zheng Qiaohua; Liu Zhiqiang; Yang Minghao; Wu Xiaobo [College of Life Science, Shaanxi Normal University, Xi' an 710062 (China); Gu Huaguang [College of Life Science, Shaanxi Normal University, Xi' an 710062 (China)], E-mail: guhuaguang@263.net; Ren Wei [College of Life Science, Shaanxi Normal University, Xi' an 710062 (China)], E-mail: renwei1964@vip.sina.com

    2009-01-26

    This Letter reports various bifurcation scenarios, including period-adding bifurcations with chaos and those with stochastic firing patterns, generated by identical neural pacemakers. The scenarios are studied by properly adjusting two physiological parameters, one is 4-aminopyridine sensitive potassium conductance and the other is extracellular calcium concentration, in both experimentation and simulation.

  10. Abnormal neural activation patterns underlying working memory impairment in chronic phencyclidine-treated mice.

    Directory of Open Access Journals (Sweden)

    Yosefu Arime

    Full Text Available Working memory impairment is a hallmark feature of schizophrenia and is thought be caused by dysfunctions in the prefrontal cortex (PFC and associated brain regions. However, the neural circuit anomalies underlying this impairment are poorly understood. The aim of this study is to assess working memory performance in the chronic phencyclidine (PCP mouse model of schizophrenia, and to identify the neural substrates of working memory. To address this issue, we conducted the following experiments for mice after withdrawal from chronic administration (14 days of either saline or PCP (10 mg/kg: (1 a discrete paired-trial variable-delay task in T-maze to assess working memory, and (2 brain-wide c-Fos mapping to identify activated brain regions relevant to this task performance either 90 min or 0 min after the completion of the task, with each time point examined under working memory effort and basal conditions. Correct responses in the test phase of the task were significantly reduced across delays (5, 15, and 30 s in chronic PCP-treated mice compared with chronic saline-treated controls, suggesting delay-independent impairments in working memory in the PCP group. In layer 2-3 of the prelimbic cortex, the number of working memory effort-elicited c-Fos+ cells was significantly higher in the chronic PCP group than in the chronic saline group. The main effect of working memory effort relative to basal conditions was to induce significantly increased c-Fos+ cells in the other layers of prelimbic cortex and the anterior cingulate and infralimbic cortex regardless of the different chronic regimens. Conversely, this working memory effort had a negative effect (fewer c-Fos+ cells in the ventral hippocampus. These results shed light on some putative neural networks relevant to working memory impairments in mice chronically treated with PCP, and emphasize the importance of the layer 2-3 of the prelimbic cortex of the PFC.

  11. An Artificial Neural Network for Movement Pattern Analysis to Estimate Blood Alcohol Content Level.

    Science.gov (United States)

    Gharani, Pedram; Suffoletto, Brian; Chung, Tammy; Karimi, Hassan A

    2017-12-13

    Impairments in gait occur after alcohol consumption, and, if detected in real-time, could guide the delivery of "just-in-time" injury prevention interventions. We aimed to identify the salient features of gait that could be used for estimating blood alcohol content (BAC) level in a typical drinking environment. We recruited 10 young adults with a history of heavy drinking to test our research app. During four consecutive Fridays and Saturdays, every hour from 8 p.m. to 12 a.m., they were prompted to use the app to report alcohol consumption and complete a 5-step straight-line walking task, during which 3-axis acceleration and angular velocity data was sampled at a frequency of 100 Hz. BAC for each subject was calculated. From sensor signals, 24 features were calculated using a sliding window technique, including energy, mean, and standard deviation. Using an artificial neural network (ANN), we performed regression analysis to define a model determining association between gait features and BACs. Part (70%) of the data was then used as a training dataset, and the results tested and validated using the rest of the samples. We evaluated different training algorithms for the neural network and the result showed that a Bayesian regularization neural network (BRNN) was the most efficient and accurate. Analyses support the use of the tandem gait task paired with our approach to reliably estimate BAC based on gait features. Results from this work could be useful in designing effective prevention interventions to reduce risky behaviors during periods of alcohol consumption.

  12. Abnormal neural activation patterns underlying working memory impairment in chronic phencyclidine-treated mice.

    Science.gov (United States)

    Arime, Yosefu; Akiyama, Kazufumi

    2017-01-01

    Working memory impairment is a hallmark feature of schizophrenia and is thought be caused by dysfunctions in the prefrontal cortex (PFC) and associated brain regions. However, the neural circuit anomalies underlying this impairment are poorly understood. The aim of this study is to assess working memory performance in the chronic phencyclidine (PCP) mouse model of schizophrenia, and to identify the neural substrates of working memory. To address this issue, we conducted the following experiments for mice after withdrawal from chronic administration (14 days) of either saline or PCP (10 mg/kg): (1) a discrete paired-trial variable-delay task in T-maze to assess working memory, and (2) brain-wide c-Fos mapping to identify activated brain regions relevant to this task performance either 90 min or 0 min after the completion of the task, with each time point examined under working memory effort and basal conditions. Correct responses in the test phase of the task were significantly reduced across delays (5, 15, and 30 s) in chronic PCP-treated mice compared with chronic saline-treated controls, suggesting delay-independent impairments in working memory in the PCP group. In layer 2-3 of the prelimbic cortex, the number of working memory effort-elicited c-Fos+ cells was significantly higher in the chronic PCP group than in the chronic saline group. The main effect of working memory effort relative to basal conditions was to induce significantly increased c-Fos+ cells in the other layers of prelimbic cortex and the anterior cingulate and infralimbic cortex regardless of the different chronic regimens. Conversely, this working memory effort had a negative effect (fewer c-Fos+ cells) in the ventral hippocampus. These results shed light on some putative neural networks relevant to working memory impairments in mice chronically treated with PCP, and emphasize the importance of the layer 2-3 of the prelimbic cortex of the PFC.

  13. Training verb argument structure production in agrammatic aphasia: behavioral and neural recovery patterns.

    Science.gov (United States)

    Thompson, Cynthia K; Riley, Ellyn A; den Ouden, Dirk-Bart; Meltzer-Asscher, Aya; Lukic, Sladjana

    2013-10-01

    Neuroimaging and lesion studies indicate a left hemisphere network for verb and verb argument structure processing, involving both frontal and temporoparietal brain regions. Although their verb comprehension is generally unimpaired, it is well known that individuals with agrammatic aphasia often present with verb production deficits, characterized by an argument structure complexity hierarchy, indicating faulty access to argument structure representations for production and integration into syntactic contexts. Recovery of verb processing in agrammatism, however, has received little attention and no studies have examined the neural mechanisms associated with improved verb and argument structure processing. In the present study we trained agrammatic individuals on verbs with complex argument structure in sentence contexts and examined generalization to verbs with less complex argument structure. The neural substrates of improved verb production were examined using functional magnetic resonance imaging (fMRI). Eight individuals with chronic agrammatic aphasia participated in the study (four experimental and four control participants). Production of three-argument verbs in active sentences was trained using a sentence generation task emphasizing the verb's argument structure and the thematic roles of sentential noun phrases. Before and after training, production of trained and untrained verbs was tested in naming and sentence production and fMRI scans were obtained, using an action naming task. Significant pre- to post-training improvement in trained and untrained (one- and two-argument) verbs was found for treated, but not control, participants, with between-group differences found for verb naming, production of verbs in sentences, and production of argument structure. fMRI activation derived from post-treatment compared to pre-treatment scans revealed upregulation in cortical regions implicated for verb and argument structure processing in healthy controls. Training

  14. Statistical Discriminability Estimation for Pattern Classification Based on Neural Incremental Attribute Learning

    DEFF Research Database (Denmark)

    Wang, Ting; Guan, Sheng-Uei; Puthusserypady, Sadasivan

    2014-01-01

    in the corresponding incremental way. Based on Single Discriminability (SD), where only the feature discrimination ability is computed, a new filter statistical feature discrimination ability predictive metric, called the Accumulative Discriminability (AD), is designed for the dynamical feature discrimination ability...... estimation. Moreover, a criterion that summarizes all the produced values of AD is employed with a GA (Genetic Algorithm)-based approach to obtain the optimum feature ordering for classification problems based on neural networks by means of IAL. Compared with the feature ordering obtained by other approaches...

  15. Influence of atmospheric rivers on vegetation productivity and fire patterns in the southwestern U.S.

    Science.gov (United States)

    Albano, Christine M.; Dettinger, Michael; Soulard, Christopher E.

    2017-01-01

    In the southwestern U.S., the meteorological phenomenon known as atmospheric rivers (ARs) has gained increasing attention due to its strong connections to floods, snowpacks, and water supplies in the West Coast states. Relatively less is known about the ecological implications of ARs, particularly in the interior Southwest, where AR storms are less common. To address this gap, we compared a chronology of AR landfalls on the west coast between 1989 and 2011 and between 25°N and 42.5°N to annual metrics of the normalized difference vegetation index (NDVI; an indicator of vegetation productivity) and daily resolution precipitation data to assess influences of AR-fed winter precipitation on vegetation productivity across the southwestern U.S. We mapped correlations between winter AR precipitation during landfalling ARs and (1) annual maximum NDVI and (2) area burned by large wildfires summarized by ecoregion during the same year as the landfalls and during the following year. Interannual variations of AR precipitation strongly influenced both NDVI and area burned by wildfire in some dryland ecoregions. The influence of ARs on dryland vegetation varied significantly depending on the latitude of landfall, with those ARs making landfall below 35°N latitude more strongly influencing these systems, and with effects observed as far as 1300 km from the landfall location. As climatologists' understanding of the synoptic patterns associated with the occurrence of ARs continues to evolve, an increased understanding of how AR landfalls, in aggregate, influence vegetation productivity and associated wildfire activity in dryland ecosystems may provide opportunities to better predict ecological responses to climate and climate change.

  16. Reprogramming fibroblasts to neural-precursor-like cells by structured overexpression of pallial patterning genes.

    Science.gov (United States)

    Raciti, Marilena; Granzotto, Marilena; Duc, Minh Do; Fimiani, Cristina; Cellot, Giada; Cherubini, Enrico; Mallamaci, Antonello

    2013-11-01

    In this study, we assayed the capability of four genes implicated in embryonic specification of the cortico-cerebral field, Foxg1, Pax6, Emx2 and Lhx2, to reprogramme mouse embryonic fibroblasts towards neural identities. Lentivirus-mediated, TetON-dependent overexpression of Pax6 and Foxg1 transgenes specifically activated the neural stem cell (NSC) reporter Sox1-EGFP in a substantial fraction of engineered cells. The efficiency of this process was enhanced up to ten times by simultaneous inactivation of Trp53 and co-administration of a specific drug mix inhibiting HDACs, H3K27-HMTase and H3K4m2-demethylase. Remarkably, a fraction of the reprogrammed population expressed other NSC markers and retained its new identity, even after switching off the reprogramming transgenes. When transferred into a pro-differentiative environment, Pax6/Foxg1-overexpressing cells activated the neuronal marker Tau-EGFP. Frequency of Tau-EGFP positive cells was almost doubled upon delayed delivery of Emx2 and Lhx2 transgenes. A further improvement of the neuron-like cell output was achieved by inhibition of the BMP and TGFβ pathways. Tau-EGFP positive cells were able to generate action potentials upon injection of depolarizing current pulses, further indicating their neuron-like phenotype. © 2013.

  17. Behavioral pattern separation and its link to the neural mechanisms of fear generalization.

    Science.gov (United States)

    Lange, Iris; Goossens, Liesbet; Michielse, Stijn; Bakker, Jindra; Lissek, Shmuel; Papalini, Silvia; Verhagen, Simone; Leibold, Nicole; Marcelis, Machteld; Wichers, Marieke; Lieverse, Ritsaert; van Os, Jim; van Amelsvoort, Therese; Schruers, Koen

    2017-11-01

    Fear generalization is a prominent feature of anxiety disorders and post-traumatic stress disorder (PTSD). It is defined as enhanced fear responding to a stimulus that bears similarities, but is not identical to a threatening stimulus. Pattern separation, a hippocampal-dependent process, is critical for stimulus discrimination; it transforms similar experiences or events into non-overlapping representations. This study is the first in humans to investigate the extent to which fear generalization relies on behavioral pattern separation abilities. Participants (N = 46) completed a behavioral task taxing pattern separation, and a neuroimaging fear conditioning and generalization paradigm. Results show an association between lower behavioral pattern separation performance and increased generalization in shock expectancy scores, but not in fear ratings. Furthermore, lower behavioral pattern separation was associated with diminished recruitment of the subcallosal cortex during presentation of generalization stimuli. This region showed functional connectivity with the orbitofrontal cortex and ventromedial prefrontal cortex. Together, the data provide novel experimental evidence that pattern separation is related to generalization of threat expectancies, and reduced fear inhibition processes in frontal regions. Deficient pattern separation may be critical in overgeneralization and therefore may contribute to the pathophysiology of anxiety disorders and PTSD. © The Author (2017). Published by Oxford University Press.

  18. An 800-year fire history

    Science.gov (United States)

    Stanley G. Kitchen

    2010-01-01

    "Fire in the woods!" The words are a real heart stopper. Yet in spite of its capacity to destroy, fire plays an essential role in shaping plant communities. Knowledge of the patterns of fire over long time periods is critical for understanding this role. Trees often retain evidence of nonlethal fires in the form of injuries or scars in the annual growth rings...

  19. Neural Networks Retrieving Boolean Patterns in a Sea of Gaussian Ones

    Science.gov (United States)

    Agliari, Elena; Barra, Adriano; Longo, Chiara; Tantari, Daniele

    2017-09-01

    Restricted Boltzmann machines are key tools in machine learning and are described by the energy function of bipartite spin-glasses. From a statistical mechanical perspective, they share the same Gibbs measure of Hopfield networks for associative memory. In this equivalence, weights in the former play as patterns in the latter. As Boltzmann machines usually require real weights to be trained with gradient-descent-like methods, while Hopfield networks typically store binary patterns to be able to retrieve, the investigation of a mixed Hebbian network, equipped with both real (e.g., Gaussian) and discrete (e.g., Boolean) patterns naturally arises. We prove that, in the challenging regime of a high storage of real patterns, where retrieval is forbidden, an additional load of Boolean patterns can still be retrieved, as long as the ratio between the overall load and the network size does not exceed a critical threshold, that turns out to be the same of the standard Amit-Gutfreund-Sompolinsky theory. Assuming replica symmetry, we study the case of a low load of Boolean patterns combining the stochastic stability and Hamilton-Jacobi interpolating techniques. The result can be extended to the high load by a non rigorous but standard replica computation argument.

  20. A cortical neural prosthesis for restoring and enhancing memory

    Science.gov (United States)

    Berger, Theodore W.; Hampson, Robert E.; Song, Dong; Goonawardena, Anushka; Marmarelis, Vasilis Z.; Deadwyler, Sam A.

    2011-08-01

    A primary objective in developing a neural prosthesis is to replace neural circuitry in the brain that no longer functions appropriately. Such a goal requires artificial reconstruction of neuron-to-neuron connections in a way that can be recognized by the remaining normal circuitry, and that promotes appropriate interaction. In this study, the application of a specially designed neural prosthesis using a multi-input/multi-output (MIMO) nonlinear model is demonstrated by using trains of electrical stimulation pulses to substitute for MIMO model derived ensemble firing patterns. Ensembles of CA3 and CA1 hippocampal neurons, recorded from rats performing a delayed-nonmatch-to-sample (DNMS) memory task, exhibited successful encoding of trial-specific sample lever information in the form of different spatiotemporal firing patterns. MIMO patterns, identified online and in real-time, were employed within a closed-loop behavioral paradigm. Results showed that the model was able to predict successful performance on the same trial. Also, MIMO model-derived patterns, delivered as electrical stimulation to the same electrodes, improved performance under normal testing conditions and, more importantly, were capable of recovering performance when delivered to animals with ensemble hippocampal activity compromised by pharmacologic blockade of synaptic transmission. These integrated experimental-modeling studies show for the first time that, with sufficient information about the neural coding of memories, a neural prosthesis capable of real-time diagnosis and manipulation of the encoding process can restore and even enhance cognitive, mnemonic processes.

  1. A cortical neural prosthesis for restoring and enhancing memory.

    Science.gov (United States)

    Berger, Theodore W; Hampson, Robert E; Song, Dong; Goonawardena, Anushka; Marmarelis, Vasilis Z; Deadwyler, Sam A

    2011-08-01

    A primary objective in developing a neural prosthesis is to replace neural circuitry in the brain that no longer functions appropriately. Such a goal requires artificial reconstruction of neuron-to-neuron connections in a way that can be recognized by the remaining normal circuitry, and that promotes appropriate interaction. In this study, the application of a specially designed neural prosthesis using a multi-input/multi-output (MIMO) nonlinear model is demonstrated by using trains of electrical stimulation pulses to substitute for MIMO model derived ensemble firing patterns. Ensembles of CA3 and CA1 hippocampal neurons, recorded from rats performing a delayed-nonmatch-to-sample (DNMS) memory task, exhibited successful encoding of trial-specific sample lever information in the form of different spatiotemporal firing patterns. MIMO patterns, identified online and in real-time, were employed within a closed-loop behavioral paradigm. Results showed that the model was able to predict successful performance on the same trial. Also, MIMO model-derived patterns, delivered as electrical stimulation to the same electrodes, improved performance under normal testing conditions and, more importantly, were capable of recovering performance when delivered to animals with ensemble hippocampal activity compromised by pharmacologic blockade of synaptic transmission. These integrated experimental-modeling studies show for the first time that, with sufficient information about the neural coding of memories, a neural prosthesis capable of real-time diagnosis and manipulation of the encoding process can restore and even enhance cognitive, mnemonic processes.

  2. What Is Lost During Dreamless Sleep: The Relationship Between Neural Connectivity Patterns and Consciousness

    Directory of Open Access Journals (Sweden)

    Michaela Klimova

    2014-09-01

    Full Text Available Non-rapid eye movement (NREM sleep is characterised by reduced consciousness; thus, studying its neural characteristics acts as a useful indication of what is needed for conscious experience. The integrated information theory (Tononi, 2008 states that the ability of different thalamocortical regions to interact is crucial for consciousness, thereby motivating research concerning connectivity changes in the thalamocortical system that accompany changing consciousness levels. This review aims to discuss investigations of functional connectivity of resting-state and large-scale brain networks, applying correlational approaches to neuroimaging data as well as studies that used brain stimulation to investigate effective connectivity. Most findings suggest a reorganisation of functional brain networks where inter-region connectivity is reduced and intra-region connectivity is stronger in deep sleep than wakefulness.

  3. Pattern recognition and analysis of short duration disturbance based on neural network

    Science.gov (United States)

    Wang, Huaying

    2008-10-01

    For quantitative detection of distortions of voltage waveform, a novel approach based on wavelet transform (WT) to detect and locate the power quality (PQ) disturbances is proposed. Due to expansion of power electronics devices, the wide diffusion of nonlinear and time-variant loads has caused massive serious PQ problems in power system. The signal containing noise is de-noised by WT, and then become input node to the wavelet neural network. The standard genetic algorithm is utilized to complete the network structure, and then the fundamental component of the signal is estimated to extract the mixed information. Therefore the disturbance signal is acquired by subtracting the fundamental component. In processing of disturbances signal, the principle of singularity detection using WT modulus maxima is presented with dyadic WT approach for the detection and localization of the PQ. The simulation results demonstrate that the proposed method is effective.

  4. Traceability of honey origin based on volatiles pattern processing by artificial neural networks.

    Science.gov (United States)

    Cajka, Tomas; Hajslova, Jana; Pudil, Frantisek; Riddellova, Katerina

    2009-02-27

    Head-space solid-phase microextraction (HS-SPME)-based procedure, coupled to comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GCxGC-TOF-MS), was employed for fast characterisation of honey volatiles. In total, 374 samples were collected over two production seasons in Corsica (n=219) and other European countries (n=155) with the emphasis to confirm the authenticity of the honeys labelled as "Corsica" (protected denomination of origin region). For the chemometric analysis, artificial neural networks with multilayer perceptrons (ANN-MLP) were tested. The best prediction (94.5%) and classification (96.5%) abilities of the ANN-MLP model were obtained when the data from two honey harvests were aggregated in order to improve the model performance compared to separate year harvests.

  5. Adaptive pattern recognition by mini-max neural networks as a part of an intelligent processor

    Science.gov (United States)

    Szu, Harold H.

