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Sample records for neural firing pattern

  1. Firing patterns transition and desynchronization induced by time delay in neural networks

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    Huang, Shoufang; Zhang, Jiqian; Wang, Maosheng; Hu, Chin-Kun

    2018-06-01

    We used the Hindmarsh-Rose (HR) model (Hindmarsh and Rose, 1984) to study the effect of time delay on the transition of firing behaviors and desynchronization in neural networks. As time delay is increased, neural networks exhibit diversity of firing behaviors, including regular spiking or bursting and firing patterns transitions (FPTs). Meanwhile, the desynchronization of firing and unstable bursting with decreasing amplitude in neural system, are also increasingly enhanced with the increase of time delay. Furthermore, we also studied the effect of coupling strength and network randomness on these phenomena. Our results imply that time delays can induce transition and desynchronization of firing behaviors in neural networks. These findings provide new insight into the role of time delay in the firing activities of neural networks, and can help to better understand the firing phenomena in complex systems of neural networks. A possible mechanism in brain that can cause the increase of time delay is discussed.

  2. Sequentially firing neurons confer flexible timing in neural pattern generators

    International Nuclear Information System (INIS)

    Urban, Alexander; Ermentrout, Bard

    2011-01-01

    Neuronal networks exhibit a variety of complex spatiotemporal patterns that include sequential activity, synchrony, and wavelike dynamics. Inhibition is the primary means through which such patterns are implemented. This behavior is dependent on both the intrinsic dynamics of the individual neurons as well as the connectivity patterns. Many neural circuits consist of networks of smaller subcircuits (motifs) that are coupled together to form the larger system. In this paper, we consider a particularly simple motif, comprising purely inhibitory interactions, which generates sequential periodic dynamics. We first describe the dynamics of the single motif both for general balanced coupling (all cells receive the same number and strength of inputs) and then for a specific class of balanced networks: circulant systems. We couple these motifs together to form larger networks. We use the theory of weak coupling to derive phase models which, themselves, have a certain structure and symmetry. We show that this structure endows the coupled system with the ability to produce arbitrary timing relationships between symmetrically coupled motifs and that the phase relationships are robust over a wide range of frequencies. The theory is applicable to many other systems in biology and physics.

  3. 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.

  4. Effective deep brain stimulation suppresses low frequency network oscillations in the basal ganglia by regularizing neural firing patterns

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    McConnell, George C.; So, Rosa Q.; Hilliard, Justin D; Lopomo, Paola; Grill, Warren M.

    2012-01-01

    Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for the motor symptoms of Parkinson’s disease (PD). The effects of DBS depend strongly on stimulation frequency: high frequencies (>90Hz) improve motor symptoms, while low frequencies (basal ganglia were studied in the unilateral 6-hydroxydopamine lesioned rat model of PD. Only high frequency DBS reversed motor symptoms and the effectiveness of DBS depended strongly on stimulation frequency in a manner reminiscent of its clinical effects in persons with PD. Quantification of single-unit activity in the globus pallidus externa (GPe) and substantia nigra reticulata (SNr) revealed that high frequency DBS, but not low frequency DBS, reduced pathological low frequency oscillations (~9Hz) and entrained neurons to fire at the stimulation frequency. Similarly, the coherence between simultaneously recorded pairs of neurons within and across GPe and SNr shifted from the pathological low frequency band to the stimulation frequency during high frequency DBS, but not during low frequency DBS. The changes in firing patterns in basal ganglia neurons were not correlated with changes in firing rate. These results indicate that high frequency DBS is more effective than low frequency DBS, not as a result of changes in firing rate, but rather due to its ability to replace pathological low frequency network oscillations with a regularized pattern of neuronal firing. PMID:23136407

  5. Effective deep brain stimulation suppresses low-frequency network oscillations in the basal ganglia by regularizing neural firing patterns.

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    McConnell, George C; So, Rosa Q; Hilliard, Justin D; Lopomo, Paola; Grill, Warren M

    2012-11-07

    Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for the motor symptoms of Parkinson's disease (PD). The effects of DBS depend strongly on stimulation frequency: high frequencies (>90 Hz) improve motor symptoms, while low frequencies (basal ganglia were studied in the unilateral 6-hydroxydopamine lesioned rat model of PD. Only high-frequency DBS reversed motor symptoms, and the effectiveness of DBS depended strongly on stimulation frequency in a manner reminiscent of its clinical effects in persons with PD. Quantification of single-unit activity in the globus pallidus externa (GPe) and substantia nigra reticulata (SNr) revealed that high-frequency DBS, but not low-frequency DBS, reduced pathological low-frequency oscillations (∼9 Hz) and entrained neurons to fire at the stimulation frequency. Similarly, the coherence between simultaneously recorded pairs of neurons within and across GPe and SNr shifted from the pathological low-frequency band to the stimulation frequency during high-frequency DBS, but not during low-frequency DBS. The changes in firing patterns in basal ganglia neurons were not correlated with changes in firing rate. These results indicate that high-frequency DBS is more effective than low-frequency DBS, not as a result of changes in firing rate, but rather due to its ability to replace pathological low-frequency network oscillations with a regularized pattern of neuronal firing.

  6. Neural network regulation driven by autonomous neural firings

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    Cho, Myoung Won

    2016-07-01

    Biological neurons naturally fire spontaneously due to the existence of a noisy current. Such autonomous firings may provide a driving force for network formation because synaptic connections can be modified due to neural firings. Here, we study the effect of autonomous firings on network formation. For the temporally asymmetric Hebbian learning, bidirectional connections lose their balance easily and become unidirectional ones. Defining the difference between reciprocal connections as new variables, we could express the learning dynamics as if Ising model spins interact with each other in magnetism. We present a theoretical method to estimate the interaction between the new variables in a neural system. We apply the method to some network systems and find some tendencies of autonomous neural network regulation.

  7. Oscillator Neural Network Retrieving Sparsely Coded Phase Patterns

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    Aoyagi, Toshio; Nomura, Masaki

    1999-08-01

    Little is known theoretically about the associative memory capabilities of neural networks in which information is encoded not only in the mean firing rate but also in the timing of firings. Particularly, in the case of sparsely coded patterns, it is biologically important to consider the timings of firings and to study how such consideration influences storage capacities and quality of recalled patterns. For this purpose, we propose a simple extended model of oscillator neural networks to allow for expression of a nonfiring state. Analyzing both equilibrium states and dynamical properties in recalling processes, we find that the system possesses good associative memory.

  8. Burst firing enhances neural output correlation

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    Ho Ka eChan

    2016-05-01

    Full Text Available Neurons communicate and transmit information predominantly through spikes. Given that experimentally observed neural spike trains in a variety of brain areas can be highly correlated, it is important to investigate how neurons process correlated inputs. Most previous work in this area studied the problem of correlation transfer analytically by making significant simplifications on neural dynamics. Temporal correlation between inputs that arises from synaptic filtering, for instance, is often ignored when assuming that an input spike can at most generate one output spike. Through numerical simulations of a pair of leaky integrate-and-fire (LIF neurons receiving correlated inputs, we demonstrate that neurons in the presence of synaptic filtering by slow synapses exhibit strong output correlations. We then show that burst firing plays a central role in enhancing output correlations, which can explain the above-mentioned observation because synaptic filtering induces bursting. The observed changes of correlations are mostly on a long time scale. Our results suggest that other features affecting the prevalence of neural burst firing in biological neurons, e.g., adaptive spiking mechanisms, may play an important role in modulating the overall level of correlations in neural networks.

  9. The equilibrium of neural firing: A mathematical theory

    Energy Technology Data Exchange (ETDEWEB)

    Lan, Sizhong, E-mail: lsz@fuyunresearch.org [Fuyun Research, Beijing, 100055 (China)

    2014-12-15

    Inspired by statistical thermodynamics, we presume that neuron system has equilibrium condition with respect to neural firing. We show that, even with dynamically changeable neural connections, it is inevitable for neural firing to evolve to equilibrium. To study the dynamics between neural firing and neural connections, we propose an extended communication system where noisy channel has the tendency towards fixed point, implying that neural connections are always attracted into fixed points such that equilibrium can be reached. The extended communication system and its mathematics could be useful back in thermodynamics.

  10. 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.

  11. Patterns of interval correlations in neural oscillators with adaptation

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    Tilo eSchwalger

    2013-11-01

    Full Text Available Neural firing is often subject to negative feedback by adaptationcurrents. These currents can induce strong correlations among the timeintervals between spikes. Here we study analytically the intervalcorrelations of a broad class of noisy neural oscillators withspike-triggered adaptation of arbitrary strength and time scale. Ourweak-noise theory provides a general relation between the correlationsand the phase-response curve (PRC of the oscillator, provesanti-correlations between neighboring intervals for adapting neuronswith type I PRC and identifies a single order parameter thatdetermines the qualitative pattern of correlations. Monotonicallydecaying or oscillating correlation structures can be related toqualitatively different voltage traces after spiking, which can beexplained by the phase plane geometry. At high firing rates, thelong-term variability of the spike train associated with thecumulative interval correlations becomes small, independent of modeldetails. Our results are verified by comparison with stochasticsimulations of the exponential, leaky, and generalizedintegrate-and-fire models with adaptation.

  12. 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.

  13. 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

  14. Firing Patterns and Transitions in Coupled Neurons Controlled by a Pacemaker

    International Nuclear Information System (INIS)

    Mei-Sheng, Li; Qi-Shao, Lu; Li-Xia, Duan; Qing-Yun, Wang

    2008-01-01

    To reveal the dynamics of neuronal networks with pacemakers, the firing patterns and their transitions are investigated in a ring HR neuronal network with gap junctions under the control of a pacemaker. Compared with the situation without pacemaker, the neurons in the network can exhibit various firing patterns as the external current is applied or the coupling strength of pacemaker varies. The results are beneficial for understanding the complex cooperative behaviour of large neural assemblies with pacemaker control

  15. 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.

  16. Neural networks improve performance of coal-fired boilers

    Energy Technology Data Exchange (ETDEWEB)

    Radl, B.J. [Pegasus Technologies Ltd., Painesville, OH (United States)

    1999-03-01

    Work sponsored by the US Department of Energy through its NICE{sup 3} programme, and co-funded by industry partners First Energy Corp. (host organisation and co-funder) and Pegasus Technologies (inventor, developer and supplier), has resulted in the development of online, real-time neural networks which help coal-fired utility boilers to dynamically adjust combustion setpoints. The payoff is a system which helps reduce NOx emissions up to 60%, while improving heat rate up to 2% overall. The system has avoided or postponed large capacity expenditures while meeting environmental compliance requirements. 3 figs., 1 tab.

  17. Neural Sequence Generation Using Spatiotemporal Patterns of Inhibition.

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    Jonathan Cannon

    2015-11-01

    Full Text Available Stereotyped sequences of neural activity are thought to underlie reproducible behaviors and cognitive processes ranging from memory recall to arm movement. One of the most prominent theoretical models of neural sequence generation is the synfire chain, in which pulses of synchronized spiking activity propagate robustly along a chain of cells connected by highly redundant feedforward excitation. But recent experimental observations in the avian song production pathway during song generation have shown excitatory activity interacting strongly with the firing patterns of inhibitory neurons, suggesting a process of sequence generation more complex than feedforward excitation. Here we propose a model of sequence generation inspired by these observations in which a pulse travels along a spatially recurrent excitatory chain, passing repeatedly through zones of local feedback inhibition. In this model, synchrony and robust timing are maintained not through redundant excitatory connections, but rather through the interaction between the pulse and the spatiotemporal pattern of inhibition that it creates as it circulates the network. These results suggest that spatially and temporally structured inhibition may play a key role in sequence generation.

  18. Neural Sequence Generation Using Spatiotemporal Patterns of Inhibition.

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    Cannon, Jonathan; Kopell, Nancy; Gardner, Timothy; Markowitz, Jeffrey

    2015-11-01

    Stereotyped sequences of neural activity are thought to underlie reproducible behaviors and cognitive processes ranging from memory recall to arm movement. One of the most prominent theoretical models of neural sequence generation is the synfire chain, in which pulses of synchronized spiking activity propagate robustly along a chain of cells connected by highly redundant feedforward excitation. But recent experimental observations in the avian song production pathway during song generation have shown excitatory activity interacting strongly with the firing patterns of inhibitory neurons, suggesting a process of sequence generation more complex than feedforward excitation. Here we propose a model of sequence generation inspired by these observations in which a pulse travels along a spatially recurrent excitatory chain, passing repeatedly through zones of local feedback inhibition. In this model, synchrony and robust timing are maintained not through redundant excitatory connections, but rather through the interaction between the pulse and the spatiotemporal pattern of inhibition that it creates as it circulates the network. These results suggest that spatially and temporally structured inhibition may play a key role in sequence generation.

  19. Granular neural networks, pattern recognition and bioinformatics

    CERN Document Server

    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...

  20. 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.

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

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    Armenteras, Dolors; Retana, Javier

    2012-01-01

    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.

  2. Variable synaptic strengths controls the firing rate distribution in feedforward neural networks.

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    Ly, Cheng; Marsat, Gary

    2018-02-01

    Heterogeneity of firing rate statistics is known to have severe consequences on neural coding. Recent experimental recordings in weakly electric fish indicate that the distribution-width of superficial pyramidal cell firing rates (trial- and time-averaged) in the electrosensory lateral line lobe (ELL) depends on the stimulus, and also that network inputs can mediate changes in the firing rate distribution across the population. We previously developed theoretical methods to understand how two attributes (synaptic and intrinsic heterogeneity) interact and alter the firing rate distribution in a population of integrate-and-fire neurons with random recurrent coupling. Inspired by our experimental data, we extend these theoretical results to a delayed feedforward spiking network that qualitatively capture the changes of firing rate heterogeneity observed in in-vivo recordings. We demonstrate how heterogeneous neural attributes alter firing rate heterogeneity, accounting for the effect with various sensory stimuli. The model predicts how the strength of the effective network connectivity is related to intrinsic heterogeneity in such delayed feedforward networks: the strength of the feedforward input is positively correlated with excitability (threshold value for spiking) when firing rate heterogeneity is low and is negatively correlated with excitability with high firing rate heterogeneity. We also show how our theory can be used to predict effective neural architecture. We demonstrate that neural attributes do not interact in a simple manner but rather in a complex stimulus-dependent fashion to control neural heterogeneity and discuss how it can ultimately shape population codes.