    1990-01-01

    In this decade and progressing into 21st Century, NASA will have missions including Space Station and the Earth related Planet Sciences. To support these missions, a high degree of sophistication in machine automation and an increasing amount of data processing throughput rate are necessary. Meeting these challenges requires intelligent machines, designed to support the necessary automations in a remote space and hazardous environment. There are two approaches to designing these intelligent machines. One of these is the knowledge-based expert system approach, namely AI. The other is a non-rule approach based on parallel and distributed computing for adaptive fault-tolerances, namely Neural or Natural Intelligence (NI). The union of AI and NI is the solution to the problem stated above. The NI segment of this unit extracts features automatically by applying Cauchy simulated annealing to a mini-max cost energy function. The feature discovered by NI can then be passed to the AI system for future processing, and vice versa. This passing increases reliability, for AI can follow the NI formulated algorithm exactly, and can provide the context knowledge base as the constraints of neurocomputing. The mini-max cost function that solves the unknown feature can furthermore give us a top-down architectural design of neural networks by means of Taylor series expansion of the cost function. A typical mini-max cost function consists of the sample variance of each class in the numerator, and separation of the center of each class in the denominator. Thus, when the total cost energy is minimized, the conflicting goals of intraclass clustering and interclass segregation are achieved simultaneously.

  6. Exploring non-stationarity patterns in schizophrenia: neural reorganization abnormalities in the alpha band

    Science.gov (United States)

    Núñez, Pablo; Poza, Jesús; Bachiller, Alejandro; Gomez-Pilar, Javier; Lubeiro, Alba; Molina, Vicente; Hornero, Roberto

    2017-08-01

    Objective. The aim of this paper was to characterize brain non-stationarity during an auditory oddball task in schizophrenia (SCH). The level of non-stationarity was measured in the baseline and response windows of relevant tones in SCH patients and healthy controls. Approach. Event-related potentials were recorded from 28 SCH patients and 51 controls. Non-stationarity was estimated in the conventional electroencephalography frequency bands by means of Kullback-Leibler divergence (KLD). Relative power (RP) was also computed to assess a possible complementarity with KLD. Main results. Results showed a widespread statistically significant increase in the level of non-stationarity from baseline to response in all frequency bands for both groups. Statistically significant differences in non-stationarity were found between SCH patients and controls in beta-2 and in the alpha band. SCH patients showed more non-stationarity in the left parieto-occipital region during the baseline window in the beta-2 band. A leave-one-out cross validation classification study with feature selection based on binary stepwise logistic regression to discriminate between SCH patients and controls provided a positive predictive value of 72.73% and negative predictive value of 78.95%. Significance. KLD can characterize transient neural reorganization during an attentional task in response to novelty and relevance. Our findings suggest anomalous reorganization of neural dynamics in SCH during an oddball task. The abnormal frequency-dependent modulation found in SCH patients during relevant tones is in agreement with the hypothesis of aberrant salience detection in SCH. The increase in non-stationarity in the alpha band during the active task supports the notion that this band is involved in top-down processing. The baseline differences in the beta-2 band suggest that hyperactivation of the default mode network during attention tasks may be related to SCH symptoms. Furthermore, the classification

  7. Global relationship of fire occurrence and fire intensity: A test of intermediate fire occurrence-intensity hypothesis

    Science.gov (United States)

    Luo, Ruisen; Hui, Dafeng; Miao, Ning; Liang, Chuan; Wells, Nicholas

    2017-05-01

    Fire plays a significant role in global atmosphere and biosphere carbon and nutrient cycles. Globally, there are substantially different distributions and impacts between fire occurrence and fire intensity. It is prominent to have a thorough investigation of global relationship between fire occurrence and fire intensity for future fire prediction and management. In this study, we proposed an intermediate fire occurrence-intensity (IFOI) hypothesis for the global relationship between fire occurrence and fire intensity, suggesting that fire occurrence changes with fire intensity following a humped relationship. We examined this hypothesis via satellite data from January 2001 to December 2013 at a global scale, and in small and large fire intensity zones, respectively. Furthermore, the fire occurrence and fire intensity relationship was developed among different vegetation types to reveal the changes of parameters and strengths. Finally, the environmental factors (including climatic, hydraulic, biological, and anthropogenic variables) underpinning the fire occurrence and intensity pattern were evaluated for the underlying mechanisms. The results supported our IFOI hypothesis and demonstrated that the humped relationship is driven by different causes among vegetation types. Fire occurrence increases with fire intensity in small fire intensity zones due to alleviation of the factors limiting both fire occurrence and intensity. Beyond a certain fire intensity threshold, fire occurrence is constrained, probably due to the limitation of available fuels. The information generated in this study could be helpful for understanding global variation of fire occurrence and fire intensity due to fire-vegetation-climate-human interactions and facilitating future fire management.

  8. From image processing to classification: IV. Classification of electrophoretic patterns by neural networks and statistical methods enable quality assessment of wheat varieties for breadmaking

    DEFF Research Database (Denmark)

    Jensen, Kirsten; Kesmir, Can; Søndergaard, Ib

    1996-01-01

    breeding programs in sevaral countries. In this study, we used two multivariate techniques to classify digitized patterns from isoelectric focusing og gliadins and glutenins: a two-layered neural network architecture consisting of a self-organizing feature map and a feed-forward classifier [1...

  9. From image processing to classification: IV. Classification of electrophoretic patterns by neural networks and statistical methods enable quality assessment of wheat varieties for bread making

    DEFF Research Database (Denmark)

    Jensen, K.; Kesmir, Can; Søndergaard, Ib

    1996-01-01

    breeding programs in several countries. In this study, we used two multivariate techniques to classify digitized patterns from isoelectric focusing of gliadins and glutenins: a two-layered neural network architecture consisting of a self-organizing feature map and a feed-forward classifier [1...

  10. Adolescent-specific patterns of behavior and neural activity during social reinforcement learning

    Science.gov (United States)

    Jones, Rebecca M.; Somerville, Leah H.; Li, Jian; Ruberry, Erika J.; Powers, Alisa; Mehta, Natasha; Dyke, Jonathan; Casey, BJ

    2014-01-01

    Humans are sophisticated social beings. Social cues from others are exceptionally salient, particularly during adolescence. Understanding how adolescents interpret and learn from variable social signals can provide insight into the observed shift in social sensitivity during this period. The current study tested 120 participants between the ages of 8 and 25 years on a social reinforcement learning task where the probability of receiving positive social feedback was parametrically manipulated. Seventy-eight of these participants completed the task during fMRI scanning. Modeling trial-by-trial learning, children and adults showed higher positive learning rates than adolescents, suggesting that adolescents demonstrated less differentiation in their reaction times for peers who provided more positive feedback. Forming expectations about receiving positive social reinforcement correlated with neural activity within the medial prefrontal cortex and ventral striatum across age. Adolescents, unlike children and adults, showed greater insular activity during positive prediction error learning and increased activity in the supplementary motor cortex and the putamen when receiving positive social feedback regardless of the expected outcome, suggesting that peer approval may motivate adolescents towards action. While different amounts of positive social reinforcement enhanced learning in children and adults, all positive social reinforcement equally motivated adolescents. Together, these findings indicate that sensitivity to peer approval during adolescence goes beyond simple reinforcement theory accounts and suggests possible explanations for how peers may motivate adolescent behavior. PMID:24550063

  11. A Cutting Pattern Recognition Method for Shearers Based on Improved Ensemble Empirical Mode Decomposition and a Probabilistic Neural Network.

    Science.gov (United States)

    Xu, Jing; Wang, Zhongbin; Tan, Chao; Si, Lei; Liu, Xinhua

    2015-10-30

    In order to guarantee the stable operation of shearers and promote construction of an automatic coal mining working face, an online cutting pattern recognition method with high accuracy and speed based on Improved Ensemble Empirical Mode Decomposition (IEEMD) and Probabilistic Neural Network (PNN) is proposed. An industrial microphone is installed on the shearer and the cutting sound is collected as the recognition criterion to overcome the disadvantages of giant size, contact measurement and low identification rate of traditional detectors. To avoid end-point effects and get rid of undesirable intrinsic mode function (IMF) components in the initial signal, IEEMD is conducted on the sound. The end-point continuation based on the practical storage data is performed first to overcome the end-point effect. Next the average correlation coefficient, which is calculated by the correlation of the first IMF with others, is introduced to select essential IMFs. Then the energy and standard deviation of the reminder IMFs are extracted as features and PNN is applied to classify the cutting patterns. Finally, a simulation example, with an accuracy of 92.67%, and an industrial application prove the efficiency and correctness of the proposed method.

  12. A Cutting Pattern Recognition Method for Shearers Based on Improved Ensemble Empirical Mode Decomposition and a Probabilistic Neural Network

    Directory of Open Access Journals (Sweden)

    Jing Xu

    2015-10-01

    Full Text Available In order to guarantee the stable operation of shearers and promote construction of an automatic coal mining working face, an online cutting pattern recognition method with high accuracy and speed based on Improved Ensemble Empirical Mode Decomposition (IEEMD and Probabilistic Neural Network (PNN is proposed. An industrial microphone is installed on the shearer and the cutting sound is collected as the recognition criterion to overcome the disadvantages of giant size, contact measurement and low identification rate of traditional detectors. To avoid end-point effects and get rid of undesirable intrinsic mode function (IMF components in the initial signal, IEEMD is conducted on the sound. The end-point continuation based on the practical storage data is performed first to overcome the end-point effect. Next the average correlation coefficient, which is calculated by the correlation of the first IMF with others, is introduced to select essential IMFs. Then the energy and standard deviation of the reminder IMFs are extracted as features and PNN is applied to classify the cutting patterns. Finally, a simulation example, with an accuracy of 92.67%, and an industrial application prove the efficiency and correctness of the proposed method.

  13. The Dlx5-FGF10 signaling cascade controls cranial neural crest and myoblast interaction during oropharyngeal patterning and development.

    Science.gov (United States)

    Sugii, Hideki; Grimaldi, Alexandre; Li, Jingyuan; Parada, Carolina; Vu-Ho, Thach; Feng, Jifan; Jing, Junjun; Yuan, Yuan; Guo, Yuxing; Maeda, Hidefumi; Chai, Yang

    2017-11-01

    Craniofacial development depends on cell-cell interactions, coordinated cellular movement and differentiation under the control of regulatory gene networks, which include the distal-less (Dlx) gene family. However, the functional significance of Dlx5 in patterning the oropharyngeal region has remained unknown. Here, we show that loss of Dlx5 leads to a shortened soft palate and an absence of the levator veli palatini, palatopharyngeus and palatoglossus muscles that are derived from the 4th pharyngeal arch (PA); however, the tensor veli palatini, derived from the 1st PA, is unaffected. Dlx5-positive cranial neural crest (CNC) cells are in direct contact with myoblasts derived from the pharyngeal mesoderm, and Dlx5 disruption leads to altered proliferation and apoptosis of CNC and muscle progenitor cells. Moreover, the FGF10 pathway is downregulated in Dlx5-/- mice, and activation of FGF10 signaling rescues CNC cell proliferation and myogenic differentiation in these mutant mice. Collectively, our results indicate that Dlx5 plays crucial roles in the patterning of the oropharyngeal region and development of muscles derived from the 4th PA mesoderm in the soft palate, likely via interactions between CNC-derived and myogenic progenitor cells. © 2017. Published by The Company of Biologists Ltd.

  14. Probabilistic neural network with homogeneity testing in recognition of discrete patterns set.

    Science.gov (United States)

    Savchenko, A V

    2013-10-01

    The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n-grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%-7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. Copyright © 2013 Elsevier Ltd. All rights reserved.

  15. Patterns of longitudinal neural activity linked to different cognitive profiles in Parkinson’s disease

    Directory of Open Access Journals (Sweden)

    Atsuko Nagano-Saito

    2016-11-01

    Full Text Available Mild cognitive impairment in Parkinson’s disease (PD has been linked with functional brain changes. Previously, using functional magnetic resonance imaging (fMRI, we reported reduced cortico-striatal activity in patients with PD who also had mild cognitive impairment (MCI versus those who did not (non-MCI. We followed up these patients to investigate the longitudinal effect on the neural activity. Twenty-four non-demented patients with Parkinson’s disease (non-MCI: 12, MCI; 12 were included in the study. Each participant underwent two fMRIs while performing the Wisconsin Card Sorting Task 20 months apart. The non-MCI patients recruited the usual cognitive corticostriatal loop at the first and second sessions (Time 1 and Time 2, respectively. However, decreased activity was observed in the cerebellum and occipital area and increased activity was observed in the medial prefrontal cortex and parietal lobe during planning set-shift at Time 2. Increased activity in the precuneus was also demonstrated while executing set-shifts at Time 2. The MCI patients revealed more activity in the frontal, parietal and occipital lobes during planning set-shifts, and in the parietal and occipital lobes, precuneus, and cerebellum, during executing set-shift at Time 2. Analysis regrouping of both groups of PD patients revealed that hippocampal and thalamic activity at Time 1 was associated with less cognitive decline over time. Our results reveal that functional alteration along the time-points differed between the non-MCI and MCI patients. They also underline the importance of preserving thalamic and hippocampal function with respect to cognitive decline over time.

  16. Neural differentiation of human embryonic stem cells as an in vitro tool for the study of the expression patterns of the neuronal cytoskeleton during neurogenesis.

    Science.gov (United States)

    Liu, Chao; Zhong, Yongwang; Apostolou, Andria; Fang, Shengyun

    2013-09-13

    The neural differentiation of human embryonic stem cells (ESCs) is a potential tool for elucidating the key mechanisms involved in human neurogenesis. Nestin and β-III-tubulin, which are cytoskeleton proteins, are marker proteins of neural stem cells (NSCs) and neurons, respectively. However, the expression patterns of nestin and β-III-tubulin in neural derivatives from human ESCs remain unclear. In this study, we found that neural progenitor cells (NPCs) derived from H9 cells express high levels of nestin and musashi-1. In contrast, β-III-tubulin was weakly expressed in a few NPCs. Moreover, in these cells, nestin formed filament networks, whereas β-III-tubulin was distributed randomly as small particles. As the differentiation proceeded, the nestin filament networks and the β-III-tubulin particles were found in both the cell soma and the cellular processes. Moreover, the colocalization of nestin and β-III-tubulin was found mainly in the cell processes and neurite-like structures and not in the cell soma. These results may aid our understanding of the expression patterns of nestin and β-III-tubulin during the neural differentiation of H9 cells. Copyright © 2013 Elsevier Inc. All rights reserved.

  17. Firing distance estimation through the analysis of the gunshot residue deposit pattern around the bullet entrance hole by inductively coupled plasma-mass spectrometry: an experimental study.

    Science.gov (United States)

    Santos, Agostinho; Magalhães, Teresa; Vieira, Duarte Nuno; Almeida, Agostinho A; Sousa, António V

    2007-03-01

    The use of inductively coupled plasma-mass spectrometry (ICP-MS) in the study of gunshot residues (GSR) is relatively recent, and only a few studies have been published on the subject. In the present paper, this instrumental technique has been used to study the deposit pattern of the GSR around the bullet entrance hole, through the analysis of antimony (Sb), barium (Ba), and lead (Pb). The data obtained were used to establish a mathematical model for estimating the firing distance. Test shots using a 6.35-mm pistol were made against a target of cotton tissue, and the amounts of Sb, Ba, and Pb deposited in quadrangular pieces of the target, cut from 4 radial positions, were determined by ICP-MS. In these experimental conditions, it was possible to estimate the firing distance on the interval [20-80] cm. The best linear correlation between ln m and d, where m is the mass of Sb, Ba, or Pb in the samples, expressed in mug/g of target tissue, and d the firing distance, was obtained at radial distances between 3.5 cm and 4.5 cm from the entrance hole. The best regression curve which adjusted to the data was a linear multiple regression between the firing distance and the logarithm of the mass of each element: d = a + b(1)X(1) + b(2)X(2) + b(3)X(3), where X(1) = ln m (Sb), X(2) = ln m(Ba) and X(3) = ln m (Pb). The accuracy of firing distance estimation using only 1 or 2 elements was not significantly different from the one obtained with the 3 elements.

  18. Altered neuronal firing pattern of the basal ganglia nucleus plays a role in levodopa-induced dyskinesia in patients with Parkinson's disease

    Directory of Open Access Journals (Sweden)

    Xiaoyu eLi

    2015-11-01

    Full Text Available Background: Levodopa therapy alleviates the symptoms of Parkinson's disease (PD, but long-term treatment often leads to motor complications such as levodopa-induced dyskinesia (LID. Aim: To explore the neuronal activity in the basal ganglia nuclei in patients with PD and LID. Methods: Thirty patients with idiopathic PD (age, 55.1±11.0 years; disease duration, 8.7±5.6 years were enrolled between August 2006 and August 2013 at the Xuanwu Hospital, Capital Medical University, China. Their Hoehn and Yahr scores ranged from 2 to 4 and their UPDRS III scores were 28.5±5.2. Fifteen of them had severe LID (UPDRS IV scores of 6.7±1.6. Microelectrode recording was performed in the globus pallidus internus (GPi and subthalamic nucleus (STN during pallidotomy (n=12 or STN deep brain stimulation (DBS; bilateral, n=12; unilateral, n=6. The firing patterns and frequencies of various cell types were analyzed by assessing single cell interspike intervals (ISIs and the corresponding coefficient of variation (CV. Results: A total of 295 neurons were identified from the GPi (n=12 and STN (n=18. These included 26 (8.8% highly grouped discharge, 30 (10.2% low frequency firing, 78 (26.4% rapid tonic discharge, 103 (34.9% irregular activity, and 58 (19.7% tremor-related activity. There were significant differences between the two groups (P<0.05 for neurons with irregular firing, highly irregular cluster-like firing, and low-frequency firing. Conclusion: Altered neuronal activity was observed in the basal ganglia nucleus of GPi and STN, and may play important roles in the pathophysiology of PD and LID.

  19. Attachment patterns trigger differential neural signature of emotional processing in adolescents.

    Directory of Open Access Journals (Sweden)

    Maria Josefina Escobar

    Full Text Available BACKGROUND: Research suggests that individuals with different attachment patterns process social information differently, especially in terms of facial emotion recognition. However, few studies have explored social information processes in adolescents. This study examined the behavioral and ERP correlates of emotional processing in adolescents with different attachment orientations (insecure attachment group and secure attachment group; IAG and SAG, respectively. This study also explored the association of these correlates to individual neuropsychological profiles. METHODOLOGY/PRINCIPAL FINDINGS: We used a modified version of the dual valence task (DVT, in which participants classify stimuli (faces and words according to emotional valence (positive or negative. Results showed that the IAG performed significantly worse than SAG on tests of executive function (EF attention, processing speed, visuospatial abilities and cognitive flexibility. In the behavioral DVT, the IAG presented lower performance and accuracy. The IAG also exhibited slower RTs for stimuli with negative valence. Compared to the SAG, the IAG showed a negative bias for faces; a larger P1 and attenuated N170 component over the right hemisphere was observed. A negative bias was also observed in the IAG for word stimuli, which was demonstrated by comparing the N170 amplitude of the IAG with the valence of the SAG. Finally, the amplitude of the N170 elicited by the facial stimuli correlated with EF in both groups (and negative valence with EF in the IAG. CONCLUSIONS/SIGNIFICANCE: Our results suggest that individuals with different attachment patterns process key emotional information and corresponding EF differently. This is evidenced by an early modulation of ERP components' amplitudes, which are correlated with behavioral and neuropsychological effects. In brief, attachments patterns appear to impact multiple domains, such as emotional processing and EFs.