  3. Coupling Strength and System Size Induce Firing Activity of Globally Coupled Neural Network

    International Nuclear Information System (INIS)

    Wei Duqu; Luo Xiaoshu; Zou Yanli

    2008-01-01

    We investigate how firing activity of globally coupled neural network depends on the coupling strength C and system size N. Network elements are described by space-clamped FitzHugh-Nagumo (SCFHN) neurons with the values of parameters at which no firing activity occurs. It is found that for a given appropriate coupling strength, there is an intermediate range of system size where the firing activity of globally coupled SCFHN neural network is induced and enhanced. On the other hand, for a given intermediate system size level, there exists an optimal value of coupling strength such that the intensity of firing activity reaches its maximum. These phenomena imply that the coupling strength and system size play a vital role in firing activity of neural network

  4. Pattern formation and firing synchronization in networks of map neurons

    International Nuclear Information System (INIS)

    Wang Qingyun; Duan Zhisheng; Huang Lin; Chen Guanrong; Lu Qishao

    2007-01-01

    Patterns and collective phenomena such as firing synchronization are studied in networks of nonhomogeneous oscillatory neurons and mixtures of oscillatory and excitable neurons, with dynamics of each neuron described by a two-dimensional (2D) Rulkov map neuron. It is shown that as the coupling strength is increased, typical patterns emerge spatially, which propagate through the networks in the form of beautiful target waves or parallel ones depending on the size of networks. Furthermore, we investigate the transitions of firing synchronization characterized by the rate of firing when the coupling strength is increased. It is found that there exists an intermediate coupling strength; firing synchronization is minimal simultaneously irrespective of the size of networks. For further increasing the coupling strength, synchronization is enhanced. Since noise is inevitable in real neurons, we also investigate the effects of white noise on firing synchronization for different networks. For the networks of oscillatory neurons, it is shown that firing synchronization decreases when the noise level increases. For the missed networks, firing synchronization is robust under the noise conditions considered in this paper. Results presented in this paper should prove to be valuable for understanding the properties of collective dynamics in real neuronal networks

  5. Adrenergic receptor-mediated modulation of striatal firing patterns.

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    Ohta, Hiroyuki; Kohno, Yu; Arake, Masashi; Tamura, Risa; Yukawa, Suguru; Sato, Yoshiaki; Morimoto, Yuji; Nishida, Yasuhiro; Yawo, Hiromu

    2016-11-01

    Although noradrenaline and adrenaline are some of the most important neurotransmitters in the central nervous system, the effects of noradrenergic/adrenergic modulation on the striatum have not been determined. In order to explore the effects of adrenergic receptor (AR) agonists on the striatal firing patterns, we used optogenetic methods which can induce continuous firings. We employed transgenic rats expressing channelrhodopsin-2 (ChR2) in neurons. The medium spiny neuron showed a slow rising depolarization during the 1-s long optogenetic striatal photostimulation and a residual potential with 8.6-s half-life decay after the photostimulation. As a result of the residual potential, five repetitive 1-sec long photostimulations with 20-s onset intervals cumulatively increased the number of spikes. This 'firing increment', possibly relating to the timing control function of the striatum, was used to evaluate the AR modulation. The β-AR agonist isoproterenol decreased the firing increment between the 1st and 5th stimulation cycles, while the α 1 -AR agonist phenylephrine enhanced the firing increment. Isoproterenol and adrenaline increased the early phase (0-0.5s of the photostimulation) firing response. This adrenergic modulation was inhibited by the β-antagonist propranolol. Conversely, phenylephrine and noradrenaline reduced the early phase response. β-ARs and α 1 -ARs work in opposition controlling the striatal firing initiation and the firing increment. Copyright © 2016 Elsevier Ireland Ltd and Japan Neuroscience Society. All rights reserved.

  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. Pattern recognition of state variables by neural networks

    International Nuclear Information System (INIS)

    Faria, Eduardo Fernandes; Pereira, Claubia

    1996-01-01

    An artificial intelligence system based on artificial neural networks can be used to classify predefined events and emergency procedures. These systems are being used in different areas. In the nuclear reactors safety, the goal is the classification of events whose data can be processed and recognized by neural networks. In this works we present a preliminary simple system, using neural networks in the recognition of patterns the recognition of variables which define a situation. (author)

  8. 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

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    Lee, Inah; Kim, Jangjin

    2010-01-01

    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…

  9. Firing patterns in the adaptive exponential integrate-and-fire model.

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    Naud, Richard; Marcille, Nicolas; Clopath, Claudia; Gerstner, Wulfram

    2008-11-01

    For simulations of large spiking neuron networks, an accurate, simple and versatile single-neuron modeling framework is required. Here we explore the versatility of a simple two-equation model: the adaptive exponential integrate-and-fire neuron. We show that this model generates multiple firing patterns depending on the choice of parameter values, and present a phase diagram describing the transition from one firing type to another. We give an analytical criterion to distinguish between continuous adaption, initial bursting, regular bursting and two types of tonic spiking. Also, we report that the deterministic model is capable of producing irregular spiking when stimulated with constant current, indicating low-dimensional chaos. Lastly, the simple model is fitted to real experiments of cortical neurons under step current stimulation. The results provide support for the suitability of simple models such as the adaptive exponential integrate-and-fire neuron for large network simulations.

  10. Spatiotemporal distribution patterns of forest fires in northern Mexico

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    Gustavo Pérez-Verdin; M. A. Márquez-Linares; A. Cortes-Ortiz; M. Salmerón-Macias

    2013-01-01

    Using the 2000-2011 CONAFOR databases, a spatiotemporal analysis of the occurrence of forest fires in Durango, one of the most affected States in Mexico, was conducted. The Moran's index was used to determine a spatial distribution pattern; also, an analysis of seasonal and temporal autocorrelation of the data collected was completed. The geographically weighted...

  11. 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

  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. 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.

  14. Relating neuronal firing patterns to functional differentiation of cerebral cortex.

    Directory of Open Access Journals (Sweden)

    Shigeru Shinomoto

    2009-07-01

    Full Text Available It has been empirically established that the cerebral cortical areas defined by Brodmann one hundred years ago solely on the basis of cellular organization are closely correlated to their function, such as sensation, association, and motion. Cytoarchitectonically distinct cortical areas have different densities and types of neurons. Thus, signaling patterns may also vary among cytoarchitectonically unique cortical areas. To examine how neuronal signaling patterns are related to innate cortical functions, we detected intrinsic features of cortical firing by devising a metric that efficiently isolates non-Poisson irregular characteristics, independent of spike rate fluctuations that are caused extrinsically by ever-changing behavioral conditions. Using the new metric, we analyzed spike trains from over 1,000 neurons in 15 cortical areas sampled by eight independent neurophysiological laboratories. Analysis of firing-pattern dissimilarities across cortical areas revealed a gradient of firing regularity that corresponded closely to the functional category of the cortical area; neuronal spiking patterns are regular in motor areas, random in the visual areas, and bursty in the prefrontal area. Thus, signaling patterns may play an important role in function-specific cerebral cortical computation.

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

    Science.gov (United States)

    Danny L. Fry; Scott L. Stephens; Brandon M. Collins; Malcolm North; Ernesto Franco-Vizcaino; Samantha J. Gill

    2014-01-01

    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...

  16. Dynamic analysis and pattern visualization of forest fires.

    Science.gov (United States)

    Lopes, António M; Tenreiro Machado, J A

    2014-01-01

    This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.

  17. 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...

  18. 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.

  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. A hierarchical fire frequency model to simulate temporal patterns of fire regimes in LANDIS

    Science.gov (United States)

    Jian Yang; Hong S. He; Eric J. Gustafson

    2004-01-01

    Fire disturbance has important ecological effects in many forest landscapes. Existing statistically based approaches can be used to examine the effects of a fire regime on forest landscape dynamics. Most examples of statistically based fire models divide a fire occurrence into two stages--fire ignition and fire initiation. However, the exponential and Weibull fire-...

  1. Mixed-severity fire fosters heterogeneous spatial patterns of conifer regeneration in a dry conifer forest

    Science.gov (United States)

    Sparkle L. Malone; Paula J. Fornwalt; Mike A. Battaglia; Marin E. Chambers; Jose M. Iniguez; Carolyn H. Sieg

    2018-01-01

    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...

  2. GABAB-receptor activation alters the firing pattern of dopamine neurons in the rat substantia nigra.

    Science.gov (United States)

    Engberg, G; Kling-Petersen, T; Nissbrandt, H

    1993-11-01

    Previous electrophysiological experiments have emphasized the importance of the firing pattern for the functioning of midbrain dopamine (DA) neurons. In this regard, excitatory amino acid receptors appear to constitute an important modulatory control mechanism. In the present study, extracellular recording techniques were used to investigate the significance of GABAB-receptor activation for the firing properties of DA neurons in the substantia nigra (SN) in the rat. Intravenous administration of the GABAB-receptor agonist baclofen (1-16 mg/kg) was associated with a dose-dependent regularization of the firing pattern, concomitant with a reduction in burst firing. At higher doses (16-32 mg/kg), the firing rate of the DA neurons was dose-dependently decreased. Also, microiontophoretic application of baclofen regularized the firing pattern of nigral DA neurons, including a reduction of burst firing. Both the regularization of the firing pattern and inhibition of firing rate produced by systemic baclofen administration was antagonized by the GABAB-receptor antagonist CGP 35348 (200 mg/kg, i.v.). The GABAA-receptor agonist muscimol produced effects on the firing properties of DA neurons that were opposite to those observed following baclofen, i.e., an increase in firing rate accompanied by a decreased regularity. The NMDA receptor antagonist MK 801 (0.4-3.2 mg/kg, i.v.) produced a moderate, dose-dependent increase in the firing rate of the nigral DA neurons as well as a slightly regularized firing pattern. Pretreatment with MK 801 (3.2 mg/kg, i.v., 3-10 min) did neither promote nor prevent the regularization of the firing pattern or inhibition of firing rate on the nigral DA neurons produced by baclofen. The present results clearly show that GABAB-receptors can alter the firing pattern of nigral DA neurons, hereby counterbalancing the previously described ability of glutamate to induce burst firing activity on these neurons.

  3. Contrasting Spatial Patterns in Active-Fire and Fire-Suppressed Mediterranean Climate Old-Growth Mixed Conifer Forests

    OpenAIRE

    Fry, Danny L.; Stephens, Scott L.; Collins, Brandon M.; North, Malcolm P.; Franco-Vizcaíno, Ernesto; Gill, Samantha J.

    2014-01-01

    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 disturbanc...

  4. Fire history and pattern in a Cascade Range landscape.

    Science.gov (United States)

    Peter H. Morrison; Frederick J. Swanson

    1990-01-01

    Fire history from years 1150 to 1985 was reconstructed by analyzing forest stands in two 1940-hectare areas in the central-western Cascade Range of Oregon. Serving as records for major fire episodes, these stands revealed a highly variable fire regime. The steeper, more dissected, lower elevation Cook-Quentin study area experienced more frequent fires (natural fire...

  5. Neural Global Pattern Similarity Underlies True and False Memories.

    Science.gov (United States)

    Ye, Zhifang; Zhu, Bi; Zhuang, Liping; Lu, Zhonglin; Chen, Chuansheng; Xue, Gui

    2016-06-22

    The neural processes giving rise to human memory strength signals remain poorly understood. Inspired by formal computational models that posit a central role of global matching in memory strength, we tested a novel hypothesis that the strengths of both true and false memories arise from the global similarity of an item's neural activation pattern during retrieval to that of all the studied items during encoding (i.e., the encoding-retrieval neural global pattern similarity [ER-nGPS]). We revealed multiple ER-nGPS signals that carried distinct information and contributed differentially to true and false memories: Whereas the ER-nGPS in the parietal regions reflected semantic similarity and was scaled with the recognition strengths of both true and false memories, ER-nGPS in the visual cortex contributed solely to true memory. Moreover, ER-nGPS differences between the parietal and visual cortices were correlated with frontal monitoring processes. By combining computational and neuroimaging approaches, our results advance a mechanistic understanding of memory strength in recognition. What neural processes give rise to memory strength signals, and lead to our conscious feelings of familiarity? Using fMRI, we found that the memory strength of a given item depends not only on how it was encoded during learning, but also on the similarity of its neural representation with other studied items. The global neural matching signal, mainly in the parietal lobule, could account for the memory strengths of both studied and unstudied items. Interestingly, a different global matching signal, originated from the visual cortex, could distinguish true from false memories. The findings reveal multiple neural mechanisms underlying the memory strengths of events registered in the brain. Copyright © 2016 the authors 0270-6474/16/366792-11$15.00/0.

  6. Classification of data patterns using an autoassociative neural network topology

    Science.gov (United States)

    Dietz, W. E.; Kiech, E. L.; Ali, M.

    1989-01-01

    A diagnostic expert system based on neural networks is developed and applied to the real-time diagnosis of jet and rocket engines. The expert system methodologies are based on the analysis of patterns of behavior of physical mechanisms. In this approach, fault diagnosis is conceptualized as the mapping or association of patterns of sensor data to patterns representing fault conditions. The approach addresses deficiencies inherent in many feedforward neural network models and greatly reduces the number of networks necessary to identify the existence of a fault condition and estimate the duration and severity of the identified fault. The network topology used in the present implementation of the diagnostic system is described, as well as the training regimen used and the response of the system to inputs representing both previously observed and unknown fault scenarios. Noise effects on the integrity of the diagnosis are also evaluated.

  7. Learning-induced pattern classification in a chaotic neural network

    International Nuclear Information System (INIS)

    Li, Yang; Zhu, Ping; Xie, Xiaoping; He, Guoguang; Aihara, Kazuyuki

    2012-01-01

    In this Letter, we propose a Hebbian learning rule with passive forgetting (HLRPF) for use in a chaotic neural network (CNN). We then define the indices based on the Euclidean distance to investigate the evolution of the weights in a simplified way. Numerical simulations demonstrate that, under suitable external stimulations, the CNN with the proposed HLRPF acts as a fuzzy-like pattern classifier that performs much better than an ordinary CNN. The results imply relationship between learning and recognition. -- Highlights: ► Proposing a Hebbian learning rule with passive forgetting (HLRPF). ► Defining indices to investigate the evolution of the weights simply. ► The chaotic neural network with HLRPF acts as a fuzzy-like pattern classifier. ► The pattern classifier ability of the network is improved much.

  8. Histomorphological patterns in osseous rests exposed at fire

    International Nuclear Information System (INIS)

    Medina, C.; Tiesler, V.; Oliva, A.I.; Quintana, P.

    2005-01-01

    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)

  9. [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.

  10. 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.

  11. 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.

  12. Spatial patterns of modern period human-caused fire occurrence in the Missouri Ozark Highlands

    Science.gov (United States)

    Jian Yang; Hong S. Healy; Stephen R. Shifley; Eric J. Gustafson

    2007-01-01

    The spatial pattern of forest fire locations is important in the study of the dynamics of fire disturbance. In this article we used a spatial point process modeling approach to quantitatively study the effects of land cover, topography, roads, municipalities, ownership, and population density on fire occurrence reported between 1970 and 2002 in the Missouri Ozark...

  13. 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.

  14. UNDERSTANDING THE SPATIO-TEMPORAL PATTERN OF FIRE DISTURBANCE IN THE EASTERN MONGOLIA USING MODIS PRODUCT

    OpenAIRE

    Wurihan; Zhang, H.; Zhang, Z.; Guo, X.; Zhao, J.; Duwala; Shan, Y.; Hongying

    2018-01-01

    Fire disturbance plays an important role in maintaining ecological balance, biodiversity and self-renewal. In this paper, the spatio-temporal pattern of fire disturbances in eastern Mongolia are studied by using the ArcGIS spatial analysis method, using the MCD45A1 data of MODIS fire products with long time series. It provides scientific basis and reference for the regional ecological environment security construction and international ecological security. Research indicates: (1) The fire dis...

  15. Post-fire vegetation and fuel development influences fire severity patterns in reburns.

    Science.gov (United States)

    Coppoletta, Michelle; Merriam, Kyle E; Collins, Brandon M

    2016-04-01

    In areas where fire regimes and forest structure have been dramatically altered, there is increasing concern that contemporary fires have the potential to set forests on a positive feedback trajectory with successive reburns, one in which extensive stand-replacing fire could promote more stand-replacing fire. Our study utilized an extensive set of field plots established following four fires that occurred between 2000 and 2010 in the northern Sierra Nevada, California, USA that were subsequently reburned in 2012. The information obtained from these field plots allowed for a unique set of analyses investigating the effect of vegetation, fuels, topography, fire weather, and forest management on reburn severity. We also examined the influence of initial fire severity and time since initial fire on influential predictors of reburn severity. Our results suggest that high- to moderate-severity fire in the initial fires led to an increase in standing snags and shrub vegetation, which in combination with severe fire weather promoted high-severity fire effects in the subsequent reburn. Although fire behavior is largely driven by weather, our study demonstrates that post-fire vegetation composition and structure are also important drivers of reburn severity. In the face of changing climatic regimes and increases in extreme fire weather, these results may provide managers with options to create more fire-resilient ecosystems. In areas where frequent high-severity fire is undesirable, management activities such as thinning, prescribed fire, or managed wildland fire can be used to moderate fire behavior not only prior to initial fires, but also before subsequent reburns.

  16. A hidden Markov model approach to neuron firing patterns.

    Science.gov (United States)

    Camproux, A C; Saunier, F; Chouvet, G; Thalabard, J C; Thomas, G

    1996-11-01

    Analysis and characterization of neuronal discharge patterns are of interest to neurophysiologists and neuropharmacologists. In this paper we present a hidden Markov model approach to modeling single neuron electrical activity. Basically the model assumes that each interspike interval corresponds to one of several possible states of the neuron. Fitting the model to experimental series of interspike intervals by maximum likelihood allows estimation of the number of possible underlying neuron states, the probability density functions of interspike intervals corresponding to each state, and the transition probabilities between states. We present an application to the analysis of recordings of a locus coeruleus neuron under three pharmacological conditions. The model distinguishes two states during halothane anesthesia and during recovery from halothane anesthesia, and four states after administration of clonidine. The transition probabilities yield additional insights into the mechanisms of neuron firing.

  17. Optimization of patterns of control bars using neural networks

    International Nuclear Information System (INIS)

    Mejia S, D.M.; Ortiz S, J.J.

    2005-01-01

    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)

  18. Photopolymerized materials and patterning for improved performance of neural prosthetics

    Science.gov (United States)

    Tuft, Bradley William

    correlates with the maximum feature slope. Neurite alignment is compared on unpatterned, unidirectional, and multidirectional photopolymerized micropatterns. The effect of substrate rigidity on neurite alignment to physical cues was determined by maintaining equivalent pattern microfeatures, afforded by the reaction control of photopolymerization, while concomitantly altering the composition of several copolymer platforms to tune matrix stiffness. For each platform, neurite alignment to unidirectional patterns increases with increasing substrate rigidity. Interestingly, SGN neurites respond to material stiffness cues that are orders of magnitude higher (GPa) than what is typically ascribed to neural environments (kPa). Finally, neurite behavior at bioactive borders of various adhesion modulating molecules was evaluated on micropatterned materials to determine which cues took precedence in establishing neurite directionality. At low microfeatures aspect ratios, neurites align to the pattern direction but are then caused to turn and repel from or turn and align to bioactive borders. Conversely, physical cues dominate neurite path-finding as pattern feature slope increases, i.e. aspect ratio of sloping photopolymerized features increases, causing neurites to readily cross bioactive borders. The photopolymerization method developed in this work to generate micro and nanopatterned materials serves as an additional surface engineering tool that enables investigation of cell-material interactions including directed de novo neurite growth. The results of this interdisciplinary effort contribute substantially to polymer neural regeneration technology and will lead to development of advanced biomaterials that improve neural prosthetic tissue integration and performance by spatially directing nerve growth.

  19. Spatial patterns of persistent neural activity vary with the behavioral context of short-term memory.

    Science.gov (United States)

    Daie, Kayvon; Goldman, Mark S; Aksay, Emre R F

    2015-02-18

    A short-term memory can be evoked by different inputs and control separate targets in different behavioral contexts. To address the circuit mechanisms underlying context-dependent memory function, we determined through optical imaging how memory is encoded at the whole-network level in two behavioral settings. Persistent neural activity maintaining a memory of desired eye position was imaged throughout the oculomotor integrator after saccadic or optokinetic stimulation. While eye position was encoded by the amplitude of network activity, the spatial patterns of firing were context dependent: cells located caudally generally were most persistent following saccadic input, whereas cells located rostrally were most persistent following optokinetic input. To explain these data, we computationally identified four independent modes of network activity and found these were differentially accessed by saccadic and optokinetic inputs. These results show how a circuit can simultaneously encode memory value and behavioral context, respectively, in its amplitude and spatial pattern of persistent firing. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Sleep/wake firing patterns of human genioglossus motor units.

    Science.gov (United States)

    Bailey, E Fiona; Fridel, Keith W; Rice, Amber D

    2007-12-01

    Although studies of the principal tongue protrudor muscle genioglossus (GG) suggest that whole muscle GG electromyographic (EMG) activities are preserved in nonrapid eye movement (NREM) sleep, it is unclear what influence sleep exerts on individual GG motor unit (MU) activities. We characterized the firing patterns of human GG MUs in wakefulness and NREM sleep with the aim of determining 1) whether the range of MU discharge patterns evident in wakefulness is preserved in sleep and 2) what effect the removal of the "wakefulness" input has on the magnitude of the respiratory modulation of MU activities. Microelectrodes inserted into the extrinsic tongue protrudor muscle, the genioglossus, were used to follow the discharge of single MUs. We categorized MU activities on the basis of the temporal relationship between the spike train and the respiration cycle and quantified the magnitude of the respiratory modulation of each MU using the eta (eta(2)) index, in wakefulness and sleep. The majority of MUs exhibited subtle increases or decreases in respiratory modulation but were otherwise unaffected by NREM sleep. In contrast, 30% of MUs exhibited marked sleep-associated changes in discharge frequency and respiratory modulation. We suggest that GG MUs should not be considered exclusively tonic or phasic; rather, the discharge pattern appears to be a flexible feature of GG activities in healthy young adults. Whether such flexibility is important in the response to changes in the chemical and/or mechanical environment and whether it is preserved as a function of aging or in individuals with obstructive sleep apnea are critical questions for future research.

  1. 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.

  2. 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.

  3. Regulated expression of the neural cell adhesion molecule L1 by specific patterns of neural impulses.

    Science.gov (United States)

    Itoh, K; Stevens, B; Schachner, M; Fields, R D

    1995-11-24

    Development of the mammalian nervous system is regulated by neural impulse activity, but the molecular mechanisms are not well understood. If cell recognition molecules [for example, L1 and the neural cell adhesion molecule (NCAM)] were influenced by specific patterns of impulse activity, cell-cell interactions controlling nervous system structure could be regulated by nervous system function at critical stages of development. Low-frequency electrical pulses delivered to mouse sensory neurons in culture (0.1 hertz for 5 days) down-regulated expression of L1 messenger RNA and protein (but not NCAM). Fasciculation of neurites, adhesion of neuroblastoma cells, and the number of Schwann cells on neurites was reduced after 0.1-hertz stimulation, but higher frequencies or stimulation after synaptogenesis were without effect.

  4. Transformed Neural Pattern Reinstatement during Episodic Memory Retrieval.

    Science.gov (United States)

    Xiao, Xiaoqian; Dong, Qi; Gao, Jiahong; Men, Weiwei; Poldrack, Russell A; Xue, Gui

    2017-03-15

    Contemporary models of episodic memory posit that remembering involves the reenactment of encoding processes. Although encoding-retrieval similarity has been consistently reported and linked to memory success, the nature of neural pattern reinstatement is poorly understood. Using high-resolution fMRI on human subjects, our results obtained clear evidence for item-specific pattern reinstatement in the frontoparietal cortex, even when the encoding-retrieval pairs shared no perceptual similarity. No item-specific pattern reinstatement was found in the ventral visual cortex. Importantly, the brain regions and voxels carrying item-specific representation differed significantly between encoding and retrieval, and the item specificity for encoding-retrieval similarity was smaller than that for encoding or retrieval, suggesting different nature of representations between encoding and retrieval. Moreover, cross-region representational similarity analysis suggests that the encoded representation in the ventral visual cortex was reinstated in the frontoparietal cortex during retrieval. Together, these results suggest that, in addition to reinstatement of the originally encoded pattern in the brain regions that perform encoding processes, retrieval may also involve the reinstatement of a transformed representation of the encoded information. These results emphasize the constructive nature of memory retrieval that helps to serve important adaptive functions. SIGNIFICANCE STATEMENT Episodic memory enables humans to vividly reexperience past events, yet how this is achieved at the neural level is barely understood. A long-standing hypothesis posits that memory retrieval involves the faithful reinstatement of encoding-related activity. We tested this hypothesis by comparing the neural representations during encoding and retrieval. We found strong pattern reinstatement in the frontoparietal cortex, but not in the ventral visual cortex, that represents visual details. Critically

  5. Climate controls on fire pattern in African and Australian continents

    Science.gov (United States)

    Zubkova, M.; Boschetti, L.; Abatzoglou, J. T.

    2017-12-01

    Studies have primarily attributed the recent decrease in global fire activity in many savanna and grassland regions as detected by the Global Fire Emission Database (GFEDv4s) to anthropogenic changes such as deforestation and cropland expansion (Andela et al. 2017, van der Werf et al. 2008). These changes have occurred despite increases in fire weather season length (Jolly et al. 2015). Efforts to better resolve retrospective and future changes in fire activity require refining the host of influences on societal and environmental factors on fire activity. In this study, we analyzed how climate variability influences interannual fire activity in Africa and Australia, the two continents most affected by fire and responsible for over half of the global pyrogenic emissions. We expand on the analysis presented in Andela et al. (2017) by using the most recent Collection 6 MODIS MCD64 Burned Area Product and exploring the explanatory power of a broader suite of climate variables that have been previously shown to explain fire variability (Bowman et al. 2017). We examined which climate metrics show a strong interannual relationship with the amount of burned area and fire size accounting for antecedent and in-season atmospheric conditions. Fire characteristics were calculated using the 500m resolution MCD64A1 product (2002-2016); the analysis was conducted at the ecoregion scale, and further stratified by landcover using a broad aggregation (forest, shrublands and grasslands) of the Landcover CCI maps (CCI-LC, 2014); all agricultural areas fires were excluded from the analysis. The results of the analysis improve our knowledge of climate controls on fire dynamics in the most fire-prone places in the world which is critical for statistical fire and vegetation models. Being able to predict the impact of climate on fire activity has a strategic importance in designing future fire management scenarios, help to avoid degradation of biodiversity and ecosystem services and improve

  6. New Solutions to the Firing Squad Synchronization Problems for Neural and Hyperdag P Systems

    Directory of Open Access Journals (Sweden)

    Michael J. Dinneen

    2009-11-01

    Full Text Available We propose two uniform solutions to an open question: the Firing Squad Synchronization Problem (FSSP, for hyperdag and symmetric neural P systems, with anonymous cells. Our solutions take e_c+5 and 6e_c+7 steps, respectively, where e_c is the eccentricity of the commander cell of the dag or digraph underlying these P systems. The first and fast solution is based on a novel proposal, which dynamically extends P systems with mobile channels. The second solution is substantially longer, but is solely based on classical rules and static channels. In contrast to the previous solutions, which work for tree-based P systems, our solutions synchronize to any subset of the underlying digraph; and do not require membrane polarizations or conditional rules, but require states, as typically used in hyperdag and neural P systems.

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

    Science.gov (United States)

    Fry, Danny L; Stephens, Scott L; Collins, Brandon M; North, Malcolm P; Franco-Vizcaíno, Ernesto; Gill, Samantha J

    2014-01-01

    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.

  8. 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.

  9. 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.

  10. Predicting the effect of fire on large-scale vegetation patterns in North America.

    Science.gov (United States)

    Donald McKenzie; David L. Peterson; Ernesto. Alvarado

    1996-01-01

    Changes in fire regimes are expected across North America in response to anticipated global climatic changes. Potential changes in large-scale vegetation patterns are predicted as a result of altered fire frequencies. A new vegetation classification was developed by condensing Kuchler potential natural vegetation types into aggregated types that are relatively...

  11. The cluster analysis based on non-teacher artificial neural network for the danger prediction of coal spontaneous fire

    Energy Technology Data Exchange (ETDEWEB)

    Wang, D.; Wang, J. [China University of Mining and Technology (China)

    1999-04-01

    This paper focuses on the problem of predicting the danger level of spontaneous fire in coal mines. Firstly, the inadequacy of the present artificial neural networks prediction model is analysed. Then a new cluster model based on non-teacher neural network is constructed according to the danger judgement standards given by experts. On this basis, by adopting the error square sum criterion and its algorithm, the corresponding prediction software is developed and applied in two working faces of Chaili Coal Mine. The forecasting result is importantly significant for the prevention of spontaneous fire. 4 refs., 1 fig., 1 tab.

  12. 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.

  13. 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.