  20. Neural correlates of intentional switching from ternary to binary meter in a musical hemiola pattern

    Directory of Open Access Journals (Sweden)

    Takako eFujioka

    2014-11-01

    Full Text Available Musical rhythms are often perceived and interpreted within a metrical framework that integrates timing information hierarchically based on interval ratios. Endogenous timing processes facilitate this metrical integration and allow us using the sensory context for predicting when an expected sensory event will happen (‘predictive timing’. Previously, we showed that listening to metronomes and subjectively imagining the two different meters of march and waltz modulated the resulting auditory evoked responses in the temporal lobe and motor-related brain areas such as the motor cortex, basal ganglia, and cerebellum. Here we further explored the intentional transitions between the two metrical contexts, known as hemiola in the Western classical music dating back to the 16th century. We examined MEG from 12 musicians while they repeatedly listened to a sequence of 12 unaccented clicks with an interval of 390 ms, and tapped to them with the right hand according to a 3+3+2+2+2 hemiola accent pattern. While participants listened to the same metronome sequence and imagined the accents, their pattern of brain responses significantly changed just before the pivot point of metric transition from ternary to binary meter. Until 100 ms before the pivot point, brain activities were more similar to those in the simple ternary meter than those in the simple binary meter, but the pattern was reversed afterwards. A similar transition was also observed at the downbeat after the pivot. Brain areas related to the metric transition were identified from source reconstruction of the MEG using a beamformer and included auditory cortices, sensorimotor and premotor cortices, cerebellum, inferior/middle frontal gyrus, parahippocampal gyrus, inferior parietal lobule, cingulate cortex, and precuneus. The results strongly support that predictive timing processes related to auditory-motor, fronto-parietal, and medial limbic systems underlie metrical representation and its

  1. Fires in Amazonia

    Science.gov (United States)

    Aragão, Luiz E. O. C.; Anderson, Liana O.; Lima, André; Arai, Egidio

    2016-11-01

    Fire has been used since the first humans arrived in Amazonia; however, it has recently become a widely used instrument for large-scale forest clearance. Patterns of fire incidence in the region have been exacerbated by recent drought events. Understanding temporal and spatial fire patterns as well as their consequences for forest structure, species composition, and the carbon cycle is critical for minimising global change impacts on Amazonian ecosystems and people. In this chapter, we provide an overview of the state of our knowledge on the spatial and temporal patterns of fire incidence in Amazonia, depicting the historical fire usage in the region, their relationship with land use and land cover, and their responses to climate seasonality and droughts. We subsequently focus on the impacts of fire, by quantifying the extent of burnt forests during major droughts and describing the main impacts on forest structure, composition, and carbon stocks. Finally, we present an overview of modelling initiatives for forecasting fire incidence in the region. We conclude by providing a comprehensive view of the processes that influence fire occurrence, potential feedbacks, and impacts in Amazonia. We also highlight how key areas within fire ecology must be improved for a better understanding of the long-term effect of fire on the Amazon forest 'biome'.

  2. Neural Plasticity and Memory: Is Memory Encoded in Hydrogen Bonding Patterns?

    Science.gov (United States)

    Amtul, Zareen; Rahman, Atta-Ur

    2016-02-01

    Current models of memory storage recognize posttranslational modification vital for short-term and mRNA translation for long-lasting information storage. However, at the molecular level things are quite vague. A comprehensive review of the molecular basis of short and long-lasting synaptic plasticity literature leads us to propose that the hydrogen bonding pattern at the molecular level may be a permissive, vital step of memory storage. Therefore, we propose that the pattern of hydrogen bonding network of biomolecules (glycoproteins and/or DNA template, for instance) at the synapse is the critical edifying mechanism essential for short- and long-term memories. A novel aspect of this model is that nonrandom impulsive (or unplanned) synaptic activity functions as a synchronized positive-feedback rehearsal mechanism by revising the configurations of the hydrogen bonding network by tweaking the earlier tailored hydrogen bonds. This process may also maintain the elasticity of the related synapses involved in memory storage, a characteristic needed for such networks to alter intricacy and revise endlessly. The primary purpose of this review is to stimulate the efforts to elaborate the mechanism of neuronal connectivity both at molecular and chemical levels. © The Author(s) 2014.

  3. Theoretical approaches to holistic biological features: Pattern formation, neural networks and the brain-mind relation.

    Science.gov (United States)

    Gierer, Alfred

    2002-06-01

    The topic of this article is the relation between bottom-up and top-down, reductionist and holistic approaches to the solution of basic biological problems. While there is no doubt that the laws of physics apply to all events in space and time, including the domains of life, understanding biology depends not only on elucidating the role of the molecules involved, but, to an increasing extent, on systems theoretical approaches in diverse fields of the life sciences. Examples discussed in this article are the generation of spatial patterns in development by the interplay of autocatalysis and lateral inhibition; the evolution of integrating capabilities of the human brain, such as cognition-based empathy; and both neurobiological and epistemological aspects of scientific theories of consciousness and the mind.

  4. A Review of Fire Interactions and Mass Fires

    Directory of Open Access Journals (Sweden)

    Mark A. Finney

    2011-01-01

    Full Text Available The character of a wildland fire can change dramatically in the presence of another nearby fire. Understanding and predicting the changes in behavior due to fire-fire interactions cannot only be life-saving to those on the ground, but also be used to better control a prescribed fire to meet objectives. In discontinuous fuel types, such interactions may elicit fire spread where none otherwise existed. Fire-fire interactions occur naturally when spot fires start ahead of the main fire and when separate fire events converge in one location. Interactions can be created intentionally during prescribed fires by using spatial ignition patterns. Mass fires are among the most extreme examples of interactive behavior. This paper presents a review of the detailed effects of fire-fire interaction in terms of merging or coalescence criteria, burning rates, flame dimensions, flame temperature, indraft velocity, pulsation, and convection column dynamics. Though relevant in many situations, these changes in fire behavior have yet to be included in any operational-fire models or decision support systems.

  5. Fire History

    Data.gov (United States)

    California Department of Resources — The Fire Perimeters data consists of CDF fires 300 acres and greater in size and USFS fires 10 acres and greater throughout California from 1950 to 2002. Some fires...

  6. Fire Perimeters

    Data.gov (United States)

    California Department of Resources — The Fire Perimeters data consists of CDF fires 300 acres and greater in size and USFS fires 10 acres and greater throughout California from 1950 to 2003. Some fires...

  7. Irregular Firing and High-Conductance States in Spinal Motoneurons during Scratching and Swimming

    DEFF Research Database (Denmark)

    Guzulaitis, Robertas; Hounsgaard, Jorn; Alaburda, Aidas

    2016-01-01

    in general. Here we compare conductance and firing patterns in spinal motoneurons during network activity for scratching and swimming in an ex vivo carapace-spinal cord preparation from adult turtles (Trachemys scripta elegans). The pattern and relative engagement of motoneurons are distinctly different...... of spinal motor network activity. SIGNIFICANCE STATEMENT: Neurons embedded in active neural networks can enter high-conductance states with irregular firing. This was previously shown for spinal motoneurons during scratching. Because scratching is highly specialized rhythmic behavior, it is not known...... whether high-conductance states and irregular firing are a peculiarity for motoneurons during scratching. Here, using intracellular recordings from motoneurons in an ex vivo carapace-spinal cord preparation from adult turtles, we demonstrate that irregular firing and high-conductance states are present...

  8. A model for the neural control of pineal periodicity

    Science.gov (United States)

    de Oliveira Cruz, Frederico Alan; Soares, Marilia Amavel Gomes; Cortez, Celia Martins

    2016-12-01

    The aim of this work was verify if a computational model associating the synchronization dynamics of coupling oscillators to a set of synaptic transmission equations would be able to simulate the control of pineal by a complex neural pathway that connects the retina to this gland. Results from the simulations showed that the frequency and temporal firing patterns were in the range of values found in literature.

  9. Analysis of meal patterns with the use of supervised data mining techniques--artificial neural networks and decision trees.

    Science.gov (United States)

    Hearty, Aine P; Gibney, Michael J

    2008-12-01

    At present, the analysis of dietary patterns is based on the intake of individual foods. This article demonstrates how a coding system at the meal level might be analyzed by using data mining techniques. The objective was to evaluate the usability of supervised data mining methods to predict an aspect of dietary quality based on dietary intake with a food-based coding system and a novel meal-based coding system. Food consumption databases from the North-South Ireland Food Consumption Survey 1997-1999 were used. This was a randomized cross-sectional study of 7-d recorded food and nutrient intakes of a representative sample of 1379 Irish adults. Meal definitions were recorded by the respondent. A healthy eating index (HEI) score was developed. Artificial neural networks (ANNs) and decision trees were used to predict quintiles of the HEI based on combinations of foods consumed at breakfast and main meals. This study applied both data mining techniques to the food and meal-based coding systems. The ANN had a slightly higher accuracy than did the decision tree in relation to its ability to predict HEI quintiles 1 and 5 based on the food coding system (78.7% compared with 76.9% and 71.9% compared with 70.1%, respectively). However, the decision tree had higher accuracies than did the ANN on the basis of the meal coding system (67.5% compared with 54.6% and 75.1% compared with 72.4%, respectively). ANNs and decision trees were successfully used to predict an aspect of dietary quality. However, further exploration of the use of ANNs and decision trees in dietary pattern analysis is warranted.

  10. Combining neural network models to predict spatial patterns of airborne pollutant accumulation in soils around an industrial point emission source.

    Science.gov (United States)

    Dimopoulos, Ioannis F; Tsiros, Ioannis X; Serelis, Konstantinos; Chronopoulou, Aikaterini

    2004-12-01

    Neural networks (NNs) have the ability to model a wide range of complex nonlinearities. A major disadvantage of NNs, however, is their instability, especially under conditions of sparse, noisy, and limited data sets. In this paper, different combining network methods are used to benefit from the existence of local minima and from the instabilities of NNs. A nonlinear k-fold cross-validation method is used to test the performance of the various networks and also to develop and select a set of networks that exhibits a low correlation of errors. The various NN models are applied to estimate the spatial patterns of atmospherically transported and deposited lead (Pb) in soils around an historical industrial air emission point source. It is shown that the resulting ensemble networks consistently give superior predictions compared with the individual networks because, for the ensemble networks, R2 values were found to be higher than 0.9 while, for the contributing individual networks, values for R2 ranged between 0.35 and 0.85. It is concluded that combining networks can be adopted as an important component in the application of artificial NN techniques in applied air quality studies.

  11. Detecting tactical patterns in basketball: comparison of merge self-organising maps and dynamic controlled neural networks.

    Science.gov (United States)

    Kempe, Matthias; Grunz, Andreas; Memmert, Daniel

    2015-01-01

    The soaring amount of data, especially spatial-temporal data, recorded in recent years demands for advanced analysis methods. Neural networks derived from self-organizing maps established themselves as a useful tool to analyse static and temporal data. In this study, we applied the merge self-organising map (MSOM) to spatio-temporal data. To do so, we investigated the ability of MSOM's to analyse spatio-temporal data and compared its performance to the common dynamical controlled network (DyCoN) approach to analyse team sport position data. The position data of 10 players were recorded via the Ubisense tracking system during a basketball game. Furthermore, three different pre-selected plays were recorded for classification. Following data preparation, the different nets were trained with the data of the first half. The training success of both networks was evaluated by achieved entropy. The second half of the basketball game was presented to both nets for automatic classification. Both approaches were able to present the trained data extremely well and to detect the pre-selected plays correctly. In conclusion, MSOMs are a useful tool to analyse spatial-temporal data, especially in team sports. By their direct inclusion of different time length of tactical patterns, they open up new opportunities within team sports.

  12. Using c-Jun to identify fear extinction learning-specific patterns of neural activity that are affected by single prolonged stress.

    Science.gov (United States)

    Knox, Dayan; Stanfield, Briana R; Staib, Jennifer M; David, Nina P; DePietro, Thomas; Chamness, Marisa; Schneider, Elizabeth K; Keller, Samantha M; Lawless, Caroline

    2017-12-29

    Neural circuits via which stress leads to disruptions in fear extinction is often explored in animal stress models. Using the single prolonged stress (SPS) model of post traumatic stress disorder and the immediate early gene (IEG) c-Fos as a measure of neural activity, we previously identified patterns of neural activity through which SPS disrupts extinction retention. However, none of these stress effects were specific to fear or extinction learning and memory. C-Jun is another IEG that is sometimes regulated in a different manner to c-Fos and could be used to identify emotional learning/memory specific patterns of neural activity that are sensitive to SPS. Animals were either fear conditioned (CS-fear) or presented with CSs only (CS-only) then subjected to extinction training and testing. C-Jun was then assayed within neural substrates critical for extinction memory. Inhibited c-Jun levels in the hippocampus (Hipp) and enhanced functional connectivity between the ventromedial prefrontal cortex (vmPFC) and basolateral amygdala (BLA) during extinction training was disrupted by SPS in the CS-fear group only. As a result, these effects were specific to emotional learning/memory. SPS also disrupted inhibited Hipp c-Jun levels, enhanced BLA c-Jun levels, and altered functional connectivity among the vmPFC, BLA, and Hipp during extinction testing in SPS rats in the CS-fear and CS-only groups. As a result, these effects were not specific to emotional learning/memory. Our findings suggest that SPS disrupts neural activity specific to extinction memory, but may also disrupt the retention of fear extinction by mechanisms that do not involve emotional learning/memory. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Effects of habitat characteristics and interspecific interactions on co-occurrence patterns of saproxylic beetles breeding in tree boles after forest fire: null model analyses.

    Science.gov (United States)

    Azeria, Ermias T; Ibarzabal, Jacques; Hébert, Christian

    2012-04-01

    It is often suggested that habitat attributes and interspecific interactions can cause non-random species co-occurrence patterns, but quantifying their contributions can be difficult. Null models that systematically exclude and include habitat effects can give information on the contribution of these factors to community assembly. In the boreal forest, saproxylic beetles are known to be attracted to recently burned forests where they breed in dead and dying trees. We examined whether species co-occurrences of saproxylic beetles that develop in, and emerge from, boles of recently burned trees show non-random patterns. We also estimated the extent to which both the post-fire habitat attributes and interspecific interactions among beetles contribute to such patterns. We sampled tree boles encompassing key attributes (tree species, tree size/dbh and burn severity) that are thought to characterize species-habitat associations of saproxylic beetles, a proposition that we tested using indicator species analysis. Two null models with no habitat constraints ("unconstrained") indicated that a total of 29.4% of the species pairs tested had significant co-occurrence patterns. Habitat-constrained null models indicated that most of the detected species aggregations (72%) and segregations (59%) can be explained by shared and distinct species-habitat relationships, respectively. The assembly pattern was also driven by interspecific interactions, of which some were modulated by habitat; for example, predator and prey species tended to co-occur in large-sized trees (a proxy of available bark/wood food resource primarily for the prey). In addition, some species segregation suggesting antagonistic, competitive, or prey-predator interactions were evident after accounting for the species' affinities for the same tree species. Overall, our results suggest that an intimate link between habitat and interspecific interactions can have important roles for community assembly of saproxylic

  14. Network-level accident-mapping: Distance based pattern matching using artificial neural network.

    Science.gov (United States)

    Deka, Lipika; Quddus, Mohammed

    2014-04-01

    The objective of an accident-mapping algorithm is to snap traffic accidents onto the correct road segments. Assigning accidents onto the correct segments facilitate to robustly carry out some key analyses in accident research including the identification of accident hot-spots, network-level risk mapping and segment-level accident risk modelling. Existing risk mapping algorithms have some severe limitations: (i) they are not easily 'transferable' as the algorithms are specific to given accident datasets; (ii) they do not perform well in all road-network environments such as in areas of dense road network; and (iii) the methods used do not perform well in addressing inaccuracies inherent in and type of road environment. The purpose of this paper is to develop a new accident mapping algorithm based on the common variables observed in most accident databases (e.g. road name and type, direction of vehicle movement before the accident and recorded accident location). The challenges here are to: (i) develop a method that takes into account uncertainties inherent to the recorded traffic accident data and the underlying digital road network data, (ii) accurately determine the type and proportion of inaccuracies, and (iii) develop a robust algorithm that can be adapted for any accident set and road network of varying complexity. In order to overcome these challenges, a distance based pattern-matching approach is used to identify the correct road segment. This is based on vectors containing feature values that are common in the accident data and the network data. Since each feature does not contribute equally towards the identification of the correct road segments, an ANN approach using the single-layer perceptron is used to assist in "learning" the relative importance of each feature in the distance calculation and hence the correct link identification. The performance of the developed algorithm was evaluated based on a reference accident dataset from the UK confirming that

  15. Lesser Neural Pattern Similarity across Repeated Tests Is Associated with Better Long-Term Memory Retention.

    Science.gov (United States)

    Karlsson Wirebring, Linnea; Wiklund-Hörnqvist, Carola; Eriksson, Johan; Andersson, Micael; Jonsson, Bert; Nyberg, Lars

    2015-07-01

    Encoding and retrieval processes enhance long-term memory performance. The efficiency of encoding processes has recently been linked to representational consistency: the reactivation of a representation that gets more specific each time an item is further studied. Here we examined the complementary hypothesis of whether the efficiency of retrieval processes also is linked to representational consistency. Alternatively, recurrent retrieval might foster representational variability--the altering or adding of underlying memory representations. Human participants studied 60 Swahili-Swedish word pairs before being scanned with fMRI the same day and 1 week later. On Day 1, participants were tested three times on each word pair, and on Day 7 each pair was tested once. A BOLD signal change in right superior parietal cortex was associated with subsequent memory on Day 1 and with successful long-term retention on Day 7. A representational similarity analysis in this parietal region revealed that beneficial recurrent retrieval was associated with representational variability, such that the pattern similarity on Day 1 was lower for retrieved words subsequently remembered compared with those subsequently forgotten. This was mirrored by a monotonically decreased BOLD signal change in dorsolateral prefrontal cortex on Day 1 as a function of repeated successful retrieval for words subsequently remembered, but not for words subsequently forgotten. This reduction in prefrontal response could reflect reduced demands on cognitive control. Collectively, the results offer novel insights into why memory retention benefits from repeated retrieval, and they suggest fundamental differences between repeated study and repeated testing. Repeated testing is known to produce superior long-term retention of the to-be-learned material compared with repeated encoding and other learning techniques, much because it fosters repeated memory retrieval. This study demonstrates that repeated memory

  16. Floristic patterns and disturbance history in karri ( Eucalyptus diversicolor: Myrtaceae) forest, south-western Australia: 2. Origin, growth form and fire response

    Science.gov (United States)

    Wardell-Johnson, Grant W.; Williams, M. R.; Mellican, A. E.; Annells, A.

    2007-03-01

    We examined the influence of disturbance history on the floristic composition of a single community type in karri forest, south-western Australia. Cover-abundance of 224 plant species and six disturbance and site-based environmental variables were recorded in 91, 20 m × 20 m quadrats. Numerical taxonomic and correlation approaches were used to relate these and 10 plant species-group variables based on origin, growth form and fire response. Ordination revealed no discernable pattern of sites based on floristic composition. However, all 10 species-group variables were significantly correlated with the ordination axes. Species richness within these groups varied with category and with respect to many of the disturbance and site variables. We encountered low diversity of vascular plants at the community level and limited diversity of growth forms. Thus most species were herbs (62.1%) or shrubs (30.3%), and there were no epiphytes and few species of trees or climbers. Although many introduced species were recorded (18.3% of all taxa), virtually all (83%) were herbs that demonstrated little persistence in the community, and there was limited evidence of transformer species. Time-since-fire (and other disturbance) influenced species richness more than the number of recent past fires because of a high proportion of ephemerals associated with the immediate post-fire period. Long-lived shrubs with soil stored seed dominate numerically, and in understorey biomass in comparison with neighboring vegetation types because of their greater flexibility of response following irregular, but intense disturbance events. However, interactions between nutrient status, regeneration mechanisms and community composition may be worthy of further investigation.

  17. Easy to Apply Polyoxazoline-Based Coating for Precise and Long-Term Control of Neural Patterns.

    Science.gov (United States)

    Weydert, Serge; Zürcher, Stefan; Tanner, Stefanie; Zhang, Ning; Ritter, Rebecca; Peter, Thomas; Aebersold, Mathias J; Thompson-Steckel, Greta; Forró, Csaba; Rottmar, Markus; Stauffer, Flurin; Valassina, Irene A; Morgese, Giulia; Benetti, Edmondo M; Tosatti, Samuele; Vörös, János

    2017-09-05

    Arranging cultured cells in patterns via surface modification is a tool used by biologists to answer questions in a specific and controlled manner. In the past decade, bottom-up neuroscience emerged as a new application, which aims to get a better understanding of the brain via reverse engineering and analyzing elementary circuitry in vitro. Building well-defined neural networks is the ultimate goal. Antifouling coatings are often used to control neurite outgrowth. Because erroneous connectivity alters the entire topology and functionality of minicircuits, the requirements are demanding. Current state-of-the-art coating solutions such as widely used poly(l-lysine)-g-poly(ethylene glycol) (PLL-g-PEG) fail to prevent primary neurons from making undesired connections in long-term cultures. In this study, a new copolymer with greatly enhanced antifouling properties is developed, characterized, and evaluated for its reliability, stability, and versatility. To this end, the following components are grafted to a poly(acrylamide) (PAcrAm) backbone: hexaneamine, to support spontaneous electrostatic adsorption in buffered aqueous solutions, and propyldimethylethoxysilane, to increase the durability via covalent bonding to hydroxylated culture surfaces and antifouling polymer poly(2-methyl-2-oxazoline) (PMOXA). In an assay for neural connectivity control, the new copolymer's ability to effectively prevent unwanted neurite outgrowth is compared to the gold standard, PLL-g-PEG. Additionally, its versatility is evaluated on polystyrene, glass, and poly(dimethylsiloxane) using primary hippocampal and cortical rat neurons as well as C2C12 myoblasts, and human fibroblasts. PAcrAm-g-(PMOXA, NH2, Si) consistently outperforms PLL-g-PEG with all tested culture surfaces and cell types, and it is the first surface coating which reliably prevents arranged nodes of primary neurons from forming undesired connections over the long term. Whereas the presented work focuses on the proof of

  18. Volatile Hydrocarbon Analysis in Blood by Headspace Solid-Phase Microextraction: The Interpretation of VHC Patterns in Fire-Related Incidents.