  14. Differential theory of learning for efficient neural network pattern recognition

    Science.gov (United States)

    Hampshire, John B., II; Vijaya Kumar, Bhagavatula

    1993-09-01

    We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generate well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

  15. Neural pattern similarity underlies the mnemonic advantages for living words.

    Science.gov (United States)

    Xiao, Xiaoqian; Dong, Qi; Chen, Chuansheng; Xue, Gui

    2016-06-01

    It has been consistently shown that words representing living things are better remembered than words representing nonliving things, yet the underlying cognitive and neural mechanisms have not been clearly elucidated. The present study used both univariate and multivariate pattern analyses to examine the hypotheses that living words are better remembered because (1) they draw more attention and/or (2) they share more overlapping semantic features. Subjects were asked to study a list of living and nonliving words during a semantic judgment task. An unexpected recognition test was administered 30 min later. We found that subjects recognized significantly more living words than nonliving words. Results supported the overlapping semantic feature hypothesis by showing that (a) semantic ratings showed greater semantic similarity for living words than for nonliving words, (b) there was also significantly greater neural global pattern similarity (nGPS) for living words than for nonliving words in the posterior portion of left parahippocampus (LpPHG), (c) the nGPS in the LpPHG reflected the rated semantic similarity, and also mediated the memory differences between two semantic categories, and (d) greater univariate activation was found for living words than for nonliving words in the left hippocampus (LHIP), which mediated the better memory performance for living words and might reflect greater semantic context binding. In contrast, although living words were processed faster and elicited a stronger activity in the dorsal attention network, these differences did not mediate the animacy effect in memory. Taken together, our results provide strong support to the overlapping semantic features hypothesis, and emphasize the important role of semantic organization in episodic memory encoding. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. Understanding the Spatio-Temporal Pattern of Fire Disturbance in the Eastern Mongolia Using Modis Product

    Science.gov (United States)

    Wurihan; Zhang, H.; Zhang, Z.; Guo, X.; Zhao, J.; Duwala; Shan, Y.; Hongying

    2018-04-01

    Fire disturbance plays an important role in maintaining ecological balance, biodiversity and self-renewal. In this paper, the spatio-temporal pattern of fire disturbances in eastern Mongolia are studied by using the ArcGIS spatial analysis method, using the MCD45A1 data of MODIS fire products with long time series. It provides scientific basis and reference for the regional ecological environment security construction and international ecological security. Research indicates: (1) The fire disturbance in eastern Mongolia has obvious high and low peak interleaving phenomenon in the year, and the seasonal change is obvious. (2) The distribution pattern of fire disturbance in eastern Mongolia is aggregated, which indicates that the fire disturbance is not random and it is caused by certain influence. (3) Fire disturbance is mainly distributed in the eastern province of Mongolia, the border between China and Mongolia and the northern forest area of Sukhbaatar province. (4) The fire disturbance in the eastern part of the study area is strong and the southwest is weaker. The spreading regularity of fire disturbances in eastern Mongolia is closer to the natural level of ecosystem.

  17. UNDERSTANDING THE SPATIO-TEMPORAL PATTERN OF FIRE DISTURBANCE IN THE EASTERN MONGOLIA USING MODIS PRODUCT

    Directory of Open Access Journals (Sweden)

    Wurihan

    2018-04-01

    Full Text Available Fire disturbance plays an important role in maintaining ecological balance, biodiversity and self-renewal. In this paper, the spatio-temporal pattern of fire disturbances in eastern Mongolia are studied by using the ArcGIS spatial analysis method, using the MCD45A1 data of MODIS fire products with long time series. It provides scientific basis and reference for the regional ecological environment security construction and international ecological security. Research indicates: (1 The fire disturbance in eastern Mongolia has obvious high and low peak interleaving phenomenon in the year, and the seasonal change is obvious. (2 The distribution pattern of fire disturbance in eastern Mongolia is aggregated, which indicates that the fire disturbance is not random and it is caused by certain influence. (3 Fire disturbance is mainly distributed in the eastern province of Mongolia, the border between China and Mongolia and the northern forest area of Sukhbaatar province. (4 The fire disturbance in the eastern part of the study area is strong and the southwest is weaker. The spreading regularity of fire disturbances in eastern Mongolia is closer to the natural level of ecosystem.

  18. Linking vegetation patterns to potential smoke production and fire hazard

    Science.gov (United States)

    Roger D. Ottmar; Ernesto Alvarado

    2004-01-01

    During the past 80 years, various disturbances (such as wildfire and wind events) and management actions (including fire exclusion, logging, and domestic livestock grazing) have significantly modified the composition and structure of forests and ranges across the western United States. The resulting fuel loadings directly influence potential smoke production from...

  19. NEURAL PROGENITORS, PATTERNING AND ECOLOGY IN NEOCORTICAL ORIGINS

    Directory of Open Access Journals (Sweden)

    Francisco eAboitiz

    2013-11-01

    Full Text Available 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.

  20. Neural breathing pattern in newborn infants pre- and postextubation.

    Science.gov (United States)

    Iyer, Narayan P; Dickson, John; Ruiz, Michelle E; Chatburn, Robert; Beck, Jennifer; Sinderby, Chister; Rodriguez, Ricardo J

    2017-12-01

    To describe the neural breathing pattern before and after extubation in newborn infants. Prospective, observational study. In infants deemed ready for extubation, the diaphragm electrical activity (EAdi) was continuously recorded from 30 minute before to two hours after extubation. Total of 25 neonates underwent 29 extubations; 10 extubations resulted in re-intubation within 72 hours. Postextubation, there was an increase in peak EAdi (EAdi-max) and EAdi-delta (peak minus minimum EAdi) in both groups. The pre- to postextubation change in EAdi-max (8.9-11.1 μv) and EAdi-delta (6-8 μv) was less in the failure group in comparison with the change in EAdi-max (10.2-13.4 μv) and EAdi-delta (6.3-10.6 μv) in the success group, (p = 0.02 and 0.01, respectively). In our neonatal cohort, extubation failure was associated with a smaller increase in peak and delta EAdi after extubation. If confirmed, these findings indicate an important cause of extubation failure in preterm infants. ©2017 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.

  1. Comparison of eye imaging pattern recognition using neural network

    Science.gov (United States)

    Bukhari, W. M.; Syed A., M.; Nasir, M. N. M.; Sulaima, M. F.; Yahaya, M. S.

    2015-05-01

    The beauty of eye recognition system that it is used in automatic identifying and verifies a human weather from digital images or video source. There are various behaviors of the eye such as the color of the iris, size of pupil and shape of the eye. This study represents the analysis, design and implementation of a system for recognition of eye imaging. All the eye images that had been captured from the webcam in RGB format must through several techniques before it can be input for the pattern and recognition processes. The result shows that the final value of weight and bias after complete training 6 eye images for one subject is memorized by the neural network system and be the reference value of the weight and bias for the testing part. The target classifies to 5 different types for 5 subjects. The eye images can recognize the subject based on the target that had been set earlier during the training process. When the values between new eye image and the eye image in the database are almost equal, it is considered the eye image is matched.

  2. FIRE

    International Nuclear Information System (INIS)

    Brtis, J.S.; Hausheer, T.G.

    1990-01-01

    FIRE, a microcomputer based program to assist engineers in reviewing and documenting the fire protection impact of design changes has been developed. Acting as an electronic consultant, FIRE is designed to work with an experienced nuclear system engineer, who may not have any detailed fire protection expertise. FIRE helps the engineer to decide if a modification might adversely affect the fire protection design of the station. Since its first development, FIRE has been customized to reflect the fire protection philosophy of the Commonwealth Edison Company. That program is in early production use. This paper discusses the FIRE program in light of its being a useful application of expert system technologies in the power industry

  3. 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.

  4. Motor unit firing rate patterns during voluntary muscle force generation: a simulation study

    Science.gov (United States)

    Hu, Xiaogang; Rymer, William Z.; Suresh, Nina L.

    2014-04-01

    Objective. Muscle force is generated by a combination of motor unit (MU) recruitment and changes in the discharge rate of active MUs. There have been two basic MU recruitment and firing rate paradigms reported in the literature, which describe the control of the MUs during force generation. The first (termed the reverse ‘onion skin’ profile), exhibits lower firing rates for lower threshold units, with higher firing rates occurring in higher threshold units. The second (termed the ‘onion skin’ profile), exhibits an inverse arrangement, with lower threshold units reaching higher firing rates. Approach. Using a simulation of the MU activity in a hand muscle, this study examined the force generation capacity and the variability of the muscle force magnitude at different excitation levels of the MU pool under these two different MU control paradigms. We sought to determine which rate/recruitment scheme was more efficient for force generation, and which scheme gave rise to the lowest force variability. Main results. We found that the force output of both firing patterns leads to graded force output at low excitation levels, and that the force generation capacity of the two different paradigms diverged around 50% excitation. In the reverse ‘onion skin’ pattern, at 100% excitation, the force output reached up to 88% of maximum force, whereas for the ‘onion skin’ pattern, the force output only reached up to 54% of maximum force at 100% excitation. The force variability was lower at the low to moderate force levels under the ‘onion skin’ paradigm than with the reverse ‘onion skin’ firing patterns, but this effect was reversed at high force levels. Significance. This study captures the influence of MU recruitment and firing rate organization on muscle force properties, and our results suggest that the different firing organizations can be beneficial at different levels of voluntary muscle force generation and perhaps for different tasks.

  5. Post-fire recovery of torpor and activity patterns of a small mammal.

    Science.gov (United States)

    Stawski, Clare; Hume, Taylor; Körtner, Gerhard; Currie, Shannon E; Nowack, Julia; Geiser, Fritz

    2017-05-01

    To cope with the post-fire challenges of decreased availability of food and shelter, brown antechinus ( Antechinus stuartii ), a small marsupial mammal, increase the use of energy-conserving torpor and reduce activity. However, it is not known how long it takes for animals to resume pre-fire torpor and activity patterns during the recovery of burnt habitat. Therefore, we tested the hypothesis that antechinus will adjust torpor use and activity after a fire depending on vegetation recovery. We simultaneously quantified torpor and activity patterns for female antechinus from three adjacent areas: (i) the area of a management burn 1 year post-fire, (ii) an area that was burned 2 years prior, and (iii) a control area. In comparison to shortly after the management burn, antechinus in all three groups displayed less frequent and less pronounced torpor while being more active. We provide the first evidence that only 1 year post-fire antechinus resume pre-fire torpor and activity patterns, probably in response to the return of herbaceous ground cover and foraging opportunities. © 2017 The Author(s).

  6. 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

  7. 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

  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

    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....... Feed-forward back-propagation neural networks indicate that sociodemographic characteristics of drivers and victims, accident location, and period of the day are extremely relevant factors. Accident patterns suggest that countermeasures are necessary for identified problems concerning mainly vulnerable...

  9. Activity patterns of cultured neural networks on micro electrode arrays

    NARCIS (Netherlands)

    Rutten, Wim; van Pelt, J.

    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 (ventral motor region or dorsal sensory region). It consists of an array of micro electrodes on

  10. Localized states in an unbounded neural field equation with smooth firing rate function: a multi-parameter analysis.

    Science.gov (United States)

    Faye, Grégory; Rankin, James; Chossat, Pascal

    2013-05-01

    The existence of spatially localized solutions in neural networks is an important topic in neuroscience as these solutions are considered to characterize working (short-term) memory. We work with an unbounded neural network represented by the neural field equation with smooth firing rate function and a wizard hat spatial connectivity. Noting that stationary solutions of our neural field equation are equivalent to homoclinic orbits in a related fourth order ordinary differential equation, we apply normal form theory for a reversible Hopf bifurcation to prove the existence of localized solutions; further, we present results concerning their stability. Numerical continuation is used to compute branches of localized solution that exhibit snaking-type behaviour. We describe in terms of three parameters the exact regions for which localized solutions persist.

  11. 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...

  12. Forest and Land Fire Prevention Through the Hotspot Movement Pattern Approach

    Science.gov (United States)

    Turmudi, T.; Kardono, P.; Hartanto, P.; Ardhitasari, Y.

    2018-02-01

    Indonesia has experienced a great forest fire disaster in 2015. The losses incurred were enormous. But actually the incidence of forest and land fires occurs almost every year. Various efforts were made to cope with the fire disaster. The appearance of a hotspot becomes an early indication of the fire incident both location and time. By studying the location and time of the hotspot's appearance indicates that the hotspot has certain movement patterns from year to year. This study aims to show the pattern of movement of hotspots from year to year that can be used for the prevention of forest and land fires. The method used is time series analysis of land cover and hotspot distribution. The data used were land cover data from 2005 to 2016, hotspot data from 2005 to 2016. The location of this study is the territory of Meranti Kepulauan District. The results show that the highest hotspot is 425 hotspots occurs in the shrubs and bushes. From year to year, the pattern of hotspot movement occurs in the shrubs and bushes cover. The hotspot pattern follows the direction of unused land for cultivation and is dominated by shrubs. From these results, we need to pay more attentiont for the land with the cover of shrubs adjacent to the cultivated land.

  13. 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

  14. Firing patterns of spontaneously active motor units in spinal cord-injured subjects.

    Science.gov (United States)

    Zijdewind, Inge; Thomas, Christine K

    2012-04-01

    Involuntary motor unit activity at low rates is common in hand muscles paralysed by spinal cord injury. Our aim was to describe these patterns of motor unit behaviour in relation to motoneurone and motor unit properties. Intramuscular electromyographic activity (EMG), surface EMG and force were recorded for 30 min from thenar muscles of nine men with chronic cervical SCI. Motor units fired for sustained periods (>10 min) at regular (coefficient of variation ≤ 0.15, CV, n =19 units) or irregular intervals (CV>0.15, n =14). Regularly firing units started and stopped firing independently suggesting that intrinsic motoneurone properties were important for recruitment and derecruitment. Recruitment (3.6 Hz, SD 1.2), maximal (10.2 Hz, SD 2.3, range: 7.5-15.4 Hz) and derecruitment frequencies were low (3.3 Hz, SD 1.6), as were firing rate increases after recruitment (~20 intervals in 3 s). Once active, firing often covaried, promoting the idea that units received common inputs.Half of the regularly firing units showed a very slow decline (>40 s) in discharge before derecruitment and had interspike intervals longer than their estimated after hyperpolarisation potential (AHP) duration (estimated by death rate and breakpoint analyses). The other units were derecruited more abruptly and had shorter estimated AHP durations. Overall, regularly firing units had longer estimated AHP durations and were weaker than irregularly firing units, suggesting they were lower threshold units. Sustained firing of units at regular rates may reflect activation of persistent inward currents, visible here in the absence of voluntary drive, whereas irregularly firing units may only respond to synaptic noise.

  15. 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.

  16. 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

  17. Levels and patterns of polycyclic aromatic hydrocarbons (PAHs) in soils after forest fires in South Korea.

    Science.gov (United States)

    Kim, Eun Jung; Choi, Sung-Deuk; Chang, Yoon-Seok

    2011-11-01

    To investigate the influence of biomass burning on the levels of polycyclic aromatic hydrocarbons (PAHs) in soils, temporal trends and profiles of 16 US Environmental Protection Agency priority PAHs were studied in soil and ash samples collected 1, 5, and 9 months after forest fires in South Korea. The levels of PAHs in the burnt soils 1 month after the forest fires (mean, 1,200 ng/g dry weight) were comparable with those of contaminated urban soils. However, 5 and 9 months after the forest fires, these levels decreased considerably to those of general forest soils (206 and 302 ng/g, respectively). The burnt soils and ash were characterized by higher levels of light PAHs with two to four rings, reflecting direct emissions from biomass burning. Five and 9 months after the forest fires, the presence of naphthalene decreased considerably, which indicates that light PAHs were rapidly volatilized or degraded from the burnt soils. The temporal trend and pattern of PAHs clearly suggests that soils in the forest-fire region can be contaminated by PAHs directly emitted from biomass burning. However, the fire-affected soils can return to the pre-fire conditions over time through the washout and wind dissipation of the ash with high content of PAHs as well as vaporization or degradation of light PAHs.

  18. Landscape Patterns of Burn Severity in the Soberanes Fire of 2016

    Science.gov (United States)

    Potter, Christopher

    2016-01-01

    The Soberanes Fire started on July 22, 2016 in Monterey County on the California Central Coast from an illegal campfire. This fire burned for 10 weeks at a record cost of more than $208 million for protection and control. A progressive analysis of the normalized burn ratio from the Landsat satellite showed that the final high burn severity (HBS) area for the Soberanes Fire comprised 22 percent of the total area burned, whereas final moderate burn severity (MBS) area comprised about 10 percent of the total area burned of approximately 53,470 ha (132,130 acres). The resulting landscape pattern of burn severity classes from the 2016 Soberanes Fire revealed that the majority of HBS area was located in the elevation zone between 500 and 1000 m, in the slope zone between 15 percent and 30 percent, or on south-facing aspects.

  19. Pattern recognition neural-net by spatial mapping of biology visual field

    Science.gov (United States)

    Lin, Xin; Mori, Masahiko

    2000-05-01

    The method of spatial mapping in biology vision field is applied to artificial neural networks for pattern recognition. By the coordinate transform that is called the complex-logarithm mapping and Fourier transform, the input images are transformed into scale- rotation- and shift- invariant patterns, and then fed into a multilayer neural network for learning and recognition. The results of computer simulation and an optical experimental system are described.

  20. Fire patterns in the range of the greater sage-grouse, 1984-2013 — Implications for conservation and management

    Science.gov (United States)

    Brooks, Matthew L.; Matchett, John R.; Shinneman, Douglas J.; Coates, Peter S.

    2015-09-10

    Fire ranks among the top three threats to the greater sage-grouse (Centrocercus urophasianus) throughout its range, and among the top two threats in the western part of its range. The national research strategy for this species and the recent U.S. Department of the Interior Secretarial Order 3336 call for science-based threats assessment of fire to inform conservation planning and fire management efforts. The cornerstone of such assessments is a clear understanding of where fires are occurring and what aspects of fire regimes may be shifting outside of their historical range of variation. This report fulfills this need by describing patterns of fire area, fire size, fire rotation, and fire season length and timing from 1984 to 2013 across the range of the greater sage-grouse. This information need is further addressed by evaluating the ecological and management implications of these fire patterns. Analyses are stratified by major vegetation types and the seven greater sage-grouse management zones, delineated regionally as four western and three eastern management zones. Soil temperature and moisture indicators of resilience to fire and resistance to cheatgrass invasion, and the potential for establishment of a grass/fire cycle, are used as unifying concepts in developing fire threat assessments for each analysis strata.

  1. 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.

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

    African Journals Online (AJOL)

    A two-layer feed-forward neural network with Hyperbolic tangent sigmoid ... The neural network model when subjected to test, using the validation input data; ... Proportional Integral Derivative (PID) Controller is used to control the neural ...

  3. Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks.

    Science.gov (United States)

    Goudar, Vishwa; Buonomano, Dean V

    2018-03-14

    Much of the information the brain processes and stores is temporal in nature-a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds-we show that this temporal invariance emerges because the recurrent dynamics generate neural trajectories with appropriately modulated angular velocities. Together our results generate testable predictions as to how recurrent networks may use different mechanisms to generalize across the relevant spatial and temporal features of complex time-varying stimuli. © 2018, Goudar et al.

  4. Amphioxus and lamprey AP-2 genes: implications for neural crest evolution and migration patterns

    Science.gov (United States)

    Meulemans, Daniel; Bronner-Fraser, Marianne

    2002-01-01

    The neural crest is a uniquely vertebrate cell type present in the most basal vertebrates, but not in cephalochordates. We have studied differences in regulation of the neural crest marker AP-2 across two evolutionary transitions: invertebrate to vertebrate, and agnathan to gnathostome. Isolation and comparison of amphioxus, lamprey and axolotl AP-2 reveals its extensive expansion in the vertebrate dorsal neural tube and pharyngeal arches, implying co-option of AP-2 genes by neural crest cells early in vertebrate evolution. Expression in non-neural ectoderm is a conserved feature in amphioxus and vertebrates, suggesting an ancient role for AP-2 genes in this tissue. There is also common expression in subsets of ventrolateral neurons in the anterior neural tube, consistent with a primitive role in brain development. Comparison of AP-2 expression in axolotl and lamprey suggests an elaboration of cranial neural crest patterning in gnathostomes. However, migration of AP-2-expressing neural crest cells medial to the pharyngeal arch mesoderm appears to be a primitive feature retained in all vertebrates. Because AP-2 has essential roles in cranial neural crest differentiation and proliferation, the co-option of AP-2 by neural crest cells in the vertebrate lineage was a potentially crucial event in vertebrate evolution.

  5. Landscape-scale patterns of fire and drought on the high plains, USA

    Science.gov (United States)

    Paulette Ford; Charles Jackson; Matthew Reeves; Benjamin Bird; Dave Turner

    2015-01-01

    We examine 31 years (1982-2012) of temperature, precipitation and natural wildfire occurrence data for Federal and Tribal lands to determine landscape-scale patterns of drought and fire on the southern and central High Plains of the western United States. The High Plains states of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas and...

  6. 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.

  7. Fire and Spillage Risk Assessment Pattern in Scientific Laboratories

    OpenAIRE

    Manouchehr Omidvari; N. Mansouri

    2015-01-01

        Material hazards are the most important risk in scientific laboratories. In risk assessment processing, the potential impact of assessor personal judgment is the most important issue. This study tried to develop a risk assessment pattern based on Failure Mode and Effect Analysis (FMEA) and Analytical Hierarchy Process (AHP) logics and empirical data in scientific laboratories. The most important issues were high pressure reservoirs and hardware failure fuel. The other type of data about b...

  8. 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.

  9. Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks.

    Science.gov (United States)

    Zhang, WenJun

    2007-07-01

    Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance

  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

    2018-04-01

    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. 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,

  12. 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

    The baroreceptor neurons serve as the primary transducers of blood pressure for the autonomic nervous system and are thus critical in enabling the body to respond effectively to changes in blood pressure. These neurons can be separated into two types (A and C) based on the myelination...... 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...

  13. 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.

  14. 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...

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

    Science.gov (United States)

    Qu, Jing; Qian, Liu; Chen, Chuansheng; Xue, Gui; Li, Huiling; Xie, Peng; Mei, Leilei

    2017-01-01

    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. PMID:28878640

  16. 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.

  17. 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...

  18. 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.

  19. Compact holographic optical neural network system for real-time pattern recognition

    Science.gov (United States)

    Lu, Taiwei; Mintzer, David T.; Kostrzewski, Andrew A.; Lin, Freddie S.

    1996-08-01

    One of the important characteristics of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced in the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections that can be implemented with 1D electronic wires. High-resolution pattern recognition problems can require a large number of neurons for parallel processing of an image. This paper describes a holographic optical neural network (HONN) that is based on high- resolution volume holographic materials and is capable of performing massive 3D parallel interconnection of tens of thousands of neurons. A HONN with more than 16,000 neurons packaged in an attache case has been developed. Rotation- shift-scale-invariant pattern recognition operations have been demonstrated with this system. System parameters such as the signal-to-noise ratio, dynamic range, and processing speed are discussed.

  20. 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...

  1. Remotely-sensed active fire data for protected area management: eight-year patterns in the Manas National Park, India.

    Science.gov (United States)

    Takahata, Chihiro; Amin, Rajan; Sarma, Pranjit; Banerjee, Gitanjali; Oliver, William; Fa, John E

    2010-02-01

    The Terai-Duar savanna and grasslands, which once extended along most of the Himalayan foothills, now only remain in a number of protected areas. Within these localities, grassland burning is a major issue, but data on frequency and distribution of fires are limited. Here, we analysed the incidence of active fires, which only occur during the dry season (Nov.-Mar.), within a significant area of Terai grasslands: the Manas National Park (MNP), India. We obtained locations of 781 fires during the 2000-2008 dry seasons, from the Fire Information for Resource Management System (FIRMS) that delivers global MODIS hotspot/fire locations using remote sensing and GIS technologies. Annual number of fires rose significantly from around 20 at the start of the study period to over 90 after 2002, with most (85%) detected between December and January. Over half of the fires occurred in tall grasslands, but fire density was highest in wetland and riverine vegetation, dry at the time. Most burning took place near rivers, roads and the park boundary, suggesting anthropogenic origins. A kernel density map of all recorded fires indicated three heavily burnt areas in the MNP, all within the tall grasslands. Our study demonstrates, despite some technical caveats linked to fire detection technology, which is improving, that remote fire data can be a practical tool in understanding fire concentration and burning temporal patterns in highly vulnerable habitats, useful in guiding management.

  2. 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

  3. Deep brain stimulation changes basal ganglia output nuclei firing pattern in the dystonic hamster.

    Science.gov (United States)

    Leblois, Arthur; Reese, René; Labarre, David; Hamann, Melanie; Richter, Angelika; Boraud, Thomas; Meissner, Wassilios G

    2010-05-01

    Dystonia is a heterogeneous syndrome of movement disorders characterized by involuntary muscle contractions leading to abnormal movements and postures. While medical treatment is often ineffective, deep brain stimulation (DBS) of the internal pallidum improves dystonia. Here, we studied the impact of DBS in the entopeduncular nucleus (EP), the rodent equivalent of the human globus pallidus internus, on basal ganglia output in the dt(sz)-hamster, a well-characterized model of dystonia by extracellular recordings. Previous work has shown that EP-DBS improves dystonic symptoms in dt(sz)-hamsters. We report that EP-DBS changes firing pattern in the EP, most neurons switching to a less regular firing pattern during DBS. In contrast, EP-DBS did not change the average firing rate of EP neurons. EP neurons display multiphasic responses to each stimulation impulse, likely underlying the disruption of their firing rhythm. Finally, neurons in the substantia nigra pars reticulata display similar responses to EP-DBS, supporting the idea that EP-DBS affects basal ganglia output activity through the activation of common afferent fibers. Copyright 2010 Elsevier Inc. All rights reserved.

  4. 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

  5. A Parallel Supercomputer Implementation of a Biological Inspired Neural Network and its use for Pattern Recognition

    International Nuclear Information System (INIS)

    De Ladurantaye, Vincent; Lavoie, Jean; Bergeron, Jocelyn; Parenteau, Maxime; Lu Huizhong; Pichevar, Ramin; Rouat, Jean

    2012-01-01

    A parallel implementation of a large spiking neural network is proposed and evaluated. The neural network implements the binding by synchrony process using the Oscillatory Dynamic Link Matcher (ODLM). Scalability, speed and performance are compared for 2 implementations: Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA) running on clusters of multicore supercomputers and NVIDIA graphical processing units respectively. A global spiking list that represents at each instant the state of the neural network is described. This list indexes each neuron that fires during the current simulation time so that the influence of their spikes are simultaneously processed on all computing units. Our implementation shows a good scalability for very large networks. A complex and large spiking neural network has been implemented in parallel with success, thus paving the road towards real-life applications based on networks of spiking neurons. MPI offers a better scalability than CUDA, while the CUDA implementation on a GeForce GTX 285 gives the best cost to performance ratio. When running the neural network on the GTX 285, the processing speed is comparable to the MPI implementation on RQCHP's Mammouth parallel with 64 notes (128 cores).

  6. Rotation-invariant neural pattern recognition system with application to coin recognition.

    Science.gov (United States)

    Fukumi, M; Omatu, S; Takeda, F; Kosaka, T

    1992-01-01

    In pattern recognition, it is often necessary to deal with problems to classify a transformed pattern. A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. The system was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that the approach works well for variable rotation pattern recognition.

  7. 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

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

    Science.gov (United States)

    Bauermeister, Christoph; Schwalger, Tilo; Russell, David F; Neiman, Alexander B; Lindner, Benjamin

    2013-01-01

    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.

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

    International Nuclear Information System (INIS)

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

    2016-01-01

    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. (paper)

  10. 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.

  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

    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.

  12. The chronotron: a neuron that learns to fire temporally precise spike patterns.

    Directory of Open Access Journals (Sweden)

    Răzvan V Florian

    Full Text Available 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.

  13. Numerical analysis of a neural network with hierarchically organized patterns

    International Nuclear Information System (INIS)

    Bacci, Silvia; Wiecko, Cristina; Parga, Nestor

    1988-01-01

    A numerical analysis of the retrieval behaviour of an associative memory model where the memorized patterns are stored hierarchically is performed. It is found that the model is able to categorize errors. For a finite number of categories, these are retrieved correctly even when the stored patterns are not. Instead, when they are allowed to increase with the number of neurons, their retrieval quality deteriorates above a critical category capacity. (Author)

  14. Chimeras in leaky integrate-and-fire neural networks: effects of reflecting connectivities

    Science.gov (United States)

    Tsigkri-DeSmedt, Nefeli Dimitra; Hizanidis, Johanne; Schöll, Eckehard; Hövel, Philipp; Provata, Astero

    2017-07-01

    The effects of attracting-nonlocal and reflecting connectivity are investigated in coupled Leaky Integrate-and-Fire (LIF) elements, which model the exchange of electrical signals between neurons. Earlier investigations have demonstrated that repulsive-nonlocal and hierarchical network connectivity can induce complex synchronization patterns and chimera states in systems of coupled oscillators. In the LIF system we show that if the elements are nonlocally linked with positive diffusive coupling on a ring network, the system splits into a number of alternating domains. Half of these domains contain elements whose potential stays near the threshold and they are interrupted by active domains where the elements perform regular LIF oscillations. The active domains travel along the ring with constant velocity, depending on the system parameters. When we introduce reflecting coupling in LIF networks unexpected complex spatio-temporal structures arise. For relatively extensive ranges of parameter values, the system splits into two coexisting domains: one where all elements stay near the threshold and one where incoherent states develop, characterized by multi-leveled mean phase velocity profiles.