    Science.gov (United States)

    Waters, Brian; Hara, Kenji; Ikematsu, Natsuki; Takayama, Mio; Kashiwagi, Masayuki; Matsusue, Aya; Kubo, Shin-Ichi

    2017-05-01

    A headspace solid-phase microextraction (HS-SPME) technique was used to quantitate the concentration of volatile hydrocarbons from the blood of cadavers by cryogenic gas chromatography-mass spectroscopy. A total of 24 compounds including aromatic and aliphatic volatile hydrocarbons were analyzed by this method. The analytes in the headspace of 0.1 g of blood mixed with 1.0 mL of distilled water plus 1 µL of an internal standard solution were adsorbed onto a 100-µm polydimethylsiloxane fiber at 0°C for 15 min, and measured using a GC-MS full scan method. The limit of quantitation for the analytes ranged from 6.8 to 10 ng per 1 g of blood. This method was applied to actual autopsy cases to quantitate the level of volatile hydrocarbons (VHCs) in the blood of cadavers who died in fire-related incidents. The patterns of the VHCs revealed the presence or absence of accelerants. Petroleum-based fuels such as gasoline and kerosene were differentiated. The detection of C8-C13 aliphatic hydrocarbons indicated the presence of kerosene; the detection of C3 alkylbenzenes in the absence of C8-C13 aliphatic hydrocarbons was indicative of gasoline; and elevated levels of styrene or benzene in the absence of C3/C4 alkylbenzenes and aliphatic hydrocarbons indicated a normal construction fire. This sensitive HS-SPME method could help aid the investigation of fire-related deaths by providing a simple pattern to use for the interpretation of VHCs in human blood. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  19. GIAO C-H COSY Simulations Merged with Artificial Neural Networks Pattern Recognition Analysis. Pushing the Structural Validation a Step Forward.

    Science.gov (United States)

    Zanardi, María M; Sarotti, Ariel M

    2015-10-02

    The structural validation problem using quantum chemistry approaches (confirm or reject a candidate structure) has been tackled with artificial neural network (ANN) mediated multidimensional pattern recognition from experimental and calculated 2D C-H COSY. In order to identify subtle errors (such as regio- or stereochemical), more than 400 ANNs have been built and trained, and the most efficient in terms of classification ability were successfully validated in challenging real examples of natural product misassignments.

  20. SOX1 links the function of neural patterning and Notch signalling in the ventral spinal cord during the neuron-glial fate switch

    Energy Technology Data Exchange (ETDEWEB)

    Genethliou, Nicholas; Panayiotou, Elena [The Cyprus Institute of Neurology and Genetics, Airport Avenue, No. 6, Agios Dometios, 2370 Nicosia (Cyprus); Department of Biological Sciences, University of Cyprus, P.O. Box 20537, 1678 Nicosia (Cyprus); Panayi, Helen; Orford, Michael; Mean, Richard; Lapathitis, George; Gill, Herman; Raoof, Sahir [The Cyprus Institute of Neurology and Genetics, Airport Avenue, No. 6, Agios Dometios, 2370 Nicosia (Cyprus); Gasperi, Rita De; Elder, Gregory [James J. Peters VA Medical Center, Research and Development (3F22), 130 West Kingsbridge Road, Bronx, NY 10468 (United States); Kessaris, Nicoletta; Richardson, William D. [Wolfson Institute for Biomedical Research and Research Department of Cell and Developmental Biology, University College London, Gower Street, London WC1E 6BT (United Kingdom); Malas, Stavros, E-mail: smalas@cing.ac.cy [The Cyprus Institute of Neurology and Genetics, Airport Avenue, No. 6, Agios Dometios, 2370 Nicosia (Cyprus); Department of Biological Sciences, University of Cyprus, P.O. Box 20537, 1678 Nicosia (Cyprus)

    2009-12-25

    During neural development the transition from neurogenesis to gliogenesis, known as the neuron-glial ({Nu}/G) fate switch, requires the coordinated function of patterning factors, pro-glial factors and Notch signalling. How this process is coordinated in the embryonic spinal cord is poorly understood. Here, we demonstrate that during the N/G fate switch in the ventral spinal cord (vSC) SOX1 links the function of neural patterning and Notch signalling. We show that, SOX1 expression in the vSC is regulated by PAX6, NKX2.2 and Notch signalling in a domain-specific manner. We further show that SOX1 regulates the expression of Hes1 and that loss of Sox1 leads to enhanced production of oligodendrocyte precursors from the pMN. Finally, we show that Notch signalling functions upstream of SOX1 during this fate switch and is independently required for the acquisition of the glial fate perse by regulating Nuclear Factor I A expression in a PAX6/SOX1/HES1/HES5-independent manner. These data integrate functional roles of neural patterning factors, Notch signalling and SOX1 during gliogenesis.

  1. Fractal auditory-nerve firing patterns may derive from fractal switching in sensory hair-cell ion channels

    Science.gov (United States)

    Lowen, S. B.; Teich, M. C.

    1993-08-01

    Hair-cell ion channels, which provide a crucial link in the transformation of incoming acoustic information to neural action-potential trains, switch between open and closed states with power-law-distributed (fractal) dwell times. Trains of action potentials recorded from auditory nerves in mammals always exhibit fractal behavior, including a 1/f-type spectrum, for long time scales. We provide a mathematical model linking these two fractal behaviors within a common framework.

  2. The use of ATSR active fire counts for estimating relative patterns of biomass burning - A study from the boreal forest region

    NARCIS (Netherlands)

    Kasischke, Eric S.; Hewson, Jennifer H.; Stocks, Brian; van der Werf, Guido; Randerson, James T.

    2003-01-01

    Satellite fire products have the potential to construct inter-annual time series of fire activity, but estimating area burned requires considering biases introduced by orbiting geometry, fire behavior, and the presence of clouds and smoke. Here we evaluated the performance of fire counts from the

  3. Assessing neural tuning for object perception in schizophrenia and bipolar disorder with multivariate pattern analysis of fMRI data

    Directory of Open Access Journals (Sweden)

    Eric A. Reavis

    2017-01-01

    Conclusions: The results show for the first time MVPA can be used successfully to classify individual perceptual stimuli in schizophrenia and bipolar disorder. However, the results do not provide evidence of abnormal neural tuning in schizophrenia and bipolar disorder.

  4. Three Tctn proteins are functionally conserved in the regulation of neural tube patterning and Gli3 processing but not ciliogenesis and Hedgehog signaling in the mouse.

    Science.gov (United States)

    Wang, Chengbing; Li, Jia; Meng, Qing; Wang, Baolin

    2017-10-01

    Tctn1, Tctn2, and Tctn3 are membrane proteins that localize at the transition zone of primary cilia. Tctn1 and Tctn2 mutations have been reported in both humans and mice, but Tctn3 mutations have been reported only in humans. It is also not clear whether the three Tctn proteins are functionally conserved with respect to ciliogenesis and Hedgehog (Hh) signaling. In the present study, we report that loss of Tctn3 gene function in mice results in a decrease in ciliogenesis and Hh signaling. Consistent with this, Tctn3 mutant mice exhibit holoprosencephaly and randomized heart looping and lack the floor plate in the neural tube, the phenotypes similar to those of Tctn1 and Tctn2 mutants. We also show that overexpression of Tctn3, but not Tctn1 or Tctn2, can rescue ciliogenesis in Tctn3 mutant cells. Similarly, replacement of Tctn3 with Tctn1 or Tctn2 in the Tctn3 gene locus results in reduced ciliogenesis and Hh signaling, holoprosencephaly, and randomized heart looping. Surprisingly, however, the neural tube patterning and the proteolytic processing of Gli3 (a transcription regulator for Hh signaling) into a repressor, both of which are usually impaired in ciliary gene mutants, are normal. These results suggest that Tctn1, Tctn2, and Tctn3 are functionally divergent with respect to their role in ciliogenesis and Hh signaling but conserved in neural tube patterning and Gli3 processing. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Cross-scale analysis of fire regimes

    Science.gov (United States)

    Donald A. Falk; Carol Miller; Donald McKenzie; Anne E. Black

    2007-01-01

    Cross-scale spatial and temporal perspectives are important for studying contagious landscape disturbances such as fire, which are controlled by myriad processes operating at different scales. We examine fire regimes in forests of western North America, focusing on how observed patterns of fire frequency change across spatial scales. To quantify changes in fire...

  6. Genetic control of active neural circuits

    Directory of Open Access Journals (Sweden)

    Leon Reijmers

    2009-12-01

    Full Text Available The use of molecular tools to study the neurobiology of complex behaviors has been hampered by an inability to target the desired changes to relevant groups of neurons. Specific memories and specific sensory representations are sparsely encoded by a small fraction of neurons embedded in a sea of morphologically and functionally similar cells. In this review we discuss genetics techniques that are being developed to address this difficulty. In several studies the use of promoter elements that are responsive to neural activity have been used to drive long lasting genetic alterations into neural ensembles that are activated by natural environmental stimuli. This approach has been used to examine neural activity patterns during learning and retrieval of a memory, to examine the regulation of receptor trafficking following learning and to functionally manipulate a specific memory trace. We suggest that these techniques will provide a general approach to experimentally investigate the link between patterns of environmentally activated neural firing and cognitive processes such as perception and memory.

  7. Neural mechanisms of sequence generation in songbirds

    Science.gov (United States)

    Langford, Bruce

    Animal models in research are useful for studying more complex behavior. For example, motor sequence generation of actions requiring good muscle coordination such as writing with a pen, playing an instrument, or speaking, may involve the interaction of many areas in the brain, each a complex system in itself; thus it can be difficult to determine causal relationships between neural behavior and the behavior being studied. Birdsong, however, provides an excellent model behavior for motor sequence learning, memory, and generation. The song consists of learned sequences of notes that are spectrographically stereotyped over multiple renditions of the song, similar to syllables in human speech. The main areas of the songbird brain involve in singing are known, however, the mechanisms by which these systems store and produce song are not well understood. We used a custom built, head-mounted, miniature motorized microdrive to chronically record the neural firing patterns of identified neurons in HVC, a pre-motor cortical nucleus which has been shown to be important in song timing. These were done in Bengalese finch which generate a song made up of stereotyped notes but variable note sequences. We observed song related bursting in neurons projecting to Area X, a homologue to basal ganglia, and tonic firing in HVC interneurons. Interneuron had firing rate patterns that were consistent over multiple renditions of the same note sequence. We also designed and built a light-weight, low-powered wireless programmable neural stimulator using Bluetooth Low Energy Protocol. It was able to generate perturbations in the song when current pulses were administered to RA, which projects to the brainstem nucleus responsible for syringeal muscle control.

  8. Spatiotemporal canards in neural field equations

    Science.gov (United States)

    Avitabile, D.; Desroches, M.; Knobloch, E.

    2017-04-01

    Canards are special solutions to ordinary differential equations that follow invariant repelling slow manifolds for long time intervals. In realistic biophysical single-cell models, canards are responsible for several complex neural rhythms observed experimentally, but their existence and role in spatially extended systems is largely unexplored. We identify and describe a type of coherent structure in which a spatial pattern displays temporal canard behavior. Using interfacial dynamics and geometric singular perturbation theory, we classify spatiotemporal canards and give conditions for the existence of folded-saddle and folded-node canards. We find that spatiotemporal canards are robust to changes in the synaptic connectivity and firing rate. The theory correctly predicts the existence of spatiotemporal canards with octahedral symmetry in a neural field model posed on the unit sphere.

  9. Evidence of fuels management and fire weather influencing fire severity in an extreme fire event

    Science.gov (United States)

    Lydersen, Jamie M; Collins, Brandon M.; Brooks, Matthew L.; Matchett, John R.; Shive, Kristen L.; Povak, Nicholas A.; Kane, Van R.; Smith, Douglas F.

    2017-01-01

    Following changes in vegetation structure and pattern, along with a changing climate, large wildfire incidence has increased in forests throughout the western U.S. Given this increase there is great interest in whether fuels treatments and previous wildfire can alter fire severity patterns in large wildfires. We assessed the relative influence of previous fuels treatments (including wildfire), fire weather, vegetation and water balance on fire severity in the Rim Fire of 2013. We did this at three different spatial scales to investigate whether the influences on fire severity changed across scales. Both fuels treatments and previous low to moderate severity wildfire reduced the prevalence of high severity fire. In general, areas without recent fuels treatments and areas that previously burned at high severity tended to have a greater proportion of high severity fire in the Rim Fire. Areas treated with prescribed fire, especially when combined with thinning, had the lowest proportions of high severity. Proportion of the landscape burned at high severity was most strongly influenced by fire weather and proportional area previously treated for fuels or burned by low to moderate severity wildfire. The proportion treated needed to effectively reduce the amount of high fire severity fire varied by spatial scale of analysis, with smaller spatial scales requiring a greater proportion treated to see an effect on fire severity. When moderate and high severity fire encountered a previously treated area, fire severity was significantly reduced in the treated area relative to the adjacent untreated area. Our results show that fuels treatments and low to moderate severity wildfire can reduce fire severity in a subsequent wildfire, even when burning under fire growth conditions. These results serve as further evidence that both fuels treatments and lower severity wildfire can increase forest resilience.

  10. Fire safety

    Science.gov (United States)

    Robert H. White; Mark A. Dietenberger

    1999-01-01

    Fire safety is an important concern in all types of construction. The high level of national concern for fire safety is reflected in limitations and design requirements in building codes. These code requirements are discussed in the context of fire safety design and evaluation in the initial section of this chapter. Since basic data on fire behavior of wood products...

  11. Achieving Consistent Near-Optimal Pattern Recognition Accuracy Using Particle Swarm Optimization to Pre-Train Artificial Neural Networks

    Science.gov (United States)

    Nikelshpur, Dmitry O.

    2014-01-01

    Similar to mammalian brains, Artificial Neural Networks (ANN) are universal approximators, capable of yielding near-optimal solutions to a wide assortment of problems. ANNs are used in many fields including medicine, internet security, engineering, retail, robotics, warfare, intelligence control, and finance. "ANNs have a tendency to get…

  12. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

    1996-12-31

    The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

  13. Complexity of VTA DA neural activities in response to PFC transection in nicotine treated rats

    Directory of Open Access Journals (Sweden)

    Akay Yasemin M

    2011-02-01

    Full Text Available Abstract Background The dopaminergic (DA neurons in the ventral tegmental area (VTA are widely implicated in the addiction and natural reward circuitry of the brain. These neurons project to several areas of the brain, including prefrontal cortex (PFC, nucleus accubens (NAc and amygdala. The functional coupling between PFC and VTA has been demonstrated, but little is known about how PFC mediates nicotinic modulation in VTA DA neurons. The objectives of this study were to investigate the effect of acute nicotine exposure on the VTA DA neuronal firing and to understand how the disruption of communication from PFC affects the firing patterns of VTA DA neurons. Methods Extracellular single-unit recordings were performed on Sprague-Dawley rats and nicotine was administered after stable recording was established as baseline. In order to test how input from PFC affects the VTA DA neuronal firing, bilateral transections were made immediate caudal to PFC to mechanically delete the interaction between VTA and PFC. Results The complexity of the recorded neural firing was subsequently assessed using a method based on the Lempel-Ziv estimator. The results were compared with those obtained when computing the entropy of neural firing. Exposure to nicotine triggered a significant increase in VTA DA neurons firing complexity when communication between PFC and VTA was present, while transection obliterated the effect of nicotine. Similar results were obtained when entropy values were estimated. Conclusions Our findings suggest that PFC plays a vital role in mediating VTA activity. We speculate that increased firing complexity with acute nicotine administration in PFC intact subjects is due to the close functional coupling between PFC and VTA. This hypothesis is supported by the fact that deletion of PFC results in minor alterations of VTA DA neural firing when nicotine is acutely administered.

  14. Bergwind fires and the location pattern of forest patches in the southern cape landscape, South-Africa

    CSIR Research Space (South Africa)

    Geldenhuys, CJ

    1994-01-01

    Full Text Available in the dissected mountains. Late arrival of europeans in the area due to many deep and steep gorges through the coastal platform, and the early control over timber cutting and forest clearing prevented man from influencing the location pattern of the forests...

  15. Thinning and prescribed fire effects on snag abundance and spatial pattern in an eastern Cascade Range dry forest, Washington, USA

    Science.gov (United States)

    Paul F. Hessburg; Nicholas A. Povak; R. Brion. Salter

    2010-01-01

    Mechanical thinning and prescribed burning practices are commonly used to address tree stocking, spacing, composition, and canopy and surface fuel conditions in western US mixed conifer forests. We examined the effects of these fuel treatments alone and combined on snag abundance and spatial pattern across 12 10-ha treatment units in central Washington State. A snag...

  16. Evidence of fuels management and fire weather influencing fire severity in an extreme fire event.

    Science.gov (United States)

    Lydersen, Jamie M; Collins, Brandon M; Brooks, Matthew L; Matchett, John R; Shive, Kristen L; Povak, Nicholas A; Kane, Van R; Smith, Douglas F

    2017-10-01

    Following changes in vegetation structure and pattern, along with a changing climate, large wildfire incidence has increased in forests throughout the western United States. Given this increase, there is great interest in whether fuels treatments and previous wildfire can alter fire severity patterns in large wildfires. We assessed the relative influence of previous fuels treatments (including wildfire), fire weather, vegetation, and water balance on fire-severity in the Rim Fire of 2013. We did this at three different spatial scales to investigate whether the influences on fire severity changed across scales. Both fuels treatments and previous low to moderate-severity wildfire reduced the prevalence of high-severity fire. In general, areas without recent fuels treatments and areas that previously burned at high severity tended to have a greater proportion of high-severity fire in the Rim Fire. Areas treated with prescribed fire, especially when combined with thinning, had the lowest proportions of high severity. The proportion of the landscape burned at high severity was most strongly influenced by fire weather and proportional area previously treated for fuels or burned by low to moderate severity wildfire. The proportion treated needed to effectively reduce the amount of high severity fire varied by spatial scale of analysis, with smaller spatial scales requiring a greater proportion treated to see an effect on fire severity. When moderate and high-severity fire encountered a previously treated area, fire severity was significantly reduced in the treated area relative to the adjacent untreated area. Our results show that fuels treatments and low to moderate-severity wildfire can reduce fire severity in a subsequent wildfire, even when burning under fire growth conditions. These results serve as further evidence that both fuels treatments and lower severity wildfire can increase forest resilience. © 2017 by the Ecological Society of America.

  17. Organization of the pallium in the fire-bellied toad Bombina orientalis. I: Morphology and axonal projection pattern of neurons revealed by intracellular biocytin labeling.

    Science.gov (United States)

    Roth, Gerhard; Laberge, Frédéric; Mühlenbrock-Lenter, Sabine; Grunwald, Wolfgang

    2007-03-20

    The cytoarchitecture and axonal projection pattern of pallial areas was studied in the fire-bellied toad Bombina orientalis by intracellular injection of biocytin into a total of 326 neurons forming 204 clusters. Five pallial regions were identified, differing in morphology and projection pattern of neurons. The rostral pallium receiving the bulk of dorsal thalamic afferents has reciprocal connections with all other pallial areas and projects to the septum, nucleus accumbens, and anterior dorsal striatum. The medial pallium projects bilaterally to the medial pallium, septum, nucleus accumbens, mediocentral amygdala, and hypothalamus and ipsilaterally to the rostral, dorsal, and lateral pallium. The ventral part of the medial pallium is distinguished by efferents to the eminentia thalami and the absence of contralateral projections. The dorsal pallium has only ipsilateral projections running to the rostral, medial, and lateral pallium; septum; nucleus accumbens; and eminentia thalami. The lateral pallium has ipsilateral projections to the olfactory bulbs and to the rostral, medial, dorsal, and ventral pallium. The ventral pallium including the striatopallial transition area (SPTA) has ipsilateral projections to the olfactory bulbs, rostral and lateral pallium, dorsal striatopallidum, vomeronasal amygdala, and hypothalamus. The medial pallium can be tentatively homologized with the mammalian hippocampal formation, the dorsal pallium with allocortical areas, the lateral pallium rostrally with the piriform and caudally with the entorhinal cortex, the ventral pallium with the accessory olfactory amygdala. The rostral pallium, with its projections to the dorsal and ventral striatopallidum, resembles the mammalian frontal cortex. 2007 Wiley-Liss, Inc.

  18. MODIS NDVI Response Following Fires in Siberia

    Science.gov (United States)

    Ranson, K. Jon; Sun, G.; Kovacs, K.; Kharuk, V. I.

    2003-01-01

    The Siberian boreal forest is considered a carbon sink but may become an important source of carbon dioxide if climatic warming predictions are correct. The forest is continually changing through various disturbance mechanisms such as insects, logging, mineral exploitation, and especially fires. Patterns of disturbance and forest recovery processes are important factors regulating carbon flux in this area. NASA's Terra MODIS provides useful information for assessing location of fires and post fire changes in forests. MODIS fire (MOD14), and NDVI (MOD13) products were used to examine fire occurrence and post fire variability in vegetation cover as indicated by NDVI. Results were interpreted for various post fire outcomes, such as decreased NDVI after fire, no change in NDVI after fire and positive NDVI change after fire. The fire frequency data were also evaluated in terms of proximity to population centers, and transportation networks.

  19. Fire ecology of the forest habitat types of northern Idaho

    Science.gov (United States)

    Jane Kapler Smith; William C. Fischer

    1997-01-01

    Provides information on fire ecology in forest habitat and community types occurring in northern Idaho. Identifies fire groups based on presettlement fire regimes and patterns of succession and stand development after fire. Describes forest fuels and suggests considerations for fire management.

  20. Climatic and weather factors affecting fire occurrence and behavior

    Science.gov (United States)

    Randall P. Benson; John O. Roads; David R. Weise

    2009-01-01

    Weather and climate have a profound influence on wildland fire ignition potential, fire behavior, and fire severity. Local weather and climate are affected by large-scale patterns of winds over the hemispheres that predispose wildland fuels to fire. The characteristics of wildland fuels, especially the moisture content, ultimately determine fire behavior and the impact...

  1. Shift in fire-ecosystems and weather changes

    Science.gov (United States)

    Bongani Finiza

    2013-01-01

    During recent decades too much focus fell on fire suppression and fire engineering methods. Little attention has been given to understanding the shift in the changing fire weather resulting from the global change in weather patterns. Weather change have gradually changed the way vegetation cover respond to fire occurrence and brought about changes in fire behavior and...

  2. Neural precursors exhibit distinctly different patterns of cell migration upon transplantation during either the acute or chronic phase of EAE: a serial MR imaging study.

    Science.gov (United States)

    Muja, Naser; Cohen, Mikhal E; Zhang, Jiangyang; Kim, Heechul; Gilad, Assaf A; Walczak, Piotr; Ben-Hur, Tamir; Bulte, Jeff W M

    2011-06-01

    As the complex pathogenesis of multiple sclerosis contributes to spatiotemporal variations in the trophic micromilieu of the central nervous system, the optimal intervention period for cell-replacement therapy must be systematically defined. We applied serial, 3D high-resolution magnetic resonance imaging to transplanted neural precursor cells (NPCs) labeled with superparamagnetic iron oxide nanoparticles and 5-bromo-2-deoxyuridine, and compared the migration pattern of NPCs in acute inflamed (n = 10) versus chronic demyelinated (n = 9) brains of mice induced with experimental allergic encephalomyelitis (EAE). Serial in vivo and ex-vivo 3D magnetic resonance imaging revealed that NPCs migrated 2.5 ± 1.3 mm along the corpus callosum in acute EAE. In chronic EAE, cell migration was slightly reduced (2.3 ± 1.3 mm) and only occurred in the lateral side of transplantation. Surprisingly, in 6/10 acute EAE brains, NPCs were found to migrate in a radial pattern along RECA-1(+) cortical blood vessels, in a pattern hitherto only reported for migrating glioblastoma cells. This striking radial biodistribution pattern was not detected in either chronic EAE or disease-free control brains. In both acute and chronic EAE brain, Iba1(+) microglia/macrophage number was significantly higher in central nervous system regions containing migrating NPCs. The existence of differential NPC migration patterns is an important consideration for implementing future translational studies in multiple sclerosis patients with variable disease. Copyright © 2011 Wiley-Liss, Inc.

  3. The Xenopus Irx genes are essential for neural patterning and define the border between prethalamus and thalamus through mutual antagonism with the anterior repressors Fezf and Arx.

    Science.gov (United States)

    Rodríguez-Seguel, Elisa; Alarcón, Pilar; Gómez-Skarmeta, José Luis

    2009-05-15

    The Iroquois (Irx) genes encode homeoproteins conserved during evolution. Vertebrate genomes contain six Irx genes organized in two clusters, IrxA (which harbors Irx1, Irx2 and Irx4) and IrxB (which harbors Irx3, Irx5 and Irx6). To determine the precise role of these genes during development and their putative redundancies, we conducted a comparative expression analysis and a comprehensive loss-of-function study of all the early expressed Irx genes (Irx1-5) using specific morpholinos in Xenopus. We found that the five Irx genes display largely overlapping expression patterns and contribute to neural patterning. All Irx genes are required for proper formation of posterior forebrain, midbrain, hindbrain and, to a lesser an extent, spinal cord. Nevertheless, Irx1 and Irx3 seem to have a predominant role during regionalization of the neural plate. In addition, we find that the common anterior limit of Irx gene expression, which will correspond to the future border between the prethalamus and thalamus, is defined by mutual repression between Fezf and Irx proteins. This mutual repression is likely direct. Finally, we show that Arx, another anteriorly expressed repressor, also contribute to delineate the anterior border of Irx expression.

  4. Pattern Recognition of Momentary Mental Workload Based on Multi-Channel Electrophysiological Data and Ensemble Convolutional Neural Networks.

    Science.gov (United States)

    Zhang, Jianhua; Li, Sunan; Wang, Rubin

    2017-01-01

    In this paper, we deal with the Mental Workload (MWL) classification problem based on the measured physiological data. First we discussed the optimal depth (i.e., the number of hidden layers) and parameter optimization algorithms for the Convolutional Neural Networks (CNN). The base CNNs designed were tested according to five classification performance indices, namely Accuracy, Precision, F-measure, G-mean, and required training time. Then we developed an Ensemble Convolutional Neural Network (ECNN) to enhance the accuracy and robustness of the individual CNN model. For the ECNN design, three model aggregation approaches (weighted averaging, majority voting and stacking) were examined and a resampling strategy was used to enhance the diversity of individual CNN models. The results of MWL classification performance comparison indicated that the proposed ECNN framework can effectively improve MWL classification performance and is featured by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.

  5. Pattern Recognition of Momentary Mental Workload Based on Multi-Channel Electrophysiological Data and Ensemble Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Jianhua Zhang

    2017-05-01

    Full Text Available In this paper, we deal with the Mental Workload (MWL classification problem based on the measured physiological data. First we discussed the optimal depth (i.e., the number of hidden layers and parameter optimization algorithms for the Convolutional Neural Networks (CNN. The base CNNs designed were tested according to five classification performance indices, namely Accuracy, Precision, F-measure, G-mean, and required training time. Then we developed an Ensemble Convolutional Neural Network (ECNN to enhance the accuracy and robustness of the individual CNN model. For the ECNN design, three model aggregation approaches (weighted averaging, majority voting and stacking were examined and a resampling strategy was used to enhance the diversity of individual CNN models. The results of MWL classification performance comparison indicated that the proposed ECNN framework can effectively improve MWL classification performance and is featured by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.

  6. Fire water

    Energy Technology Data Exchange (ETDEWEB)

    Thorpe, K. [Lawrence Webster Forrest Ltd. (United Kingdom)

    2001-01-01

    The article focuses on the value of water in fighting fires and discusses why refineries should identify water supply and distribution in contingency planning against fire. In the event of a fire, water will be required for (i) extinguishing the fire; (ii) protection of equipment and (iii) confinement of the fire. The thought process for identifying the water demand in the event of a fire is outlined. Tables give data on (a) water rates for cooling storage tanks; (b) water rates for cooling process units (c) guide to water requirements for various sizes of process units and (d) pumping requirements.

  7. A Decline in Response Variability Improves Neural Signal Detection during Auditory Task Performance.

    Science.gov (United States)

    von Trapp, Gardiner; Buran, Bradley N; Sen, Kamal; Semple, Malcolm N; Sanes, Dan H

    2016-10-26

    The detection of a sensory stimulus arises from a significant change in neural activity, but a sensory neuron's response is rarely identical to successive presentations of the same stimulus. Large trial-to-trial variability would limit the central nervous system's ability to reliably detect a stimulus, presumably affecting perceptual performance. However, if response variability were to decrease while firing rate remained constant, then neural sensitivity could improve. Here, we asked whether engagement in an auditory detection task can modulate response variability, thereby increasing neural sensitivity. We recorded telemetrically from the core auditory cortex of gerbils, both while they engaged in an amplitude-modulation detection task and while they sat quietly listening to the identical stimuli. Using a signal detection theory framework, we found that neural sensitivity was improved during task performance, and this improvement was closely associated with a decrease in response variability. Moreover, units with the greatest change in response variability had absolute neural thresholds most closely aligned with simultaneously measured perceptual thresholds. Our findings suggest that the limitations imposed by response variability diminish during task performance, thereby improving the sensitivity of neural encoding and potentially leading to better perceptual sensitivity. The detection of a sensory stimulus arises from a significant change in neural activity. However, trial-to-trial variability of the neural response may limit perceptual performance. If the neural response to a stimulus is quite variable, then the response on a given trial could be confused with the pattern of neural activity generated when the stimulus is absent. Therefore, a neural mechanism that served to reduce response variability would allow for better stimulus detection. By recording from the cortex of freely moving animals engaged in an auditory detection task, we found that variability

  8. Fire Stations

    Data.gov (United States)

    Department of Homeland Security — Fire Stations in the United States Any location where fire fighters are stationed or based out of, or where equipment that such personnel use in carrying out their...

  9. Evaluating the predictability of PM10 grades in Seoul, Korea using a neural network model based on synoptic patterns.

    Science.gov (United States)

    Hur, Sun-Kyong; Oh, Hye-Ryun; Ho, Chang-Hoi; Kim, Jinwon; Song, Chang-Keun; Chang, Lim-Seok; Lee, Jae-Bum

    2016-11-01

    As of November 2014, the Korean Ministry of Environment (KME) has been forecasting the concentration of particulate matter with diameters ≤ 10 μm (PM10) classified into four grades: low (PM10 ≤ 30 μg m(-3)), moderate (30  150 μg m(-3)). The KME operational center generates PM10 forecasts using statistical and chemistry-transport models, but the overall performance and the hit rate for the four PM10 grades has not previously been evaluated. To provide a statistical reference for the current air quality forecasting system, we have developed a neural network model based on the synoptic patterns of several meteorological fields such as geopotential height, air temperature, relative humidity, and wind. Hindcast of the four PM10 grades in Seoul, Korea was performed for the cold seasons (October-March) of 2001-2014 when the high and very high PM10 grades are frequently observed. Because synoptic patterns of the meteorological fields are distinctive for each PM10 grade, these fields were adopted and quantified as predictors in the form of cosine similarities to train the neural network model. Using these predictors in conjunction with the PM10 concentration in Seoul from the day before prediction as an additional predictor, an overall hit rate of 69% was achieved; the hit rates for the low, moderate, high, and very high PM10 grades were 33%, 83%, 45%, and 33%, respectively. Our findings also suggest that the synoptic patterns of meteorological variables are reliable predictors for the identification of the favorable conditions for each PM10 grade, as well as for the transboundary transport of PM10 from China. This evaluation of PM10 predictability can be reliably used as a statistical reference and further, complement to the current air quality forecasting system. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. The role of the mesenchyme in mouse neural fold elevation. II. Patterns of hyaluronate synthesis and distribution in embryos developing in vitro

    Energy Technology Data Exchange (ETDEWEB)

    Morris-Wiman, J.; Brinkley, L.L. (Univ. of Michigan Medical School, Ann Arbor (USA))

    1990-06-01

    Hyaluronate (HA) distribution patterns were examined in the cranial mesenchyme underlying the mesencephalic neural folds of mouse embryos maintained in roller tube culture. Using standard image-processing techniques, the digitized images of Alcian blue-stained or 3H-glucosamine-labeled sections digested with an enzyme specific for HA, were subtracted from adjacent, undigested sections. The resultant difference picture images (DPI) accurately depicted the distribution of stained or labeled HA within the cranial mesenchyme. 3H-glucosamine-labeled HA was distributed uniformly throughout the cranial mesenchyme as 12, 18, and 24 hr of culture. By contrast, the mesenchyme was uniformly stained with Alcian blue at 12 hr, but stain intensity decreased in the central regions of the mesenchyme at 18 and 24 hr. HA distribution patterns were also examined in the cranial mesenchyme of embryos cultured in the presence of diazo-oxo-norleucine (DON), a glutamine analogue that inhibits glycosaminoglycan and glycoprotein synthesis. In DON-treated mesenchyme, Alcian blue staining of HA was decreased from that in controls at 12, 18, and 24 hr. However, incorporation of 3H-glucosamine into HA was increased. The distribution of labeled HA within treated mesenchyme as 12, 18, and 24 hr resembled that in controls at 12 hr. These results indicate that the distribution of HA within the cranial mesenchyme of normal mouse embryos during neural fold elevation and convergence is not determined solely by regional differences in HA synthesis. We propose that HA distribution patterns result from the expansion of the HA-rich extracellular matrix of the central mesenchyme regions. This expansion may play a major role in fold elevation. These results also suggest that DON treatment reversibly inhibits HA synthesis.

  11. Effect of broadcast baiting on abundance patterns of red imported fire ants (Hymenoptera: Formicidae) and key local ant genera at long-term monitoring sites in Brisbane, Australia.

    Science.gov (United States)

    McNaught, Melinda K; Wylie, F Ross; Harris, Evan J; Alston, Clair L; Burwell, Chris J; Jennings, Craig

    2014-08-01

    In 2001, the red imported fire ant (Solenopsis invicta Buren) was identified in Brisbane, Australia. An eradication program involving broadcast bait treatment with two insect growth regulators and a metabolic inhibitor began in September of that year and is currently ongoing. To gauge the impacts of these treatments on local ant populations, we examined long-term monitoring data and quantified abundance patterns of S. invicta and common local ant genera using a linear mixed-effects model. For S. invicta, presence in pitfalls reduced over time to zero on every site. Significantly higher numbers of S. invicta workers were collected on high-density polygyne sites, which took longer to disinfest compared with monogyne and low-density polygyne sites. For local ants, nine genus groups of the 10 most common genera analyzed either increased in abundance or showed no significant trend. Five of these genus groups were significantly less abundant at the start of monitoring on high-density polygyne sites compared with monogyne and low-density polygyne sites. The genus Pheidole significantly reduced in abundance over time, suggesting that it was affected by treatment efforts. These results demonstrate that the treatment regime used at the time successfully removed S. invicta from these sites in Brisbane, and that most local ant genera were not seriously impacted by the treatment. These results have important implications for current and future prophylactic treatment efforts, and suggest that native ants remain in treated areas to provide some biological resistance to S. invicta.

  12. Grid cell firing patterns may arise from feedback interaction between intrinsic rebound spiking and transverse travelling waves with multiple heading angles

    Directory of Open Access Journals (Sweden)

    Michael E Hasselmo

    2014-10-01

    Full Text Available This article presents a model using cellular resonance and rebound properties to model grid cells in medial entorhinal cortex. The model simulates the intrinsic resonance properties of single layer II stellate cells with different frequencies due to the hyperpolarization activated cation current (h current. The stellate cells generate rebound spikes after a delay interval that differs for neurons with different resonance frequency. Stellate cells drive inhibitory interneurons to cause rebound from inhibition in an alternating set of stellate cells that drive interneurons to activate the first set of cells. This allows maintenance of activity with cycle skipping of the spiking of cells that matches recent physiological data on theta cycle skipping. The rebound spiking interacts with subthreshold oscillatory input to stellate cells or interneurons regulated by medial septal input and defined relative to the spatial location coded by neurons. The timing of rebound determines whether the network maintains the activity for the same location or shifts to phases of activity representing a different location. Simulations show that spatial firing patterns similar to grid cells can be generated with a range of different resonance frequencies, indicating how grid cells could be generated with low frequencies present in bats and in mice with knockout of the HCN1 subunit of the h current.

  13. Tip Deflection Determination of a Barrel for the Effect of an Accelerating Projectile Before Firing Using Finite Element and Artificial Neural Network Combined Algorithm

    Directory of Open Access Journals (Sweden)

    Mehmet Akif Koç

    Full Text Available Abstract For realistic applications, design and control engineers have limited modelling options in dealing with some vibration problems that hold many nonlinearity such as non-uniform geometry, variable velocity loadings, indefinite damping cases, etc. For these reasons numerous time consuming experimental studies at high costs must be done for determining the actual behaviour such nonlinear systems. However, using advantages of multiple computational methods like Finite Element Method (FEM together with an Artificial Intelligence (ANN, many complicated engineering problems can be handled and solved to some extent. This study, proposes a new collective method to deal with the nonlinear vibrations of the barrels in order to fulfil accurate shooting expectancy. Using known analytical methods, in practical, to determine dynamic behaviour of the barrel beam is not possible for all conditions of firing that include numerous varieties of ammunition for different purposes, and each projectile of different ammunition has different mass and exit velocity. In order to cover all cases this study proposes a new method that combines a precise FEM with ANN, and can be used for determining the exact dynamic behaviour of a barrel for some cases and then for precisely predicting the behaviour for all other possible cases of firing. In this study, the whole nonlinear behaviour of an antiaircraft barrel were obtained with 3.5% accuracy errors by ANN trained by FEM using calculated analysis results of ammunitions for a particular range. The proposed FEM-ANN combined method can be very useful for design and control engineers in design and control of barrels in order to compensate the effect of nonlinear vibrations of a barrel for achieving a higher shooting accuracy; and can reduce high-cost experimental works.

  14. US Fire Administration Fire Statistics

    Data.gov (United States)

    Department of Homeland Security — The U.S. Fire Administration collects data from a variety of sources to provide information and analyses on the status and scope of the fire problem in the United...

  15. A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells.

    Science.gov (United States)

    Boland, M V; Murphy, R F

    2001-12-01

    Assessment of protein subcellular location is crucial to proteomics efforts since localization information provides a context for a protein's sequence, structure, and function. The work described below is the first to address the subcellular localization of proteins in a quantitative, comprehensive manner. Images for ten different subcellular patterns (including all major organelles) were collected using fluorescence microscopy. The patterns were described using a variety of numeric features, including Zernike moments, Haralick texture features, and a set of new features developed specifically for this purpose. To test the usefulness of these features, they were used to train a neural network classifier. The classifier was able to correctly recognize an average of 83% of previously unseen cells showing one of the ten patterns. The same classifier was then used to recognize previously unseen sets of homogeneously prepared cells with 98% accuracy. Algorithms were implemented using the commercial products Matlab, S-Plus, and SAS, as well as some functions written in C. The scripts and source code generated for this work are available at http://murphylab.web.cmu.edu/software. murphy@cmu.edu

  16. Subalpine vegetation pattern three decades after stand-replacing fire: Effects of landscape context and topography on plant community composition, tree regeneration, and diversity

    Science.gov (United States)

    Jonathan D. Coop; Robert T. Massatti; Anna W. Schoettle

    2010-01-01

    These subalpine wildfires generated considerable, persistent increases in plant species richness at local and landscape scales, and a diversity of plant communities. The findings suggest that fire suppression in such systems must lead to reduced diversity. Concerns about post-fire invasion by exotic plants appear unwarranted in high-elevation wilderness settings.

  17. Investigating the association between weather conditions, calendar events and socio-economic patterns with trends in fire incidence: an Australian case study

    Science.gov (United States)

    Corcoran, Jonathan; Higgs, Gary; Rohde, David; Chhetri, Prem

    2011-06-01

    Fires in urban areas can cause significant economic, physical and psychological damage. Despite this, there has been a comparative lack of research into the spatial and temporal analysis of fire incidence in urban contexts. In this paper, we redress this gap through an exploration of the association of fire incidence to weather, calendar events and socio-economic characteristics in South-East Queensland, Australia using innovative technique termed the quad plot. Analysing trends in five fire incident types, including malicious false alarms (hoax calls), residential buildings, secondary (outdoor), vehicle and suspicious fires, results suggest that risk associated with all is greatly increased during school holidays and during long weekends. For all fire types the lowest risk of incidence was found to occur between one and six a.m. It was also found that there was a higher fire incidence in socially disadvantaged neighbourhoods and there was some evidence to suggest that there may be a compounding impact of high temperatures in such areas. We suggest that these findings may be used to guide the operations of fire services through spatial and temporal targeting to better utilise finite resources, help mitigate risk and reduce casualties.