  15. Designing a Pattern Recognition Neural Network with a Reject Output and Many Sets of Weights and Biases

    OpenAIRE

    Dung, Le; Mizukawa, Makoto

    2008-01-01

    Adding the reject output to the pattern recognition neural network is an approach to help the neural network can classify almost all patterns of a training data set by using many sets of weights and biases, even if the neural network is small. With a smaller number of neurons, we can implement the neural network on a hardware-based platform more easily and also reduce the response time of it. With the reject output the neural network can produce not only right or wrong results but also reject...

  16. Organization of Anti-Phase Synchronization Pattern in Neural Networks: What are the Key Factors?

    Science.gov (United States)

    Li, Dong; Zhou, Changsong

    2011-01-01

    Anti-phase oscillation has been widely observed in cortical neural network. Elucidating the mechanism underlying the organization of anti-phase pattern is of significance for better understanding more complicated pattern formations in brain networks. In dynamical systems theory, the organization of anti-phase oscillation pattern has usually been considered to relate to time delay in coupling. This is consistent to conduction delays in real neural networks in the brain due to finite propagation velocity of action potentials. However, other structural factors in cortical neural network, such as modular organization (connection density) and the coupling types (excitatory or inhibitory), could also play an important role. In this work, we investigate the anti-phase oscillation pattern organized on a two-module network of either neuronal cell model or neural mass model, and analyze the impact of the conduction delay times, the connection densities, and coupling types. Our results show that delay times and coupling types can play key roles in this organization. The connection densities may have an influence on the stability if an anti-phase pattern exists due to the other factors. Furthermore, we show that anti-phase synchronization of slow oscillations can be achieved with small delay times if there is interaction between slow and fast oscillations. These results are significant for further understanding more realistic spatiotemporal dynamics of cortico-cortical communications. PMID:22232576

  17. Vector neural net identifying many strongly distorted and correlated patterns

    Science.gov (United States)

    Kryzhanovsky, Boris V.; Mikaelian, Andrei L.; Fonarev, Anatoly B.

    2005-01-01

    We suggest an effective and simple algorithm providing a polynomial storage capacity of a network of the form M ~ N2s+1, where N is the dimension of the stored binary patterns. In this problem the value of the free parameter s is restricted by the inequalities N >> slnN >= 1. The algorithm allows us to identify a large number of highly distorted similar patterns. The negative influence of correlations of the patterns is suppressed by choosing a sufficiently large value of the parameter s. We show the efficiency of the algorithm by the example of a perceptron identifier, but it also can be used to increase the storage capacity of full connected systems of associative memory.

  18. 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.

  19. Optimization of the kernel functions in a probabilistic neural network analyzing the local pattern distribution.

    Science.gov (United States)

    Galleske, I; Castellanos, J

    2002-05-01

    This article proposes a procedure for the automatic determination of the elements of the covariance matrix of the gaussian kernel function of probabilistic neural networks. Two matrices, a rotation matrix and a matrix of variances, can be calculated by analyzing the local environment of each training pattern. The combination of them will form the covariance matrix of each training pattern. This automation has two advantages: First, it will free the neural network designer from indicating the complete covariance matrix, and second, it will result in a network with better generalization ability than the original model. A variation of the famous two-spiral problem and real-world examples from the UCI Machine Learning Repository will show a classification rate not only better than the original probabilistic neural network but also that this model can outperform other well-known classification techniques.

  20. Rotation and scale change invariant point pattern relaxation matching by the Hopfield neural network

    Science.gov (United States)

    Sang, Nong; Zhang, Tianxu

    1997-12-01

    Relaxation matching is one of the most relevant methods for image matching. The original relaxation matching technique using point patterns is sensitive to rotations and scale changes. We improve the original point pattern relaxation matching technique to be invariant to rotations and scale changes. A method that makes the Hopfield neural network perform this matching process is discussed. An advantage of this is that the relaxation matching process can be performed in real time with the neural network's massively parallel capability to process information. Experimental results with large simulated images demonstrate the effectiveness and feasibility of the method to perform point patten relaxation matching invariant to rotations and scale changes and the method to perform this matching by the Hopfield neural network. In addition, we show that the method presented can be tolerant to small random error.

  1. COMMUNICATION Designing a somatosensory neural prosthesis: percepts evoked by different patterns of thalamic stimulation

    Science.gov (United States)

    Heming, Ethan; Sanden, Andrew; Kiss, Zelma H. T.

    2010-12-01

    Although major advances have been made in the development of motor prostheses, fine motor control requires intuitive somatosensory feedback. Here we explored whether a thalamic site for a somatosensory neural prosthetic could provide natural somatic sensation to humans. Different patterns of electrical stimulation (obtained from thalamic spike trains) were applied in patients undergoing deep brain stimulation surgery. Changes in pattern produced different sensations, while preserving somatotopic representation. While most percepts were reported as 'unnatural', some stimulations produced more 'natural' sensations than others. However, the additional patterns did not elicit more 'natural' percepts than high-frequency (333 Hz) electrical stimulation. These features suggest that despite some limitations, the thalamus may be a feasible site for a somatosensory neural prosthesis and different stimulation patterns may be useful in its development.

  2. Definition of new 3D invariants. Applications to pattern recognition problems with neural networks

    International Nuclear Information System (INIS)

    Proriol, J.

    1996-01-01

    We propose a definition of new 3D invariants. Usual pattern recognition methods use 2D descriptions of 3D objects, we propose a 2D approximation of the defined 3D invariants which can be used with neural networks to solve pattern recognition problems. We describe some methods to use the 2 D approximants. This work is an extension of previous 3D invariants used to solve some high energy physics problems. (author)

  3. 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’

  4. 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…

  5. Play It Again: Neural Responses to Reunion with Excluders Predicted by Attachment Patterns

    Science.gov (United States)

    White, Lars O.; Wu, Jia; Borelli, Jessica L.; Mayes, Linda C.; Crowley, Michael J.

    2013-01-01

    Reunion behavior following stressful separations from caregivers is often considered the single most sensitive clue to infant attachment patterns. Extending these ideas to middle childhood/early adolescence, we examined participants' neural responses to reunion with peers who had previously excluded them. We recorded event-related potentials…

  6. 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…

  7. Sociocultural patterning of neural activity during self-reflection.

    Science.gov (United States)

    Ma, Yina; Bang, Dan; Wang, Chenbo; Allen, Micah; Frith, Chris; Roepstorff, Andreas; Han, Shihui

    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 magnetic resonance imaging, we monitored neural responses from adults in East Asian (Chinese) and Western (Danish) cultural contexts during judgments of social, mental and physical attributes of themselves and public figures to assess cultural influences on self-referential processing of personal attributes in different dimensions. We found 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-reflection by changing the weight of the mPFC and TPJ in the social brain network.

  8. Horizontal two phase flow pattern identification by neural networks

    International Nuclear Information System (INIS)

    Crivelaro, Kelen Cristina Oliveira; Seleghim Junior, Paulo; Hervieu, Eric

    1999-01-01

    A multiphase fluid can flow according to several flow regimes. The problem associated with multiphase systems are basically related to the behavior of macroscopic parameters, such as pressure drop, thermal exchanges and so on, and their strong correlation to the flow regime. From the industrial applications point of view, the safety and longevity of equipment and systems can only be assured when they work according to the flow regimes for which they were designed to. This implies in the need to diagnose flow regimes in real time. The automatic diagnosis of flow regimes represents an objective of extreme importance, mainly for applications on nuclear and petrochemical industries. In this work, a neural network is used in association to a probe of direct visualization for the identification of a gas-liquid flow horizontal regimes, developed in an experimental circuit. More specifically, the signals produced by the probe are used to compose a qualitative image of the flow, which is promptly sent to the network for the recognition of the regimes. Results are presented for different transitions among the flow regimes, which demonstrate the extremely satisfactory performance of the diagnosis system. (author)

  9. 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.

  10. 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.

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

    Science.gov (United States)

    2016-11-29

    rhythmic patterns. As such, our models are appropriate for describing various phenomena in the auditory system, including critical nonlinear...several distinct intrinsic behaviors available near a Hopf bifurcation or a Bautin (a.k.a. double limit cycle) bifurcation. Stability analysis shows...example the perception of pitch at event timescales (Meddis & O’Mard, 2006) and the perception of pulse and meter at rhythmic timescales (Large

  12. Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks.

    Science.gov (United States)

    Gardner, Brian; Sporea, Ioana; Grüning, André

    2015-12-01

    Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question. Here we examine how multilayered networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a new supervised learning rule, MultilayerSpiker, that can train spiking networks containing hidden layer neurons to perform transformations between spatiotemporal input and output spike patterns. The performance of the proposed learning rule is demonstrated in terms of the number of pattern mappings it can learn, the complexity of network structures it can be used on, and its classification accuracy when using multispike-based encodings. In particular, the learning rule displays robustness against input noise and can generalize well on an example data set. Our approach contributes to both a systematic understanding of how computations might take place in the nervous system and a learning rule that displays strong technical capability.

  13. Similar patterns of neural activity predict memory function during encoding and retrieval.

    Science.gov (United States)

    Kragel, James E; Ezzyat, Youssef; Sperling, Michael R; Gorniak, Richard; Worrell, Gregory A; Berry, Brent M; Inman, Cory; Lin, Jui-Jui; Davis, Kathryn A; Das, Sandhitsu R; Stein, Joel M; Jobst, Barbara C; Zaghloul, Kareem A; Sheth, Sameer A; Rizzuto, Daniel S; Kahana, Michael J

    2017-07-15

    Neural networks that span the medial temporal lobe (MTL), prefrontal cortex, and posterior cortical regions are essential to episodic memory function in humans. Encoding and retrieval are supported by the engagement of both distinct neural pathways across the cortex and common structures within the medial temporal lobes. However, the degree to which memory performance can be determined by neural processing that is common to encoding and retrieval remains to be determined. To identify neural signatures of successful memory function, we administered a delayed free-recall task to 187 neurosurgical patients implanted with subdural or intraparenchymal depth electrodes. We developed multivariate classifiers to identify patterns of spectral power across the brain that independently predicted successful episodic encoding and retrieval. During encoding and retrieval, patterns of increased high frequency activity in prefrontal, MTL, and inferior parietal cortices, accompanied by widespread decreases in low frequency power across the brain predicted successful memory function. Using a cross-decoding approach, we demonstrate the ability to predict memory function across distinct phases of the free-recall task. Furthermore, we demonstrate that classifiers that combine information from both encoding and retrieval states can outperform task-independent models. These findings suggest that the engagement of a core memory network during either encoding or retrieval shapes the ability to remember the past, despite distinct neural interactions that facilitate encoding and retrieval. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. 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

  15. Transition of spiral calcium waves between multiple stable patterns can be triggered by a single calcium spark in a fire-diffuse-fire model

    Science.gov (United States)

    Tang, Ai-Hui; Wang, Shi-Qiang

    2009-01-01

    Spiral patterns have been found in various nonequilibrium systems. The Ca2+-induced Ca2+ release system in single cardiac cells is unique for highly discrete reaction elements, each giving rise to a Ca2+ spark upon excitation. We imaged the spiral Ca2+ waves in isolated cardiac cells and numerically studied the effect of system excitability on spiral patterns using a two-dimensional fire-diffuse-fire model. We found that under certain conditions, the system was able to display multiple stable patterns of spiral waves, each exhibiting different periods and distinct routines of spiral tips. Transition between these different patterns could be triggered by an internal fluctuation in the form of a single Ca2+ spark. PMID:19792039

  16. Transition of spiral calcium waves between multiple stable patterns can be triggered by a single calcium spark in a fire-diffuse-fire model.

    Science.gov (United States)

    Tang, Ai-Hui; Wang, Shi-Qiang

    2009-09-01

    Spiral patterns have been found in various nonequilibrium systems. The Ca(2+)-induced Ca(2+) release system in single cardiac cells is unique for highly discrete reaction elements, each giving rise to a Ca(2+) spark upon excitation. We imaged the spiral Ca(2+) waves in isolated cardiac cells and numerically studied the effect of system excitability on spiral patterns using a two-dimensional fire-diffuse-fire model. We found that under certain conditions, the system was able to display multiple stable patterns of spiral waves, each exhibiting different periods and distinct routines of spiral tips. Transition between these different patterns could be triggered by an internal fluctuation in the form of a single Ca(2+) spark.

  17. Neural signatures of attention: insights from decoding population activity patterns.

    Science.gov (United States)

    Sapountzis, Panagiotis; Gregoriou, Georgia G

    2018-01-01

    Understanding brain function and the computations that individual neurons and neuronal ensembles carry out during cognitive functions is one of the biggest challenges in neuroscientific research. To this end, invasive electrophysiological studies have provided important insights by recording the activity of single neurons in behaving animals. To average out noise, responses are typically averaged across repetitions and across neurons that are usually recorded on different days. However, the brain makes decisions on short time scales based on limited exposure to sensory stimulation by interpreting responses of populations of neurons on a moment to moment basis. Recent studies have employed machine-learning algorithms in attention and other cognitive tasks to decode the information content of distributed activity patterns across neuronal ensembles on a single trial basis. Here, we review results from studies that have used pattern-classification decoding approaches to explore the population representation of cognitive functions. These studies have offered significant insights into population coding mechanisms. Moreover, we discuss how such advances can aid the development of cognitive brain-computer interfaces.

  18. Predicting Neural Activity Patterns Associated with Sentences Using a Neurobiologically Motivated Model of Semantic Representation.

    Science.gov (United States)

    Anderson, Andrew James; Binder, Jeffrey R; Fernandino, Leonardo; Humphries, Colin J; Conant, Lisa L; Aguilar, Mario; Wang, Xixi; Doko, Donias; Raizada, Rajeev D S

    2017-09-01

    We introduce an approach that predicts neural representations of word meanings contained in sentences then superposes these to predict neural representations of new sentences. A neurobiological semantic model based on sensory, motor, social, emotional, and cognitive attributes was used as a foundation to define semantic content. Previous studies have predominantly predicted neural patterns for isolated words, using models that lack neurobiological interpretation. Fourteen participants read 240 sentences describing everyday situations while undergoing fMRI. To connect sentence-level fMRI activation patterns to the word-level semantic model, we devised methods to decompose the fMRI data into individual words. Activation patterns associated with each attribute in the model were then estimated using multiple-regression. This enabled synthesis of activation patterns for trained and new words, which were subsequently averaged to predict new sentences. Region-of-interest analyses revealed that prediction accuracy was highest using voxels in the left temporal and inferior parietal cortex, although a broad range of regions returned statistically significant results, showing that semantic information is widely distributed across the brain. The results show how a neurobiologically motivated semantic model can decompose sentence-level fMRI data into activation features for component words, which can be recombined to predict activation patterns for new sentences. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  19. 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.