  18. Controlling the elements: an optogenetic approach to understanding the neural circuits of fear.

    Science.gov (United States)

    Johansen, Joshua P; Wolff, Steffen B E; Lüthi, Andreas; LeDoux, Joseph E

    2012-06-15

    Neural circuits underlie our ability to interact in the world and to learn adaptively from experience. Understanding neural circuits and how circuit structure gives rise to neural firing patterns or computations is fundamental to our understanding of human experience and behavior. Fear conditioning is a powerful model system in which to study neural circuits and information processing and relate them to learning and behavior. Until recently, technological limitations have made it difficult to study the causal role of specific circuit elements during fear conditioning. However, newly developed optogenetic tools allow researchers to manipulate individual circuit components such as anatomically or molecularly defined cell populations, with high temporal precision. Applying these tools to the study of fear conditioning to control specific neural subpopulations in the fear circuit will facilitate a causal analysis of the role of these circuit elements in fear learning and memory. By combining this approach with in vivo electrophysiological recordings in awake, behaving animals, it will also be possible to determine the functional contribution of specific cell populations to neural processing in the fear circuit. As a result, the application of optogenetics to fear conditioning could shed light on how specific circuit elements contribute to neural coding and to fear learning and memory. Furthermore, this approach may reveal general rules for how circuit structure and neural coding within circuits gives rise to sensory experience and behavior. Copyright © 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  19. Ecological Impacts of the Cerro Grande Fire: Predicting Elk Movement and Distribution Patterns in Response to Vegetative Recovery through Simulation Modeling October 2005

    Energy Technology Data Exchange (ETDEWEB)

    Rupp, Susan P. [Texas Tech Univ., Lubbock, TX (United States)

    2005-10-01

    In May 2000, the Cerro Grande Fire burned approximately 17,200 ha in north-central New Mexico as the result of an escaped prescribed burn initiated by Bandelier National Monument. The interaction of large-scale fires, vegetation, and elk is an important management issue, but few studies have addressed the ecological implications of vegetative succession and landscape heterogeneity on ungulate populations following large-scale disturbance events. Primary objectives of this research were to identify elk movement pathways on local and landscape scales, to determine environmental factors that influence elk movement, and to evaluate movement and distribution patterns in relation to spatial and temporal aspects of the Cerro Grande Fire. Data collection and assimilation reflect the collaborative efforts of National Park Service, U.S. Forest Service, and Department of Energy (Los Alamos National Laboratory) personnel. Geographic positioning system (GPS) collars were used to track 54 elk over a period of 3+ years and locational data were incorporated into a multi-layered geographic information system (GIS) for analysis. Preliminary tests of GPS collar accuracy indicated a strong effect of 2D fixes on position acquisition rates (PARs) depending on time of day and season of year. Slope, aspect, elevation, and land cover type affected dilution of precision (DOP) values for both 2D and 3D fixes, although significant relationships varied from positive to negative making it difficult to delineate the mechanism behind significant responses. Two-dimensional fixes accounted for 34% of all successfully acquired locations and may affect results in which those data were used. Overall position acquisition rate was 93.3% and mean DOP values were consistently in the range of 4.0 to 6.0 leading to the conclusion collar accuracy was acceptable for modeling purposes. SAVANNA, a spatially explicit, process-oriented ecosystem model, was used to simulate successional dynamics. Inputs to the

  20. Implementing Signature Neural Networks with Spiking Neurons.

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence

  1. Implementing Signature Neural Networks with Spiking Neurons

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm—i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data—to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the

  2. Systems thinking and wildland fire management

    Science.gov (United States)

    Matthew P. Thompson; Christopher J. Dunn; David E. Calkin

    2017-01-01

    A changing climate, changing development and land use patterns, and increasing pressures on ecosystem services raise global concerns over growing losses associated with wildland fires. New management paradigms acknowledge that fire is inevitable and often uncontrollable, and focus on living with fire rather than attempting to eliminate it from the landscape. A notable...

  3. Wildland fire limits subsequent fire occurrence

    Science.gov (United States)

    Sean A. Parks; Carol Miller; Lisa M. Holsinger; Scott Baggett; Benjamin J. Bird

    2016-01-01

    Several aspects of wildland fire are moderated by site- and landscape-level vegetation changes caused by previous fire, thereby creating a dynamic where one fire exerts a regulatory control on subsequent fire. For example, wildland fire has been shown to regulate the size and severity of subsequent fire. However, wildland fire has the potential to influence...

  4. Physical methods for generating and decoding neural activity in Hirudo verbana

    Science.gov (United States)

    Migliori, Benjamin John

    The interface between living nervous systems and hardware is an excellent proving ground for precision experimental methods and information classification systems. Nervous systems are complex (104 -- 10 15(!) connections), fragile, and highly active in intricate, constantly evolving patterns. However, despite the conveniently electrical nature of neural transmission, the interface between nervous systems and hardware poses significant experimental difficulties. As the desire for direct interfaces with neural signals continues to expand, the need for methods of generating and measuring neural activity with high spatiotemporal precision has become increasingly critical. In this thesis, I describe advances I have made in the ability to modify, generate, measure, and understand neural signals both in- and ex-vivo. I focus on methods developed for transmitting and extracting signals in the intact nervous system of Hirudo verbana (the medicinal leech), an animal with a minimally complex nervous system (10000 neurons distributed in packets along a nerve cord) that exhibits a diverse array of behaviors. To introduce artificial activity patterns, I developed a photothermal activation system in which a highly focused laser is used to irradiate carbon microparticles in contact with target neurons. The resulting local temperature increase generates an electrical current that forces the target neuron to fire neural signals, thereby providing a unique neural input mechanism. These neural signals can potentially be used to alter behavioral choice or generate specific behavioral output, and can be used endogenously in many animal models. I also describe new tools developed to expand the application of this method. In complement to this input system, I describe a new method of analyzing neural output signals involved in long-range coordination of behaviors. Leech behavioral signals are propagated between neural packets as electrical pulses in the nerve connective, a bundle of

  5. Individual Differences in Skilled Adult Readers Reveal Dissociable Patterns of Neural Activity Associated with Component Processes of Reading

    Science.gov (United States)

    Welcome, Suzanne E.; Joanisse, Marc F.

    2012-01-01

    We used fMRI to examine patterns of brain activity associated with component processes of visual word recognition and their relationships to individual differences in reading skill. We manipulated both the judgments adults made on written stimuli and the characteristics of the stimuli. Phonological processing led to activation in left inferior…

  6. Patterns of congenital bony spinal deformity and associated neural anomalies on X-ray and magnetic resonance imaging.

    Science.gov (United States)

    Trenga, Anthony P; Singla, Anuj; Feger, Mark A; Abel, Mark F

    2016-08-01

    Congenital malformations of the bony vertebral column are often accompanied by spinal cord anomalies; these observations have been reinforced with the use of magnetic resonance imaging (MRI). We hypothesized that the incidence of cord anomalies will increase as the number and complexity of bony vertebral abnormalities increases. All patients aged ≤13 years (n = 75) presenting to the pediatric spine clinic from 2003-2013 with congenital bony spinal deformity and both radiographs and MRI were analyzed retrospectively for bone and neural pathology. Chi-squared analysis was used to compare groups for categorical dependent variables. Independent t tests were used for continuous dependent variables. Significance was set at p deformity patients (n = 41) had associated spinal cord anomalies on MRI. Complex bony abnormalities had a higher incidence of cord anomalies than simple abnormalities (67, 37 %; p = 0.011). Mixed deformities of segmentation and formation had a higher incidence of cord anomalies (73 %) than failures of formation (50 %) or segmentation (45 %) alone (p = 0.065). Deformities in the sacrococcygeal area had the highest rate of spinal cord anomalies (13 of 15 patients, 87 %). In 35 cases (47 %), MRI revealed additional bony anomalies that were not seen on the radiographs. As the number of bony malformations increased, we found a higher incidence of cord anomalies. Clinicians should have increased suspicion of spinal cord pathology in the presence of mixed failures of segmentation and formation.

  7. Convolutional neural network approach for enhanced capture of breast parenchymal complexity patterns associated with breast cancer risk

    Science.gov (United States)

    Oustimov, Andrew; Gastounioti, Aimilia; Hsieh, Meng-Kang; Pantalone, Lauren; Conant, Emily F.; Kontos, Despina

    2017-03-01

    We assess the feasibility of a parenchymal texture feature fusion approach, utilizing a convolutional neural network (ConvNet) architecture, to benefit breast cancer risk assessment. Hypothesizing that by capturing sparse, subtle interactions between localized motifs present in two-dimensional texture feature maps derived from mammographic images, a multitude of texture feature descriptors can be optimally reduced to five meta-features capable of serving as a basis on which a linear classifier, such as logistic regression, can efficiently assess breast cancer risk. We combine this methodology with our previously validated lattice-based strategy for parenchymal texture analysis and we evaluate the feasibility of this approach in a case-control study with 424 digital mammograms. In a randomized split-sample setting, we optimize our framework in training/validation sets (N=300) and evaluate its descriminatory performance in an independent test set (N=124). The discriminatory capacity is assessed in terms of the the area under the curve (AUC) of the receiver operator characteristic (ROC). The resulting meta-features exhibited strong classification capability in the test dataset (AUC = 0.90), outperforming conventional, non-fused, texture analysis which previously resulted in an AUC=0.85 on the same case-control dataset. Our results suggest that informative interactions between localized motifs exist and can be extracted and summarized via a fairly simple ConvNet architecture.

  8. Combining satellite-based fire observations and ground-based lightning detections to identify lightning fires across the conterminous USA

    Science.gov (United States)

    Bar-Massada, A.; Hawbaker, T.J.; Stewart, S.I.; Radeloff, V.C.

    2012-01-01

    Lightning fires are a common natural disturbance in North America, and account for the largest proportion of the area burned by wildfires each year. Yet, the spatiotemporal patterns of lightning fires in the conterminous US are not well understood due to limitations of existing fire databases. Our goal here was to develop and test an algorithm that combined MODIS fire detections with lightning detections from the National Lightning Detection Network to identify lightning fires across the conterminous US from 2000 to 2008. The algorithm searches for spatiotemporal conjunctions of MODIS fire clusters and NLDN detected lightning strikes, given a spatiotemporal lag between lightning strike and fire ignition. The algorithm revealed distinctive spatial patterns of lightning fires in the conterminous US While a sensitivity analysis revealed that the algorithm is highly sensitive to the two thresholds that are used to determine conjunction, the density of fires it detected was moderately correlated with ground based fire records. When only fires larger than 0.4 km2 were considered, correlations were higher and the root-mean-square error between datasets was less than five fires per 625 km2 for the entire study period. Our algorithm is thus suitable for detecting broad scale spatial patterns of lightning fire occurrence, and especially lightning fire hotspots, but has limited detection capability of smaller fires because these cannot be consistently detected by MODIS. These results may enhance our understanding of large scale patterns of lightning fire activity, and can be used to identify the broad scale factors controlling fire occurrence.

  9. Fire regime in Mediterranean ecosystem

    Science.gov (United States)

    Biondi, Guido; Casula, Paolo; D'Andrea, Mirko; Fiorucci, Paolo

    2010-05-01

    Liguria and is limited in Sardinia. What is common in the two regions is the widespread presence of shrub species frequently spread by fire. The analysis in the two regions thus allows in a rather limited area to study almost all the species that characterize the Mediterranean region. This work shows that the fire regime in Mediterranean area is strongly related with vegetation patterns, is almost totally independent by the cause of ignition, and only partially dependent by fire extinguishing actions.

  10. Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method.

    Science.gov (United States)

    Guo, Xinyu; Dominick, Kelli C; Minai, Ali A; Li, Hailong; Erickson, Craig A; Lu, Long J

    2017-01-01

    The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre

  11. Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method

    Directory of Open Access Journals (Sweden)

    Xinyu Guo

    2017-08-01

    Full Text Available The whole-brain functional connectivity (FC pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes. Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150. Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross

  12. Neural constraints and flexibility in language processing.

    Science.gov (United States)

    Huyck, Christian R

    2016-01-01

    Humans process language with their neurons. Memory in neurons is supported by neural firing and by short- and long-term synaptic weight change; the emergent behaviour of neurons, synchronous firing, and cell assembly dynamics is also a form of memory. As the language signal moves to later stages, it is processed with different mechanisms that are slower but more persistent.

  13. Firing probability and mean firing rates of human muscle vasoconstrictor neurones are elevated during chronic asphyxia

    DEFF Research Database (Denmark)

    Ashley, Cynthia; Burton, Danielle; Sverrisdottir, Yrsa B

    2010-01-01

    are chronically asphyxic. We tested the hypothesis that this elevated chemical drive would shift the firing pattern from that seen in healthy subjects to that seen in OSAS. The mean firing probability (52%) and mean firing rate (0.92 Hz) of 17 muscle vasoconstrictor neurones recorded in COPD were comparable...... in the obstructive sleep apnoea syndrome (OSAS) is associated with an increase in firing probability and mean firing rate, and an increase in multiple within-burst firing. Here we characterize the firing properties of muscle vasoconstrictor neurones in patients with chronic obstructive pulmonary disease (COPD), who...... in the healthy group (78%). Conversely, single neurones fired twice in 25% of cardiac intervals, similar to OSAS (27%), but significantly higher than in the healthy group (18%). We conclude that the chronic asphyxia associated with COPD results in an increase in the firing probability and mean firing frequency...

  14. Characterization of fire regime in Sardinia (Italy)

    Science.gov (United States)

    Bacciu, V. M.; Salis, M.; Mastinu, S.; Masala, F.; Sirca, C.; Spano, D.

    2012-12-01

    In the last decades, a number of Authors highlighted the crucial role of forest fires within Mediterranean ecosystems, with impacts both negative and positive on all biosphere components and with reverberations on different scales. Fire determines the landscape structure and plant composition, but it is also the cause of enormous economic and ecological damages, beside the loss of human life. In Sardinia (Italy), the second largest island of the Mediterranean Basin, forest fires are perceived as one of the main environmental and social problems, and data are showing that the situation is worsening especially within the rural-urban peripheries and the increasing number of very large forest fires. The need for information concerning forest fire regime has been pointed out by several Authors (e.g. Rollins et al., 2002), who also emphasized the importance of understanding the factors (such as weather/climate, socio-economic, and land use) that determine spatial and temporal fire patterns. These would be used not only as a baseline to predict the climate change effect on forest fires, but also as a fire management and mitigation strategy. The main aim of this paper is, thus, to analyze the temporal and spatial patterns of fire occurrence in Sardinia (Italy) during the last three decades (1980-2010). For the analyzed period, fire statistics were provided by the Sardinian Forest Service (CFVA - Corpo Forestale e di Vigilanza Ambientale), while weather data for eight weather stations were obtained from the web site www.tutiempo.it. For each station, daily series of precipitation, mean, maximum and minimum temperature, relative humidity and wind speed were available. The present study firstly analyzed fire statistics (burned area and number of fires) according to the main fire regime characteristics (seasonality, fire return interval, fire incidence, fire size distribution). Then, fire and weather daily values were averaged to obtain monthly, seasonal and annual values, and

  15. Neurally released pituitary adenylate cyclase-activating polypeptide enhances guinea pig intrinsic cardiac neurone excitability.

    Science.gov (United States)

    Tompkins, John D; Ardell, Jeffrey L; Hoover, Donald B; Parsons, Rodney L

    2007-07-01

    Intracellular recordings were made in vitro from guinea-pig cardiac ganglia to determine whether endogenous neuropeptides such as pituitary adenylate cyclase-activating polypeptide (PACAP) or substance P released during tetanic neural stimulation modulate cardiac neurone excitability and/or contribute to slow excitatory postsynaptic potentials (sEPSPs). When nicotinic and muscarinic receptors were blocked by hexamethonium and atropine, 20 Hz stimulation for 10 s initiated a sEPSP in all innervated neurones. In 40% of the cells, excitability was enhanced after termination of the sEPSP. This suggested that non-cholinergic receptor-mediated mechanisms contributed to the sEPSP and modulated neuronal excitability. Exogenous PACAP and substance P initiated a slow depolarization in the neurones whereas neuronal excitability was only increased by PACAP. When ganglia were treated with the PAC1 antagonist PACAP6-38 (500 nM), the sEPSP evoked by 20 Hz stimulation was reduced by approximately 50% and an enhanced excitability occurred in only 10% of the cells. These observations suggested that PACAP released from preganglionic nerve terminals during tetanic stimulation enhanced neuronal excitability and evoked sEPSPs. After addition of 1 nM PACAP to the bath, 7 of 9 neurones exhibited a tonic firing pattern whereas in untreated preparations, the neurons had a phasic firing pattern. PACAP6-38 (500 nM) diminished the increase in excitability caused by 1 nM PACAP so that only 4 of 13 neurones exhibited a tonic firing pattern and the other 9 cells retained a phasic firing pattern. These findings indicate that PACAP can be released by tetanic neural stimulation in vitro and increase the excitability of intrinsic cardiac neurones. We hypothesize that in vivo PACAP released during preganglionic firing may modulate neurotransmission within the intrinsic cardiac ganglia.

  16. A neutral model of low-severity fire regimes

    Science.gov (United States)

    Don McKenzie; Amy E. Hessl

    2008-01-01

    Climate, topography, fuel loadings, and human activities all affect spatial and temporal patterns of fire occurrence. Because fire occurrence is a stochastic process, an understanding of baseline variability is necessary in order to identify constraints on surface fire regimes. With a suitable null, or neutral, model, characteristics of natural fire regimes estimated...

  17. Fire danmarkskort

    DEFF Research Database (Denmark)

    Pedersen, Birger Faurholt

    2017-01-01

    Fire danmarkskort udarbejdet på baggrund af dataudtræk fra indsendte gødningsregnskaber for planperioden 2015/2016......Fire danmarkskort udarbejdet på baggrund af dataudtræk fra indsendte gødningsregnskaber for planperioden 2015/2016...

  18. On fire

    DEFF Research Database (Denmark)

    Hansen, Helle Rabøl

    The title of this paper: “On fire”, refers to two (maybe three) aspects: firstly as a metaphor of having engagement in a community of practice according to Lave & Wenger (1991), and secondly it refers to the concrete element “fire” in the work of the fire fighters – and thirdly fire as a signifier...

  19. Fire Power

    Science.gov (United States)

    Denker, Deb; West, Lee

    2009-01-01

    For education administrators, campus fires are not only a distressing loss, but also a stark reminder that a campus faces risks that require special vigilance. In many ways, campuses resemble small communities, with areas for living, working and relaxing. A residence hall fire may raise the specter of careless youth, often with the complication of…

  20. Forest-fire models

    Science.gov (United States)

    Haiganoush Preisler; Alan Ager

    2013-01-01

    For applied mathematicians forest fire models refer mainly to a non-linear dynamic system often used to simulate spread of fire. For forest managers forest fire models may pertain to any of the three phases of fire management: prefire planning (fire risk models), fire suppression (fire behavior models), and postfire evaluation (fire effects and economic models). In...

  1. 46 CFR 28.315 - Fire pumps, fire mains, fire hydrants, and fire hoses.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 1 2010-10-01 2010-10-01 false Fire pumps, fire mains, fire hydrants, and fire hoses... After September 15, 1991, and That Operate With More Than 16 Individuals on Board § 28.315 Fire pumps, fire mains, fire hydrants, and fire hoses. (a) Each vessel 36 feet (11.8 meters) or more in length must...

  2. Mixed severity fire effects within the Rim fire: Relative importance of local climate, fire weather, topography, and forest structure

    Science.gov (United States)

    Van R. Kane; C. Alina Cansler; Nicholas A. Povak; Jonathan T. Kane; Robert J. McGaughey; James A. Lutz; Derek J. Churchill; Malcolm P. North

    2015-01-01

    Recent and projected increases in the frequency and severity of large wildfires in the western U.S. makes understanding the factors that strongly affect landscape fire patterns a management priority for optimizing treatment location. We compared the influence of variations in the local environment on burn severity patterns on the large 2013 Rim fire that burned under...