  20. Using a multi-state recurrent neural network to optimize loading patterns in BWRs

    International Nuclear Information System (INIS)

    Ortiz, Juan Jose; Requena, Ignacio

    2004-01-01

    A Multi-State Recurrent Neural Network is used to optimize Loading Patterns (LP) in BWRs. We have proposed an energy function that depends on fuel assembly positions and their nuclear cross sections to carry out optimisation. Multi-State Recurrent Neural Networks creates LPs that satisfy the Radial Power Peaking Factor and maximize the effective multiplication factor at the Beginning of the Cycle, and also satisfy the Minimum Critical Power Ratio and Maximum Linear Heat Generation Rate at the End of the Cycle, thereby maximizing the effective multiplication factor. In order to evaluate the LPs, we have used a trained back-propagation neural network to predict the parameter values, instead of using a reactor core simulator, which saved considerable computation time in the search process. We applied this method to find optimal LPs for five cycles of Laguna Verde Nuclear Power Plant (LVNPP) in Mexico

  1. Spider Trait Assembly Patterns and Resilience under Fire-Induced Vegetation Change in South Brazilian Grasslands

    Science.gov (United States)

    Podgaiski, Luciana R.; Joner, Fernando; Lavorel, Sandra; Moretti, Marco; Ibanez, Sebastien; Mendonça, Milton de S.; Pillar, Valério D.

    2013-01-01

    Disturbances induce changes on habitat proprieties that may filter organism's functional traits thereby shaping the structure and interactions of many trophic levels. We tested if communities of predators with foraging traits dependent on habitat structure respond to environmental change through cascades affecting the functional traits of plants. We monitored the response of spider and plant communities to fire in South Brazilian Grasslands using pairs of burned and unburned plots. Spiders were determined to the family level and described in feeding behavioral and morphological traits measured on each individual. Life form and morphological traits were recorded for plant species. One month after fire the abundance of vegetation hunters and the mean size of the chelicera increased due to the presence of suitable feeding sites in the regrowing vegetation, but irregular web builders decreased due to the absence of microhabitats and dense foliage into which they build their webs. Six months after fire rosette-form plants with broader leaves increased, creating a favourable habitat for orb web builders which became more abundant, while graminoids and tall plants were reduced, resulting in a decrease of proper shelters and microclimate in soil surface to ground hunters which became less abundant. Hence, fire triggered changes in vegetation structure that lead both to trait-convergence and trait-divergence assembly patterns of spiders along gradients of plant biomass and functional diversity. Spider individuals occurring in more functionally diverse plant communities were more diverse in their traits probably because increased possibility of resource exploitation, following the habitat heterogeneity hypothesis. Finally, as an indication of resilience, after twelve months spider communities did not differ from those of unburned plots. Our findings show that functional traits provide a mechanistic understanding of the response of communities to environmental change

  2. Theories of Person Perception Predict Patterns of Neural Activity During Mentalizing.

    Science.gov (United States)

    Thornton, Mark A; Mitchell, Jason P

    2017-08-22

    Social life requires making inferences about other people. What information do perceivers spontaneously draw upon to make such inferences? Here, we test 4 major theories of person perception, and 1 synthetic theory that combines their features, to determine whether the dimensions of such theories can serve as bases for describing patterns of neural activity during mentalizing. While undergoing functional magnetic resonance imaging, participants made social judgments about well-known public figures. Patterns of brain activity were then predicted using feature encoding models that represented target people's positions on theoretical dimensions such as warmth and competence. All 5 theories of person perception proved highly accurate at reconstructing activity patterns, indicating that each could describe the informational basis of mentalizing. Cross-validation indicated that the theories robustly generalized across both targets and participants. The synthetic theory consistently attained the best performance-approximately two-thirds of noise ceiling accuracy--indicating that, in combination, the theories considered here can account for much of the neural representation of other people. Moreover, encoding models trained on the present data could reconstruct patterns of activity associated with mental state representations in independent data, suggesting the use of a common neural code to represent others' traits and states. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  3. A deep convolutional neural network to analyze position averaged convergent beam electron diffraction patterns.

    Science.gov (United States)

    Xu, W; LeBeau, J M

    2018-05-01

    We establish a series of deep convolutional neural networks to automatically analyze position averaged convergent beam electron diffraction patterns. The networks first calibrate the zero-order disk size, center position, and rotation without the need for pretreating the data. With the aligned data, additional networks then measure the sample thickness and tilt. The performance of the network is explored as a function of a variety of variables including thickness, tilt, and dose. A methodology to explore the response of the neural network to various pattern features is also presented. Processing patterns at a rate of  ∼ 0.1 s/pattern, the network is shown to be orders of magnitude faster than a brute force method while maintaining accuracy. The approach is thus suitable for automatically processing big, 4D STEM data. We also discuss the generality of the method to other materials/orientations as well as a hybrid approach that combines the features of the neural network with least squares fitting for even more robust analysis. The source code is available at https://github.com/subangstrom/DeepDiffraction. Copyright © 2018 Elsevier B.V. All rights reserved.

  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. Gait pattern recognition in cerebral palsy patients using neural network modelling

    International Nuclear Information System (INIS)

    Muhammad, J.; Gibbs, S.; Abboud, R.; Anand, S.

    2015-01-01

    Interpretation of gait data obtained from modern 3D gait analysis is a challenging and time consuming task. The aim of this study was to create neural network models which can recognise the gait patterns from pre- and post-treatment and the normal ones. Neural network is a method which works on the principle of learning from experience and then uses the obtained knowledge to predict the unknown. Methods: Twenty-eight patients with cerebral palsy were recruited as subjects whose gait was analysed in pre- and post-treatment. A group of twenty-six normal subjects also participated in this study as control group. All subjects gait was analysed using Vicon Nexus to obtain the gait parameters and kinetic and kinematic parameters of hip, knee and ankle joints in three planes of both limbs. The gait data was used as input to create neural network models. A total of approximately 300 trials were split into 70% and 30% to train and test the models, respectively. Different models were built using different parameters. The gait was categorised as three patterns, i.e., normal, pre- and post-treatments. Result: The results showed that the models using all parameters or using the joint angles and moments could predict the gait patterns with approximately 95% accuracy. Some of the models e.g., the models using joint power and moments, had lower rate in recognition of gait patterns with approximately 70-90% successful ratio. Conclusion: Neural network model can be used in clinical practice to recognise the gait pattern for cerebral palsy patients. (author)

  6. 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

  7. Vascular pattern of the dentate gyrus is regulated by neural progenitors.

    Science.gov (United States)

    Pombero, Ana; Garcia-Lopez, Raquel; Estirado, Alicia; Martinez, Salvador

    2018-05-01

    Neurogenesis is a vital process that begins during early embryonic development and continues until adulthood, though in the latter case, it is restricted to the subventricular zone and the subgranular zone of the dentate gyrus (DG). In particular, the DG's neurogenic properties are structurally and functionally unique, which may be related to its singular vascular pattern. Neurogenesis and angiogenesis share molecular signals and act synergistically, supporting the concept of a neurogenic niche as a functional unit between neural precursors cells and their environment, in which the blood vessels play an important role. Whereas it is well known that vascular development controls neural proliferation in the embryonary and in the adult brain, by releasing neurotrophic factors; the potential influence of neural cells on vascular components during angiogenesis is largely unknown. We have demonstrated that the reduction of neural progenitors leads to a significant impairment of vascular development. Since VEGF is a potential regulator in the neurogenesis-angiogenesis crosstalk, we were interested in assessing the possible role of this molecule in the hippocampal neurovascular development. Our results showed that VEGF is the molecule involved in the regulation of vascular development by neural progenitor cells in the DG.

  8. 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.

  9. 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)

  10. Multi-Connection Pattern Analysis: Decoding the representational content of neural communication.

    Science.gov (United States)

    Li, Yuanning; Richardson, Robert Mark; Ghuman, Avniel Singh

    2017-11-15

    The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity patterns of the populations as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population. Successful MCPA-based decoding indicates the involvement of distributed computational processing and provides a framework for probing the representational structure of the interaction. Simulations demonstrate the efficacy of MCPA in realistic circumstances. In addition, we demonstrate that MCPA can be applied to different signal modalities to evaluate a variety of hypothesis associated with information coding in neural communications. We apply MCPA to fMRI and human intracranial electrophysiological data to provide a proof-of-concept of the utility of this method for decoding individual natural images and faces in functional connectivity data. We further use a MCPA-based representational similarity analysis to illustrate how MCPA may be used to test computational models of information transfer among regions of the visual processing stream. Thus, MCPA can be used to assess the information represented in the coupled activity of interacting neural circuits and probe the underlying principles of information transformation between regions. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. 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.

  12. Recognition of neural brain activity patterns correlated with complex motor activity

    Science.gov (United States)

    Kurkin, Semen; Musatov, Vyacheslav Yu.; Runnova, Anastasia E.; Grubov, Vadim V.; Efremova, Tatyana Yu.; Zhuravlev, Maxim O.

    2018-04-01

    In this paper, based on the apparatus of artificial neural networks, a technique for recognizing and classifying patterns corresponding to imaginary movements on electroencephalograms (EEGs) obtained from a group of untrained subjects was developed. The works on the selection of the optimal type, topology, training algorithms and neural network parameters were carried out from the point of view of the most accurate and fast recognition and classification of patterns on multi-channel EEGs associated with the imagination of movements. The influence of the number and choice of the analyzed channels of a multichannel EEG on the quality of recognition of imaginary movements was also studied, and optimal configurations of electrode arrangements were obtained. The effect of pre-processing of EEG signals is analyzed from the point of view of improving the accuracy of recognition of imaginary movements.

  13. Dynamics and spike trains statistics in conductance-based integrate-and-fire neural networks with chemical and electric synapses

    International Nuclear Information System (INIS)

    Cofré, Rodrigo; Cessac, Bruno

    2013-01-01

    We investigate the effect of electric synapses (gap junctions) on collective neuronal dynamics and spike statistics in a conductance-based integrate-and-fire neural network, driven by Brownian noise, where conductances depend upon spike history. We compute explicitly the time evolution operator and show that, given the spike-history of the network and the membrane potentials at a given time, the further dynamical evolution can be written in a closed form. We show that spike train statistics is described by a Gibbs distribution whose potential can be approximated with an explicit formula, when the noise is weak. This potential form encompasses existing models for spike trains statistics analysis such as maximum entropy models or generalized linear models (GLM). We also discuss the different types of correlations: those induced by a shared stimulus and those induced by neurons interactions

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

    Directory of Open Access Journals (Sweden)

    Elisabeth Annie Lambert

    2011-08-01

    Full Text Available 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 fibres. Fourteen subjects (57±2 years, 9 men, 5 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<0.001. This was associated with a decrease in systolic and diastolic blood pressure (-12 ±3 and -5±2 mmHg, P<0.05, and in heart rate (-7±2 bpm, P<0.01 and an improvement in all 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<0.05. Multi-unit MSNA decreased from 68±4 to 59±5 bursts per 100 heartbeats (P<0.05. Single-unit MSNA indicated that the firing rate of individual vasoconstrictor fibres decreased from 59±10 to 32±4 spikes per 100 heart beats (P<0.05. The probability of firing decreased from 34±5 to 23±3 % of heartbeats (P<0.05, and the incidence of multiple firing decreased from 14±4 to 6±1 % of heartbeats (P<0.05. Cardiac and sympathetic baroreflex function were significantly improved (cardiac slope: 6.57±0.69 to 9.57±1.20 msec.mmHg-1; sympathetic slope: -3.86±0.34 to -5.05±0.47 bursts per 100 heartbeats.mmHg-1 P<0.05 for both. Hypocaloric diet decreased sympathetic activity and improved hemodynamic and metabolic parameters. The sympathoinhibition associated with weight loss involves marked changes, not only in the rate but also in the firing pattern of

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

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    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.

  16. In-vivo determination of chewing patterns using FBG and artificial neural networks

    Science.gov (United States)

    Pegorini, Vinicius; Zen Karam, Leandro; Rocha Pitta, Christiano S.; Ribeiro, Richardson; Simioni Assmann, Tangriani; Cardozo da Silva, Jean Carlos; Bertotti, Fábio L.; Kalinowski, Hypolito J.; Cardoso, Rafael

    2015-09-01

    This paper reports the process of pattern classification of the chewing process of ruminants. We propose a simplified signal processing scheme for optical fiber Bragg grating (FBG) sensors based on machine learning techniques. The FBG sensors measure the biomechanical forces during jaw movements and an artificial neural network is responsible for the classification of the associated chewing pattern. In this study, three patterns associated to dietary supplement, hay and ryegrass were considered. Additionally, two other important events for ingestive behavior studies were monitored, rumination and idle period. Experimental results show that the proposed approach for pattern classification has been capable of differentiating the materials involved in the chewing process with a small classification error.

  17. Ex vivo determination of chewing patterns using FBG and artificial neural networks

    Science.gov (United States)

    Karam, L. Z.; Pegorini, V.; Pitta, C. S. R.; Assmann, T. S.; Cardoso, R.; Kalinowski, H. J.; Silva, J. C. C.

    2014-05-01

    This paper reports the experimental procedures performed in a bovine head for the determination of chewing patterns during the mastication process. Mandible movements during the chewing have been simulated either by using two plasticine materials with different textures or without material. Fibre Bragg grating sensors were fixed in the jaw to monitor the biomechanical forces involved in the chewing process. The acquired signals from the sensors fed the input of an artificial neural network aiming at the classification of the measured chewing patterns for each material used in the experiment. The results obtained from the simulation of the chewing process presented different patterns for the different textures of plasticine, resulting on the determination of three chewing patterns with a classification error of 5%.

  18. 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.

  19. 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

  20. 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.