  3. A new perspective on behavioral inconsistency and neural noise in aging: Compensatory speeding of neural communication

    Directory of Open Access Journals (Sweden)

    S. Lee Hong

    2012-09-01

    Full Text Available This paper seeks to present a new perspective on the aging brain. Here, we make connections between two key phenomena of brain aging: 1 increased neural noise or random background activity; and 2 slowing of brain activity. Our perspective proposes the possibility that the slowing of neural processing due to decreasing nerve conduction velocities leads to a compensatory speeding of neuron firing rates. These increased firing rates lead to a broader distribution of power in the frequency spectrum of neural oscillations, which we propose, can just as easily be interpreted as neural noise. Compensatory speeding of neural activity, as we present, is constrained by the: A availability of metabolic energy sources; and B competition for frequency bandwidth needed for neural communication. We propose that these constraints lead to the eventual inability to compensate for age-related declines in neural function that are manifested clinically as deficits in cognition, affect, and motor behavior.

  4. ESA fire_cci product assessment

    Science.gov (United States)

    Heil, Angelika; Yue, Chao; Mouillot, Florent; Storm, Thomas; Chuvieco, Emilio; Ramo Sanchez, Ruben; Kaiser, Johannes W.

    2017-04-01

    Vegetation fires are a major disturbance in the Earth System. Fires change the biophysical properties and dynamics of ecosystems and alter terrestrial carbon pools. By altering the atmosphere's composition, fire emissions exert a significant climate forcing. To realistically model past and future changes of the Earth System, fire disturbances must be taken into account. Related modelling efforts require consistent global burned area observations covering at least 10 to 20 years. Guided by the specific requirements of a wide range of end users, the ESA fire_cci project has computed a new global burned area dataset. It applies a newly developed spectral change detection algorithm upon the ENVISAT-MERIS archive. The algorithm relies on MODIS active fire information as "seed". It comprises a pixel burned area product (spatial resolution of 333 m) with date detection information and a biweekly grid product at 0.25 degree spatial resolution. We compare fire_cci burned area with other global burned area products (MCD64 Collection 6, MCD45, GFED4, GFED4s and GEOLAND) and a set of active fires data (hotspots from MODIS, TRMM, AATSR and fire radiative power from GFAS). The analysis of patterns of agreement and disagreement between fire_cci and other products provides a better understanding of product characteristics and uncertainties. The intercomparison of the 2005-2011 fire_cci time series shows a close agreement with GFED4 data in terms of global burned area and the general spatial and temporal patterns. Pronounced differences, however, emerge for specific regions or fire events. Burned area mapped by fire_cci tends to be notably higher in regions where small agricultural fires predominate. The improved detection of small agricultural fires by fire_cci can be related to the increased spatial resolution of the MERIS sensor (333 m compared to 500 in MODIS). This is illustrated in detail using the example of the extreme 2006 spring fires in Eastern Europe.

  5. Bioprinting for Neural Tissue Engineering.

    Science.gov (United States)

    Knowlton, Stephanie; Anand, Shivesh; Shah, Twisha; Tasoglu, Savas

    2018-01-01

    Bioprinting is a method by which a cell-encapsulating bioink is patterned to create complex tissue architectures. Given the potential impact of this technology on neural research, we review the current state-of-the-art approaches for bioprinting neural tissues. While 2D neural cultures are ubiquitous for studying neural cells, 3D cultures can more accurately replicate the microenvironment of neural tissues. By bioprinting neuronal constructs, one can precisely control the microenvironment by specifically formulating the bioink for neural tissues, and by spatially patterning cell types and scaffold properties in three dimensions. We review a range of bioprinted neural tissue models and discuss how they can be used to observe how neurons behave, understand disease processes, develop new therapies and, ultimately, design replacement tissues. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. The abrupt emergence of adult-like grid cell firing during the development of the medial entorhinal cortex

    Directory of Open Access Journals (Sweden)

    Thomas Joseph Wills

    2012-04-01

    Full Text Available The neural circuits underlying spatial cognition are created during the development of the brain, and understanding how networks subserving specific cognitive functions are assembled remains a challenge for neuroscience. Here, we characterise the development of grid cells in the medial entorhinal cortex, which, by nature of their regularly spaced firing fields, are thought to provide a distance metric to the hippocampal neural representation of space. Grid cells emerge at the time of weaning in the rat, at around three weeks of age. We investigated whether grid cells in young rats are functionally equivalent to those observed in the adult as soon as they appear, or if instead they follow a gradual developmental trajectory. We find that, from the very youngest ages at which reproducible grid firing is observed (postnatal day 19: grid cells display adult-like firing fields that tessellate to form a coherent map of the local environment; that this map is universal, maintaining its internal structure across different environments; and that grid cells in young rats, as in adults, also encode a representation of direction and speed. To further investigate the developmental processes leading up to the appearance of grid cells, we present data from individual medial entorhinal cortex cells recorded across more than one day, spanning the period before and after the grid firing pattern emerged. We find that different cells show different patterns of development, but that increasing spatial stability may be a common theme.

  7. Fire history and fire management implications in the Yukon Flats National Wildlife Refuge, interior Alaska

    Science.gov (United States)

    S. A. Drury; P. J. Grissom

    2008-01-01

    We conducted this investigation in response to criticisms that the current Alaska Interagency Fire Management Plans are allowing too much of the landscape in interior Alaska to burn annually. To address this issue, we analyzed fire history patterns within the Yukon Flats National Wildlife Refuge, interior Alaska. We dated 40 fires on 27 landscape points within the...

  8. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule.

    Science.gov (United States)

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

    2015-11-01

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

  9. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin, E-mail: xmli@cqu.edu.cn [Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044 (China); College of Automation, Chongqing University, Chongqing 400044 (China)

    2015-11-15

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

  10. Bison and fire: Landscape analysis of ungulate response to Yellowstone`s fires

    Energy Technology Data Exchange (ETDEWEB)

    Wallace, L.L. [Oklahoma Univ., Norman, OK (United States). Dept. of Botany and Microbiology; Turner, M.G.; Wu, Yegang [Oak Ridge National Lab., TN (United States); Romme, W.H. [Fort Lewis Coll., Durango, CO (United States). Dept. of Biology

    1993-10-01

    A simulation model of bison survival under different scenarios of winter severity, fire size, fire pattern and population size was run. Previous work had shown the model to be realistic. The overriding factor influencing bison winter survival in the model was winter severity. This factor had significant interactions with fire size and population size as well, further reducing survival in all cases. Increasing fire size reduced survival the first year after a simulated fire, but increased survival two years after the fire. This was due to enhanced forage production in burned areas the second year. A threshold effect on survival was noted at fire sizes greater than 60% of the simulated landscape, a number which is critical in disturbance propagation in landscapes. There was no biologically important effect of fire pattern (random vs. clumped) on survival.

  11. Optical pattern recognition algorithms on neural-logic equivalent models and demonstration of their prospects and possible implementations

    Science.gov (United States)

    Krasilenko, Vladimir G.; Nikolsky, Alexander I.; Zaitsev, Alexandr V.; Voloshin, Victor M.

    2001-03-01

    Historic information regarding the appearance and creation of fundamentals of algebra-logical apparatus-`equivalental algebra' for description of neuro-nets paradigms and algorithms is considered which is unification of theory of neuron nets (NN), linear algebra and the most generalized neuro-biology extended for matrix case. A survey is given of `equivalental models' of neuron nets and associative memory is suggested new, modified matrix-tenzor neurological equivalental models (MTNLEMS) are offered with double adaptive-equivalental weighing (DAEW) for spatial-non- invariant recognition (SNIR) and space-invariant recognition (SIR) of 2D images (patterns). It is shown, that MTNLEMS DAEW are the most generalized, they can describe the processes in NN both within the frames of known paradigms and within new `equivalental' paradigm of non-interaction type, and the computing process in NN under using the offered MTNLEMs DAEW is reduced to two-step and multi-step algorithms and step-by-step matrix-tenzor procedures (for SNIR) and procedures of defining of space-dependent equivalental functions from two images (for SIR).

  12. Neural and Molecular Mechanisms Involved in Controlling the Quality of Feeding Behavior: Diet Selection and Feeding Patterns

    Directory of Open Access Journals (Sweden)

    Tsutomu Sasaki

    2017-10-01

    Full Text Available We are what we eat. There are three aspects of feeding: what, when, and how much. These aspects represent the quantity (how much and quality (what and when of feeding. The quantitative aspect of feeding has been studied extensively, because weight is primarily determined by the balance between caloric intake and expenditure. In contrast, less is known about the mechanisms that regulate the qualitative aspects of feeding, although they also significantly impact the control of weight and health. However, two aspects of feeding quality relevant to weight loss and weight regain are discussed in this review: macronutrient-based diet selection (what and feeding pattern (when. This review covers the importance of these two factors in controlling weight and health, and the central mechanisms that regulate them. The relatively limited and fragmented knowledge on these topics indicates that we lack an integrated understanding of the qualitative aspects of feeding behavior. To promote better understanding of weight control, research efforts must focus more on the mechanisms that control the quality and quantity of feeding behavior. This understanding will contribute to improving dietary interventions for achieving weight control and for preventing weight regain following weight loss.

  13. Spatio-temporal patterns and source apportionment of pollution in Qiantang River (China) using neural-based modeling and multivariate statistical techniques

    Science.gov (United States)

    Su, Shiliang; Zhi, Junjun; Lou, Liping; Huang, Fang; Chen, Xia; Wu, Jiaping

    Characterizing the spatio-temporal patterns and apportioning the pollution sources of water bodies are important for the management and protection of water resources. The main objective of this study is to describe the dynamics of water quality and provide references for improving river pollution control practices. Comprehensive application of neural-based modeling and different multivariate methods was used to evaluate the spatio-temporal patterns and source apportionment of pollution in Qiantang River, China. Measurement data were obtained and pretreated for 13 variables from 41 monitoring sites for the period of 2001-2004. A self-organizing map classified the 41 monitoring sites into three groups (Group A, B and C), representing different pollution characteristics. Four significant parameters (dissolved oxygen, biochemical oxygen demand, total phosphorus and total lead) were identified by discriminant analysis for distinguishing variations of different years, with about 80% correct assignment for temporal variation. Rotated principal component analysis (PCA) identified four potential pollution sources for Group A (domestic sewage and agricultural pollution, industrial wastewater pollution, mineral weathering, vehicle exhaust and sand mining), five for Group B (heavy metal pollution, agricultural runoff, vehicle exhaust and sand mining, mineral weathering, chemical plants discharge) and another five for Group C (vehicle exhaust and sand mining, chemical plants discharge, soil weathering, biochemical pollution, mineral weathering). The identified potential pollution sources explained 75.6% of the total variances for Group A, 75.0% for Group B and 80.0% for Group C, respectively. Receptor-based source apportionment was applied to further estimate source contributions for each pollution variable in the three groups, which facilitated and supported the PCA results. These results could assist managers to develop optimal strategies and determine priorities for river

  14. Computerized analysis of interstitial lung diseases on chest radiographs based on lung texture, geometric-pattern features, and artificial neural networks

    Science.gov (United States)

    Ishida, Takayuki; Katsuragawa, Shigehiko; Nakamura, Katsumi; Ashizawa, Kazuto; MacMahon, Heber; Doi, Kunio

    2002-05-01

    For computerized detection of interstitial lung disease on chest radiographs, we developed three different methods: texture analysis based on the Fourier transform, geometric- pattern feature analysis, and artificial neural network (ANN) analysis of image data. With these computer-aided diagnostic methods, quantitative measures can be obtained. To improve the diagnostic accuracy, we investigated combined classification schemes by using the results obtained with the three methods for distinction between normal and abnormal chest radiographs with interstitial opacities. The sensitivities of texture analysis, geometric analysis, and ANN analysis were 88.0+/- 1.6%, 91.0+/- 2.6%, and 87.5+/- 1.9%, respectively, at a specificity of 90.0%, whereas the sensitivity of a combined classification scheme with the logical OR operation was improved to 97.1%+/- 1.5% at the same specificity of 90.0%. The combined scheme can achieve higher accuracy than the individual methods for distinction between normal and abnormal cases with interstitial opacities.

  15. Detailed expression profile of all six Glypicans and their modifying enzyme Notum during chick embryogenesis and their role in dorsal-ventral patterning of the neural tube.

    Science.gov (United States)

    Saad, Kawakeb; Otto, Anthony; Theis, Susanne; Kennerley, Niki; Munsterberg, Andrea; Luke, Graham; Patel, Ketan

    2017-04-20

    Vertebrate development is orchestrated by secreted signalling molecules that regulate cell behaviour and cell fate decisions during early embryogenesis. The activity of key signalling molecules including members of Hedgehog, Bone Morphogenetic Proteins and Wnt families are regulated by Glypicans, a family of GPI linked polypeptides. Glypicans either promote or inhibit the action of signalling molecules and add a layer of complexity that needs to be understood in order to fully decipher the processes that regulate early vertebrate development. Here we present a detailed expression profile of all six Glypicans and their modifying enzyme Notum during chick embryogenesis. Our results strongly suggest that these proteins have many as yet undiscovered roles to play during early embryogenesis. Finally, we have taken an experimental approach to investigate their role during the patterning of a key embryonic structure - the neural tube. In particular, we show that over-expression of Notum leads to the dorsalisation of this structure. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. A carbon-fiber electrode array for long-term neural recording.

    Science.gov (United States)

    Guitchounts, Grigori; Markowitz, Jeffrey E; Liberti, William A; Gardner, Timothy J

    2013-08-01

    Chronic neural recording in behaving animals is an essential method for studies of neural circuit function. However, stable recordings from small, densely packed neurons remains challenging, particularly over time-scales relevant for learning. We describe an assembly method for a 16-channel electrode array consisting of carbon fibers (Carbon fiber arrays were tested in HVC (used as a proper name), a song motor nucleus, of singing zebra finches where individual neurons discharge with temporally precise patterns. Previous reports of activity in this population of neurons have required the use of high impedance electrodes on movable microdrives. Here, the carbon fiber electrodes provided stable multi-unit recordings over time-scales of months. Spike-sorting indicated that the multi-unit signals were dominated by one, or a small number of cells. Stable firing patterns during singing confirmed the stability of these clusters over time-scales of months. In addition, from a total of 10 surgeries, 16 projection neurons were found. This cell type is characterized by sparse stereotyped firing patterns, providing unambiguous confirmation of single cell recordings. Carbon fiber electrode bundles may provide a scalable solution for long-term neural recordings of densely packed neurons.

  17. Neural Oscillators Programming Simplified

    Directory of Open Access Journals (Sweden)

    Patrick McDowell

    2012-01-01

    Full Text Available The neurological mechanism used for generating rhythmic patterns for functions such as swallowing, walking, and chewing has been modeled computationally by the neural oscillator. It has been widely studied by biologists to model various aspects of organisms and by computer scientists and robotics engineers as a method for controlling and coordinating the gaits of walking robots. Although there has been significant study in this area, it is difficult to find basic guidelines for programming neural oscillators. In this paper, the authors approach neural oscillators from a programmer’s point of view, providing background and examples for developing neural oscillators to generate rhythmic patterns that can be used in biological modeling and robotics applications.

  18. Grid cell hexagonal patterns formed by fast self-organized learning within entorhinal cortex.

    Science.gov (United States)

    Mhatre, Himanshu; Gorchetchnikov, Anatoli; Grossberg, Stephen

    2012-02-01

    Grid cells in the dorsal segment of the medial entorhinal cortex (dMEC) show remarkable hexagonal activity patterns, at multiple spatial scales, during spatial navigation. It has previously been shown how a self-organizing map can convert firing patterns across entorhinal grid cells into hippocampal place cells that are capable of representing much larger spatial scales. Can grid cell firing fields also arise during navigation through learning within a self-organizing map? This article describes a simple and general mathematical property of the trigonometry of spatial navigation which favors hexagonal patterns. The article also develops a neural model that can learn to exploit this trigonometric relationship. This GRIDSmap self-organizing map model converts path integration signals into hexagonal grid cell patterns of multiple scales. GRIDSmap creates only grid cell firing patterns with the observed hexagonal structure, predicts how these hexagonal patterns can be learned from experience, and can process biologically plausible neural input and output signals during navigation. These results support an emerging unified computational framework based on a hierarchy of self-organizing maps for explaining how entorhinal-hippocampal interactions support spatial navigation. Copyright © 2010 Wiley Periodicals, Inc.

  19. Functional recordings from awake, behaving rodents through a microchannel based regenerative neural interface

    Science.gov (United States)

    Gore, Russell K.; Choi, Yoonsu; Bellamkonda, Ravi; English, Arthur

    2015-02-01

    Objective. Neural interface technologies could provide controlling connections between the nervous system and external technologies, such as limb prosthetics. The recording of efferent, motor potentials is a critical requirement for a peripheral neural interface, as these signals represent the user-generated neural output intended to drive external devices. Our objective was to evaluate structural and functional neural regeneration through a microchannel neural interface and to characterize potentials recorded from electrodes placed within the microchannels in awake and behaving animals. Approach. Female rats were implanted with muscle EMG electrodes and, following unilateral sciatic nerve transection, the cut nerve was repaired either across a microchannel neural interface or with end-to-end surgical repair. During a 13 week recovery period, direct muscle responses to nerve stimulation proximal to the transection were monitored weekly. In two rats repaired with the neural interface, four wire electrodes were embedded in the microchannels and recordings were obtained within microchannels during proximal stimulation experiments and treadmill locomotion. Main results. In these proof-of-principle experiments, we found that axons from cut nerves were capable of functional reinnervation of distal muscle targets, whether regenerating through a microchannel device or after direct end-to-end repair. Discrete stimulation-evoked and volitional potentials were recorded within interface microchannels in a small group of awake and behaving animals and their firing patterns correlated directly with intramuscular recordings during locomotion. Of 38 potentials extracted, 19 were identified as motor axons reinnervating tibialis anterior or soleus muscles using spike triggered averaging. Significance. These results are evidence for motor axon regeneration through microchannels and are the first report of in vivo recordings from regenerated motor axons within microchannels in a small

  20. Fire Safety

    Science.gov (United States)

    ... Whether the number is 911 or a regular phone number, everyone in the family should know it by ... near the phone. Include the local fire department phone number, your full home address and phone number, and ...

  1. [Artificial neural networks in Neurosciences].

    Science.gov (United States)

    Porras Chavarino, Carmen; Salinas Martínez de Lecea, José María

    2011-11-01

    This article shows that artificial neural networks are used for confirming the relationships between physiological and cognitive changes. Specifically, we explore the influence of a decrease of neurotransmitters on the behaviour of old people in recognition tasks. This artificial neural network recognizes learned patterns. When we change the threshold of activation in some units, the artificial neural network simulates the experimental results of old people in recognition tasks. However, the main contributions of this paper are the design of an artificial neural network and its operation inspired by the nervous system and the way the inputs are coded and the process of orthogonalization of patterns.

  2. Fire risk in the road landscape patterns of the state of Paraná, Brazil - planning grants for the wildland-urban interface

    Science.gov (United States)

    Daniela Biondi; Antonio Carlos Batista; Angeline Martini

    2013-01-01

    Urban growth worldwide has generated great concern in the planning of the different environments belonging to the wildland-urban interface. One of the problems that arise is the landscape treatment given to roads, which must not only comply with aesthetic and ecological principles, but also be functional, adding functions relating to forest fire prevention and control...

  3. The influence of a water current on the larval deposition pattern of females of a diverging fire salamander population (Salamandra salamandra)

    NARCIS (Netherlands)

    Krause, E.T.; Caspers, B.A.

    2015-01-01

    Fire salamanders are amphibians that exhibit a highly specific reproductive mode termed ovo-viviparity. The eggs develop inside their mothers, and the females give birth to fully developed larvae. The larvae in our study area cluster in two distinct genetic groups that can be linked directly to the

  4. Neural Patterns of Reorganization after Intensive Robot-Assisted Virtual Reality Therapy and Repetitive Task Practice in Patients with Chronic Stroke.

    Science.gov (United States)

    Saleh, Soha; Fluet, Gerard; Qiu, Qinyin; Merians, Alma; Adamovich, Sergei V; Tunik, Eugene

    2017-01-01

    Several approaches to rehabilitation of the hand following a stroke have emerged over the last two decades. These treatments, including repetitive task practice (RTP), robotically assisted rehabilitation and virtual rehabilitation activities, produce improvements in hand function but have yet to reinstate function to pre-stroke levels-which likely depends on developing the therapies to impact cortical reorganization in a manner that favors or supports recovery. Understanding cortical reorganization that underlies the above interventions is therefore critical to inform how such therapies can be utilized and improved and is the focus of the current investigation. Specifically, we compare neural reorganization elicited in stroke patients participating in two interventions: a hybrid of robot-assisted virtual reality (RAVR) rehabilitation training and a program of RTP training. Ten chronic stroke subjects participated in eight 3-h sessions of RAVR therapy. Another group of nine stroke subjects participated in eight sessions of matched RTP therapy. Functional magnetic resonance imaging (fMRI) data were acquired during paretic hand movement, before and after training. We compared the difference between groups and sessions (before and after training) in terms of BOLD intensity, laterality index of activation in sensorimotor areas, and the effective connectivity between ipsilesional motor cortex (iMC), contralesional motor cortex, ipsilesional primary somatosensory cortex (iS1), ipsilesional ventral premotor area (iPMv), and ipsilesional supplementary motor area. Last, we analyzed the relationship between changes in fMRI data and functional improvement measured by the Jebsen Taylor Hand Function Test (JTHFT), in an attempt to identify how neurophysiological changes are related to motor improvement. Subjects in both groups demonstrated motor recovery after training, but fMRI data revealed RAVR-specific changes in neural reorganization patterns. First, BOLD signal in multiple

  5. Neural Patterns of Reorganization after Intensive Robot-Assisted Virtual Reality Therapy and Repetitive Task Practice in Patients with Chronic Stroke

    Directory of Open Access Journals (Sweden)

    Soha Saleh

    2017-09-01

    Full Text Available Several approaches to rehabilitation of the hand following a stroke have emerged over the last two decades. These treatments, including repetitive task practice (RTP, robotically assisted rehabilitation and virtual rehabilitation activities, produce improvements in hand function but have yet to reinstate function to pre-stroke levels—which likely depends on developing the therapies to impact cortical reorganization in a manner that favors or supports recovery. Understanding cortical reorganization that underlies the above interventions is therefore critical to inform how such therapies can be utilized and improved and is the focus of the current investigation. Specifically, we compare neural reorganization elicited in stroke patients participating in two interventions: a hybrid of robot-assisted virtual reality (RAVR rehabilitation training and a program of RTP training. Ten chronic stroke subjects participated in eight 3-h sessions of RAVR therapy. Another group of nine stroke subjects participated in eight sessions of matched RTP therapy. Functional magnetic resonance imaging (fMRI data were acquired during paretic hand movement, before and after training. We compared the difference between groups and sessions (before and after training in terms of BOLD intensity, laterality index of activation in sensorimotor areas, and the effective connectivity between ipsilesional motor cortex (iMC, contralesional motor cortex, ipsilesional primary somatosensory cortex (iS1, ipsilesional ventral premotor area (iPMv, and ipsilesional supplementary motor area. Last, we analyzed the relationship between changes in fMRI data and functional improvement measured by the Jebsen Taylor Hand Function Test (JTHFT, in an attempt to identify how neurophysiological changes are related to motor improvement. Subjects in both groups demonstrated motor recovery after training, but fMRI data revealed RAVR-specific changes in neural reorganization patterns. First, BOLD

  6. Evidence of fuels management and fire weather influencing fire severity in an extreme fire event

    Science.gov (United States)

    Jamie M. Lydersen; Brandon M. Collins; Matthew L. Brooks; John R. Matchett; Kristen L. Shive; Nicholas A. Povak; Van R. Kane; Douglas F. Smith

    2017-01-01

    Following changes in vegetation structure and pattern, along with a changing climate, large wildfire incidence has increased in forests throughout the western U.S. Given this increase there is great interest in whether fuels treatments and previous wildfire can alter fire severity patterns in large wildfires. We assessed the relative influence of previous fuels...

  7. Spatial distribution of human-caused forest fires in Galicia (NW Spain)

    Science.gov (United States)

    M. L. Chas-Amil; J. Touza; P. Prestemon

    2010-01-01

    It is crucial for fire prevention policies to assess the spatial patterns of human-started fires and their relationship with geographical and socioeconomic aspects. This study uses fire reports for the period 1988-2006 in Galicia, Spain, to analyze the spatial distribution of human-induced fire risk attending to causes and underlying motivations associated with fire...

  8. Neural Manifolds for the Control of Movement.

    Science.gov (United States)

    Gallego, Juan A; Perich, Matthew G; Miller, Lee E; Solla, Sara A

    2017-06-07

    The analysis of neural dynamics in several brain cortices has consistently uncovered low-dimensional manifolds that capture a significant fraction of neural variability. These neural manifolds are spanned by specific patterns of correlated neural activity, the "neural modes." We discuss a model for neural control of movement in which the time-dependent activation of these neural modes is the generator of motor behavior. This manifold-based view of motor cortex may lead to a better understanding of how the brain controls movement. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. Wildland fire as a self-regulating mechanism: the role of previous burns and weather in limiting fire progression.

    Science.gov (United States)

    Parks, Sean A; Holsinger, Lisa M; Miller, Carol; Nelson, Cara R

    2015-09-01

    Theory suggests that natural fire regimes can result in landscapes that are both self-regulating and resilient to fire. For example, because fires consume fuel, they may create barriers to the spread of future fires, thereby regulating fire size. Top-down controls such as weather, however, can weaken this effect. While empirical examples demonstrating this pattern-process feedback between vegetation and fire exist, they have been geographically limited or did not consider the influence of time between fires and weather. The availability of remotely sensed data identifying fire activity over the last four decades provides an opportunity to explicitly quantify-the ability of wildland fire to limit the progression of subsequent fire. Furthermore, advances in fire progression mapping now allow an evaluation of how daily weather as a top-down control modifies this effect. In this study, we evaluated the ability of wildland fire to create barriers that limit the spread of subsequent fire along a gradient representing time between fires in four large study areas in the western United States. Using fire progression maps in conjunction with weather station data, we also evaluated the influence of daily weather. Results indicate that wildland fire does limit subsequent fire spread in all four study areas, but this effect decays over time; wildland fire no longer limits subsequent fire spread 6-18 years after fire, depending on the study area. We also found that the ability of fire to regulate, subsequent fire progression was substantially reduced under extreme conditions compared to moderate weather conditions in all four study areas. This study increases understanding of the spatial feedbacks that can lead to self-regulating landscapes as well as the effects of top-down controls, such as weather, on these feedbacks. Our results will be useful to managers who seek to restore natural fire regimes or to exploit recent burns when managing fire.

  10. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

    2018-02-01

    Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  11. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

    2018-02-01

    Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  12. Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation.

    Science.gov (United States)

    Kim, Ju-Won; Park, Seunghee

    2018-01-02

    In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size.

  13. The neural basis of form and form-motion integration from static and dynamic translational Glass patterns: A rTMS investigation.

    Science.gov (United States)

    Pavan, Andrea; Ghin, Filippo; Donato, Rita; Campana, Gianluca; Mather, George

    2017-08-15

    A long-held view of the visual system is that form and motion are independently analysed. However, there is physiological and psychophysical evidence of early interaction in the processing of form and motion. In this study, we used a combination of Glass patterns (GPs) and repetitive Transcranial Magnetic Stimulation (rTMS) to investigate in human observers the neural mechanisms underlying form-motion integration. GPs consist of randomly distributed dot pairs (dipoles) that induce the percept of an oriented stimulus. GPs can be either static or dynamic. Dynamic GPs have both a form component (i.e., orientation) and a non-directional motion component along the orientation axis. GPs were presented in two temporal intervals and observers were asked to discriminate the temporal interval containing the most coherent GP. rTMS was delivered over early visual area (V1/V2) and over area V5/MT shortly after the presentation of the GP in each interval. The results showed that rTMS applied over early visual areas affected the perception of static GPs, but the stimulation of area V5/MT did not affect observers' performance. On the other hand, rTMS was delivered over either V1/V2 or V5/MT strongly impaired the perception of dynamic GPs. These results suggest that early visual areas seem to be involved in the processing of the spatial structure of GPs, and interfering with the extraction of the global spatial structure also affects the extraction of the motion component, possibly interfering with early form-motion integration. However, visual area V5/MT is likely to be involved only in the processing of the motion component of dynamic GPs. These results suggest that motion and form cues may interact as early as V1/V2. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Fire Safety (For Parents)

    Science.gov (United States)

    ... of Braces Eating Disorders Mitral Valve Prolapse Arrhythmias Fire Safety KidsHealth > For Parents > Fire Safety Print A ... event of a fire emergency in your home. Fire Prevention Of course, the best way to practice ...

  15. Fire Research Enclosure

    Data.gov (United States)

    Federal Laboratory Consortium — FUNCTION: Simulates submarine fires, enclosed aircraft fires, and fires in enclosures at shore facilities .DESCRIPTION: FIRE I is a pressurizable, 324 cu m(11,400 cu...

  16. Active Fire Mapping Program

    Science.gov (United States)

    Active Fire Mapping Program Current Large Incidents (Home) New Large Incidents Fire Detection Maps MODIS Satellite Imagery VIIRS Satellite Imagery Fire Detection GIS Data Fire Data in Google Earth ...

  17. Neural reactivations during sleep determine network credit assignment.

    Science.gov (United States)

    Gulati, Tanuj; Guo, Ling; Ramanathan, Dhakshin S; Bodepudi, Anitha; Ganguly, Karunesh

    2017-09-01

    A fundamental goal of motor learning is to establish the neural patterns that produce a desired behavioral outcome. It remains unclear how and when the nervous system solves this 'credit assignment' problem. Using neuroprosthetic learning, in which we could control the causal relationship between neurons and behavior, we found that sleep-dependent processing was required for credit assignment and the establishment of task-related functional connectivity reflecting the casual neuron-behavior relationship. Notably, we observed a strong link between the microstructure of sleep reactivations and credit assignment, with downscaling of non-causal activity. Decoupling of spiking to slow oscillations using optogenetic methods eliminated rescaling. Thus, our results suggest that coordinated firing during sleep is essential for establishing sparse activation patterns that reflect the causal neuron-behavior relationship.

  18. Fire Synthesis

    Indian Academy of Sciences (India)

    Fire Synthesis - Preparation of Alumina Products. Tanu Mimani. Volume 16 Issue 12 December 2011 pp 1324-1332. Fulltext. Click here to view fulltext PDF. Permanent link: http://www.ias.ac.in/article/fulltext/reso/016/12/1324-1332. Keywords. Alumina; combustion; refractory materials; urea. Author Affiliations. Tanu Mimani1.

  19. Fire Synthesis

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 5; Issue 2. Fire Synthesis - Preparation of Alumina Products. Tanu Mimani. General Article Volume 5 Issue 2 February 2000 pp 50-57. Fulltext. Click here to view fulltext PDF. Permanent link: http://www.ias.ac.in/article/fulltext/reso/005/02/0050-0057 ...

  20. Forest Fires

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 6; Issue 11. Forest Fires - Origins and Ecological Paradoxes. K Narendran. General Article Volume 6 Issue 11 November 2001 pp 34-41. Fulltext. Click here to view fulltext PDF. Permanent link: http://www.ias.ac.in/article/fulltext/reso/006/11/0034-0041 ...

  1. Kv2 Channel Regulation of Action Potential Repolarization and Firing Patterns in Superior Cervical Ganglion Neurons and Hippocampal CA1 Pyramidal Neurons

    Science.gov (United States)

    Liu, Pin W.

    2014-01-01

    Kv2 family “delayed-rectifier” potassium channels are widely expressed in mammalian neurons. Kv2 channels activate relatively slowly and their contribution to action potential repolarization under physiological conditions has been unclear. We explored the function of Kv2 channels using a Kv2-selective blocker, Guangxitoxin-1E (GxTX-1E). Using acutely isolated neurons, mixed voltage-clamp and current-clamp experiments were done at 37°C to study the physiological kinetics of channel gating and action potentials. In both rat superior cervical ganglion (SCG) neurons and mouse hippocampal CA1 pyramidal neurons, 100 nm GxTX-1E produced near-saturating block of a component of current typically constituting ∼60–80% of the total delayed-rectifier current. GxTX-1E also reduced A-type potassium current (IA), but much more weakly. In SCG neurons, 100 nm GxTX-1E broadened spikes and voltage clamp experiments using action potential waveforms showed that Kv2 channels carry ∼55% of the total outward current during action potential repolarization despite activating relatively late in the spike. In CA1 neurons, 100 nm GxTX-1E broadened spikes evoked from −70 mV, but not −80 mV, likely reflecting a greater role of Kv2 when other potassium channels were partially inactivated at −70 mV. In both CA1 and SCG neurons, inhibition of Kv2 channels produced dramatic depolarization of interspike voltages during repetitive firing. In CA1 neurons and some SCG neurons, this was associated with increased initial firing frequency. In all neurons, inhibition of Kv2 channels depressed maintained firing because neurons entered depolarization block more readily. Therefore, Kv2 channels can either decrease or increase neuronal excitability depending on the time scale of excitation. PMID:24695716

  2. Spatial and temporal patterns of the stability and humidity terms in the Haines index to improve the estimate of forest fire risk in the Valencia region of Spain

    OpenAIRE

    Barberà, Maria Jesús; Estrela, María Jesús; Niclòs, Raquel; Valiente, José A.

    2014-01-01

    Ponencia presentada en: IX Congreso de la Asociación Española de Climatología celebrado en Almería entre el 28 y el 30 de octubre de 2014. [EN]The assessment of risk index in the propagation and evolution of a hypothetical forest fire is commonly based on stability and moisture content at different atmospheric levels. The Haines Index combines these terms to determine the environmental potential for wildfire growth. In this study the environmental stability and humidity associated...

  3. Fire Behavior (FB)

    Science.gov (United States)

    Robert E. Keane

    2006-01-01

    The Fire Behavior (FB) method is used to describe the behavior of the fire and the ambient weather and fuel conditions that influence the fire behavior. Fire behavior methods are not plot based and are collected by fire event and time-date. In general, the fire behavior data are used to interpret the fire effects documented in the plot-level sampling. Unlike the other...

  4. The study for practicality of remote fire monitoring using the image

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Tae Joon; Hwang, Sung Tai; Jeong, Kwung Chai; Jeong, Ji Young; Kim, Go Leo; Baik, Hong Kee; Baik, Moon Kee; Kim, Joo Sung; No, In Young

    1999-12-01

    1. Object; The study for practicality of remote fire monitoring system early to be able to the fire with small scaled fire in nuclear facility and commercial building. 2. Content; Examination of algorithm for artificial intelligence neural network(NN), Achieving of image preprocessing technology need to application, Production of image files of firing, Experiment of the feature extraction from images, Construction of experimental equipment and software for discrimination of the fire, Experiment of functionality of software for fire monitoring, Learning of neural network with the image and testing of discrimination of the fire. 3. Results; The technology of feature extraction of event related with neural network, discrimination of event generation, and enhancement to be discriminated the fire with learning of neural network was established. The present ability of discrimination of the fire that the reliability was about 99 percent as error of discrimination being about 0.0098 in case of learning, but it is difficult to discriminate because of various kinds of background images. Later it will be required the working for reducing the error of discrimination of the fire, with non-fire images. (author)

  5. A neural network simulation package in CLIPS

    Science.gov (United States)

    Bhatnagar, Himanshu; Krolak, Patrick D.; Mcgee, Brenda J.; Coleman, John

    1990-01-01

    The intrinsic similarity between the firing of a rule and the firing of a neuron has been captured in this research to provide a neural network development system within an existing production system (CLIPS). A very important by-product of this research has been the emergence of an integrated technique of using rule based systems in conjunction with the neural networks to solve complex problems. The systems provides a tool kit for an integrated use of the two techniques and is also extendible to accommodate other AI techniques like the semantic networks, connectionist networks, and even the petri nets. This integrated technique can be very useful in solving complex AI problems.

  6. Fire Symfonier

    DEFF Research Database (Denmark)

    Nielsen, Svend Hvidtfelt

    2009-01-01

    sidste fire symfonier. Den er måske snarere at opfatte som et præludium til disse. At påstå, at symfonierne fra Holmboes side er planlagt til at være beslægtede, ville være at gå for vidt. Alene de 26 år, der skiller den 10. fra den 13., gør påstanden - i bedste fald - dubiøs. Når deres udformning...... udkrystallisering som i de sidste små 30 år af hans virke har afkastet disse fire variationer over en grundlæggende central holmboesk fornemmelse for form, melodi, klang og rytme. Denne oplevelse har fået mig til at udforske symfonierne, for at finde til bunds i dette holmboeske fællestræk, som jeg mener her står...

  7. Spike Neural Models Part II: Abstract Neural Models

    OpenAIRE

    Johnson, Melissa G.; Chartier, Sylvain

    2018-01-01

    Neurons are complex cells that require a lot of time and resources to model completely. In spiking neural networks (SNN) though, not all that complexity is required. Therefore simple, abstract models are often used. These models save time, use less computer resources, and are easier to understand. This tutorial presents two such models: Izhikevich's model, which is biologically realistic in the resulting spike trains but not in the parameters, and the Leaky Integrate and Fire (LIF) model whic...

  8. Pattern recognition

    CERN Document Server

    Theodoridis, Sergios

    2003-01-01

    Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to ""learn"" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10

  9. Using a stochastic model and cross-scale analysis to evaluate controls on historical low-severity fire regimes

    Science.gov (United States)

    Maureen C. Kennedy; Donald. McKenzie

    2010-01-01

    Fire-scarred trees provide a deep temporal record of historical fire activity, but identifying the mechanisms therein that controlled landscape fire patterns is not straightforward. We use a spatially correlated metric for fire co-occurrence between pairs of trees (the Sørensen distance variogram), with output from a neutral model for fire history, to infer the...

  10. Use of pattern recognition and neural networks for non-metric sex diagnosis from lateral shape of calvarium: an innovative model for computer-aided diagnosis in forensic and physical anthropology.

    Science.gov (United States)

    Cavalli, Fabio; Lusnig, Luca; Trentin, Edmondo

    2017-05-01

    Sex determination on skeletal remains is one of the most important diagnosis in forensic cases and in demographic studies on ancient populations. Our purpose is to realize an automatic operator-independent method to determine the sex from the bone shape and to test an intelligent, automatic pattern recognition system in an anthropological domain. Our multiple-classifier system is based exclusively on the morphological variants of a curve that represents the sagittal profile of the calvarium, modeled via artificial neural networks, and yields an accuracy higher than 80 %. The application of this system to other bone profiles is expected to further improve the sensibility of the methodology.

  11. Fire Safety Training Handbook.

    Science.gov (United States)

    Montgomery County Dept. of Fire and Rescue Services, Rockville, MD. Div. of Fire Prevention.

    Designed for a community fire education effort, particularly in which local volunteers present general information on fire safety to their fellow citizens, this workbook contains nine lessons. Included are an overview of the household fire problem; instruction in basic chemistry and physics of fire, flammable liquids, portable fire extinguishers,…

  12. Assessing fire risk in Portugal during the summer fire season

    Science.gov (United States)

    Dacamara, C. C.; Pereira, M. G.; Trigo, R. M.

    2009-04-01

    steps; 1) a truncated Weibull distribution is fitted to the sample of burned areas and 2) the quality of the fitted statistical model is improved by incorporating components of the FWI System as covariates. Obtained model allows estimating on a daily basis the probability of occurrence of fires larger than a given threshold as well as producing maps of fire risk. Results as obtained from a prototype currently being developed will be presented and discussed. In particular, it will be shown that results provide additional evidence of the known fact that the extent of burned area in Portugal is controlled by two main atmospheric factors (Pereira et al. 2005): i) a long-term control related to the regime of temperature and precipitation in spring and ii) a short-term control exerted by the occurrence of very intense dry spells in days of extreme synoptic situations. Bovio, G., and A. Camia. 1998. An analysis of large forest fire danger conditions in Europe. In Proc. 3rd Int. Conf. on Forest Fire Research & 14th Conf. on Fire and Forest Meteorology, Viegas, D.X. (Ed.), Luso, 16-20 Nov., ADAI, 975-994. Cumming, S.G., 2001. Parametric models of the fire size distribution. Can J. For. Res., 31, 1297-1303. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C. and Leite, S.M., 2005. Synoptic patterns associated with large summer forest fires in Portugal. Agr. and For. Meteorol., 129 (1-2), 11-25. Uppala, S.M. et al., 2005: The ERA-40 re-analysis. Quart. J. R. Meteorol. Soc., 131, 2961-3012. Van Wagner, C.E., 1987. Development and structure of the Canadian forest fire weather index system. Canadian Forestry Service, Forest Technical Report 35, Ottawa, 37 pp.

  13. Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe

    Directory of Open Access Journals (Sweden)

    Eli eShlizerman

    2014-08-01

    Full Text Available The antennal lobe (AL, olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units, and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (i design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (ii characterize scent recognition, i.e., decision-making based on olfactory signals and (iii infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns.

  14. Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe.

    Science.gov (United States)

    Shlizerman, Eli; Riffell, Jeffrey A; Kutz, J Nathan

    2014-01-01

    The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patter