  1. Probing neural cell behaviors through micro-/nano-patterned chitosan substrates

    International Nuclear Information System (INIS)

    Sung, Chun-Yen; Yang, Chung-Yao; Yeh, J Andrew; Chen, Wen-Shiang; Wang, Yang-Kao; Cheng, Chao-Min

    2015-01-01

    In this study, we describe the development of surface-modified chitosan substrates to examine topographically related Neuro-2a cell behaviors. Different functional groups can be modified on chitosan surfaces to probe Neuro-2a cell morphology. To prepare chitosan substrates with micro/nano-scaled features, we demonstrated an easy-to-handle method that combined photolithography, inductively coupled plasma reactive ion etching, Ag nanoparticle-assisted etching, and solution casting. The results show that Neuro-2a cells preferred to adhere to a flat chitosan surface rather than a nanotextured chitosan surface as evidenced by greater immobilization and differentiation, suggesting that surface topography is crucial for neural patterning. In addition, we developed chitosan substrates with different geometric patterns and flat region depth; this allowed us to re-arrange or re-pattern Neuro-2a cell colonies at desired locations. We found that a polarity-induced micropattern provided the most suitable surface pattern for promoting neural network formation on a chitosan substrate. The cellular polarity of single Neuro-2a cell spreading correlated to a diamond-like geometry and neurite outgrowth was induced from the corners toward the grooves of the structures. This study provide greater insight into neurobiology, including neurotransmitter screening, electrophysiological stimulation platforms, and biomedical engineering. (paper)

  2. What Neural Substrates Trigger the Adept Scientific Pattern Discovery by Biologists?

    Science.gov (United States)

    Lee, Jun-Ki; Kwon, Yong-Ju

    2011-04-01

    This study investigated the neural correlates of experts and novices during biological object pattern detection using an fMRI approach in order to reveal the neural correlates of a biologist's superior pattern discovery ability. Sixteen healthy male participants (8 biologists and 8 non-biologists) volunteered for the study. Participants were shown fifteen series of organism pictures and asked to detect patterns amid stimulus pictures. Primary findings showed significant activations in the right middle temporal gyrus and inferior parietal lobule amongst participants in the biologist (expert) group. Interestingly, the left superior temporal gyrus was activated in participants from the non-biologist (novice) group. These results suggested that superior pattern discovery ability could be related to a functional facilitation of the parieto-temporal network, which is particularly driven by the right middle temporal gyrus and inferior parietal lobule in addition to the recruitment of additional brain regions. Furthermore, the functional facilitation of the network might actually pertain to high coherent processing skills and visual working memory capacity. Hence, study results suggested that adept scientific thinking ability can be detected by neuronal substrates, which may be used as criteria for developing and evaluating a brain-based science curriculum and test instrument.

  3. Tree Regeneration Spatial Patterns in Ponderosa Pine Forests Following Stand-Replacing Fire: Influence of Topography and Neighbors

    Directory of Open Access Journals (Sweden)

    Justin P. Ziegler

    2017-10-01

    Full Text Available 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 to examine the spatial pattern of tree locations and heights as well as the influence of tree interactions and topography on tree patterns. In these sparse, early-seral forests, we found that all species were spatially aggregated, partly attributable to the influence of (1 aspect and slope on conifers; (2 topographic position on quaking aspen; and (3 interspecific attraction between ponderosa pine and other species. Specifically, tree interactions were related to finer-scale patterns whereas topographic effects influenced coarse-scale patterns. Spatial structures of heights revealed conspecific size hierarchies with taller trees in denser neighborhoods. Topography and heterospecific tree interactions had nominal effect on tree height spatial structure. Our results demonstrate how stand-replacing fires create heterogeneous forest structures and suggest that scale-dependent, and often facilitatory, rather than competitive, processes act on regenerating trees. These early-seral processes will establish potential pathways of stand development, affecting future forest dynamics and management options.

  4. Firing rate estimation using infinite mixture models and its application to neural decoding.

    Science.gov (United States)

    Shibue, Ryohei; Komaki, Fumiyasu

    2017-11-01

    Neural decoding is a framework for reconstructing external stimuli from spike trains recorded by various neural recordings. Kloosterman et al. proposed a new decoding method using marked point processes (Kloosterman F, Layton SP, Chen Z, Wilson MA. J Neurophysiol 111: 217-227, 2014). 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, the use of kernel density estimation causes problems such as low decoding accuracy and high computational costs. 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 computational speed. We apply the proposed method to simulation and experimental data to verify its performance. NEW & NOTEWORTHY We propose a new neural decoding method using infinite mixture models and nonparametric Bayesian statistics. The proposed method improves decoding performance in terms of accuracy and computation speed. We have successfully applied the proposed method to position decoding from spike trains recorded in a rat hippocampus. Copyright © 2017 the American Physiological Society.

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

    Directory of Open Access Journals (Sweden)

    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.

  6. Decoding Visual Location From Neural Patterns in the Auditory Cortex of the Congenitally Deaf

    Science.gov (United States)

    Almeida, Jorge; He, Dongjun; Chen, Quanjing; Mahon, Bradford Z.; Zhang, Fan; Gonçalves, Óscar F.; Fang, Fang; Bi, Yanchao

    2016-01-01

    Sensory cortices of individuals who are congenitally deprived of a sense can exhibit considerable plasticity and be recruited to process information from the senses that remain intact. Here, we explored whether the auditory cortex of congenitally deaf individuals represents visual field location of a stimulus—a dimension that is represented in early visual areas. We used functional MRI to measure neural activity in auditory and visual cortices of congenitally deaf and hearing humans while they observed stimuli typically used for mapping visual field preferences in visual cortex. We found that the location of a visual stimulus can be successfully decoded from the patterns of neural activity in auditory cortex of congenitally deaf but not hearing individuals. This is particularly true for locations within the horizontal plane and within peripheral vision. These data show that the representations stored within neuroplastically changed auditory cortex can align with dimensions that are typically represented in visual cortex. PMID:26423461

  7. Linking dynamic patterns of neural activity in orbitofrontal cortex with decision making.

    Science.gov (United States)

    Rich, Erin L; Stoll, Frederic M; Rudebeck, Peter H

    2018-04-01

    Humans and animals demonstrate extraordinary flexibility in choice behavior, particularly when deciding based on subjective preferences. We evaluate options on different scales, deliberate, and often change our minds. Little is known about the neural mechanisms that underlie these dynamic aspects of decision-making, although neural activity in orbitofrontal cortex (OFC) likely plays a central role. Recent evidence from studies in macaques shows that attention modulates value responses in OFC, and that ensembles of OFC neurons dynamically signal different options during choices. When contexts change, these ensembles flexibly remap to encode the new task. Determining how these dynamic patterns emerge and relate to choices will inform models of decision-making and OFC function. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Antagonism between the transcription factors NANOG and OTX2 specifies rostral or caudal cell fate during neural patterning transition.

    Science.gov (United States)

    Su, Zhenghui; Zhang, Yanqi; Liao, Baojian; Zhong, Xiaofen; Chen, Xin; Wang, Haitao; Guo, Yiping; Shan, Yongli; Wang, Lihui; Pan, Guangjin

    2018-03-23

    During neurogenesis, neural patterning is a critical step during which neural progenitor cells differentiate into neurons with distinct functions. However, the molecular determinants that regulate neural patterning remain poorly understood. Here we optimized the "dual SMAD inhibition" method to specifically promote differentiation of human pluripotent stem cells (hPSCs) into forebrain and hindbrain neural progenitor cells along the rostral-caudal axis. We report that neural patterning determination occurs at the very early stage in this differentiation. Undifferentiated hPSCs expressed basal levels of the transcription factor orthodenticle homeobox 2 (OTX2) that dominantly drove hPSCs into the "default" rostral fate at the beginning of differentiation. Inhibition of glycogen synthase kinase 3β (GSK3β) through CHIR99021 application sustained transient expression of the transcription factor NANOG at early differentiation stages through Wnt signaling. Wnt signaling and NANOG antagonized OTX2 and, in the later stages of differentiation, switched the default rostral cell fate to the caudal one. Our findings have uncovered a mutual antagonism between NANOG and OTX2 underlying cell fate decisions during neural patterning, critical for the regulation of early neural development in humans. © 2018 by The American Society for Biochemistry and Molecular Biology, Inc.

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

    Science.gov (United States)

    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.

  10. Dynamical Properties of Discrete-Time Background Neural Networks with Uniform Firing Rate

    Directory of Open Access Journals (Sweden)

    Min Wan

    2013-01-01

    Full Text Available The dynamics of a discrete-time background network with uniform firing rate and background input is investigated. The conditions for stability are firstly derived. An invariant set is then obtained so that the nondivergence of the network can be guaranteed. In the invariant set, it is proved that all trajectories of the network starting from any nonnegative value will converge to a fixed point under some conditions. In addition, bifurcation and chaos are discussed. It is shown that the network can engender bifurcation and chaos with the increase of background input. The computations of Lyapunov exponents confirm the chaotic behaviors.

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

    Science.gov (United States)

    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.

  12. Global vegetation-fire pattern under different land use and climate conditions

    Science.gov (United States)

    Thonicke, K.; Poulter, B.; Heyder, U.; Gumpenberger, M.; Cramer, W.

    2008-12-01

    Fire is a process of global significance in the Earth System influencing vegetation dynamics, biogeochemical cycling and biophysical feedbacks. Naturally ignited wildfires have long history in the Earth System. Humans have been using fire to shape the landscape for their purposes for many millenia, sometimes influencing the status of the vegetation remarkably as for example in Mediterranean-type ecosystems. Processes and drivers describing fire danger, ignitions, fire spread and effects are relatively well-known for many fire-prone ecosystems. Modeling these has a long tradition in fire-affected regions to predict fire risk and behavior for fire-fighting purposes. On the other hand, the global vegetation community realized the importance of disturbances to be recognized in their global vegetation models with fire being globally most important and so-far best studied. First attempts to simulate fire globally considered a minimal set of drivers, whereas recent developments attempt to consider each fire process separately. The process-based fire model SPITFIRE (SPread and InTensity of FIRE) simulates these processes embedded in the LPJ DGVM. Uncertainties still arise from missing measurements for some parameters in less-studied fire regimes, or from broad PFT classifications which subsume different fire-ecological adaptations and tolerances. Some earth observation data sets as well as fire emission models help to evaluate seasonality and spatial distribution of simulated fire ignitions, area burnt and fire emissions within SPITFIRE. Deforestation fires are a major source of carbon released to the atmosphere in the tropics; in the Amazon basin it is the second-largest contributor to Brazils GHG emissions. How ongoing deforestation affects fire regimes, forest stability and biogeochemical cycling in the Amazon basin under present climate conditions will be presented. Relative importance of fire vs. climate and land use change is analyzed. Emissions resulting from

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

    Science.gov (United States)

    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...

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

    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...

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

    Directory of Open Access Journals (Sweden)

    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.

  17. Deep convolutional neural networks for annotating gene expression patterns in the mouse brain.

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    Zeng, Tao; Li, Rongjian; Mukkamala, Ravi; Ye, Jieping; Ji, Shuiwang

    2015-05-07

    Profiling gene expression in brain structures at various spatial and temporal scales is essential to understanding how genes regulate the development of brain structures. The Allen Developing Mouse Brain Atlas provides high-resolution 3-D in situ hybridization (ISH) gene expression patterns in multiple developing stages of the mouse brain. Currently, the ISH images are annotated with anatomical terms manually. In this paper, we propose a computational approach to annotate gene expression pattern images in the mouse brain at various structural levels over the course of development. We applied deep convolutional neural network that was trained on a large set of natural images to extract features from the ISH images of developing mouse brain. As a baseline representation, we applied invariant image feature descriptors to capture local statistics from ISH images and used the bag-of-words approach to build image-level representations. Both types of features from multiple ISH image sections of the entire brain were then combined to build 3-D, brain-wide gene expression representations. We employed regularized learning methods for discriminating gene expression patterns in different brain structures. Results show that our approach of using convolutional model as feature extractors achieved superior performance in annotating gene expression patterns at multiple levels of brain structures throughout four developing ages. Overall, we achieved average AUC of 0.894 ± 0.014, as compared with 0.820 ± 0.046 yielded by the bag-of-words approach. Deep convolutional neural network model trained on natural image sets and applied to gene expression pattern annotation tasks yielded superior performance, demonstrating its transfer learning property is applicable to such biological image sets.

  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. 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.

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

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    Zirra, Alexandra; Wiethoff, Sarah; Patani, Rickie

    2016-01-01

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  2. Improving Pattern Recognition and Neural Network Algorithms with Applications to Solar Panel Energy Optimization

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    Zamora Ramos, Ernesto

    Artificial Intelligence is a big part of automation and with today's technological advances, artificial intelligence has taken great strides towards positioning itself as the technology of the future to control, enhance and perfect automation. Computer vision includes pattern recognition and classification and machine learning. Computer vision is at the core of decision making and it is a vast and fruitful branch of artificial intelligence. In this work, we expose novel algorithms and techniques built upon existing technologies to improve pattern recognition and neural network training, initially motivated by a multidisciplinary effort to build a robot that helps maintain and optimize solar panel energy production. Our contributions detail an improved non-linear pre-processing technique to enhance poorly illuminated images based on modifications to the standard histogram equalization for an image. While the original motivation was to improve nocturnal navigation, the results have applications in surveillance, search and rescue, medical imaging enhancing, and many others. We created a vision system for precise camera distance positioning motivated to correctly locate the robot for capture of solar panel images for classification. The classification algorithm marks solar panels as clean or dirty for later processing. Our algorithm extends past image classification and, based on historical and experimental data, it identifies the optimal moment in which to perform maintenance on marked solar panels as to minimize the energy and profit loss. In order to improve upon the classification algorithm, we delved into feedforward neural networks because of their recent advancements, proven universal approximation and classification capabilities, and excellent recognition rates. We explore state-of-the-art neural network training techniques offering pointers and insights, culminating on the implementation of a complete library with support for modern deep learning architectures

  3. Trends in adverse weather patterns and large wildland fires in Aragón (NE Spain from 1978 to 2010

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    A. Cardil

    2013-05-01

    Full Text Available This work analyzes the effects of high temperature days on large wildland fires during 1978–2010 in Aragón (NE Spain. A high temperature day was established when air temperature was higher than 20 °C at 850 hPa. Temperature at 850 hPa was chosen because it properly characterizes the low troposphere state, and some of the problems that affect surface reanalysis do not occur. High temperature days were analyzed from April to October in the study period, and the number of these extreme days increased significantly. This temporal trend implied more frequent adverse weather conditions in later years that could facilitate extreme fire behavior. The effects of those high temperatures days in large wildland fire patterns have been increasingly important in the last years of the series.

  4. Biogeochemistry and plant physiological traits interact to reinforce patterns of post-fire dominance in boreal forests

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    Shenoy, A.; Kielland, K.; Johnstone, J. F.

    2011-12-01

    Increases in the frequency, extent, and severity of fire in the North American boreal region are projected to continue under a warming climate and are likely to be associated with changes in future vegetation composition. In interior Alaska, fire severity is linked to the relative dominance of deciduous versus coniferous canopy species. Severely burned areas have high levels of deciduous recruitment and subsequent stand dominance, while lightly burned areas exhibit black spruce self-replacement. To elucidate potential mechanisms by which differential fire severity results in differential post-fire vegetation development, we examined changes in soil nitrogen (N) supply (NO3- and NH4+) and in situ 15N uptake by young aspen (Populus tremuloides) and black spruce (Picea mariana) trees growing in lightly and severely burned areas. We hypothesized that (a) soil nitrate supply would be higher in severely burned sites and (b) since conifers have been shown to have a reduced physiological capacity for NO3- uptake, aspen would display greater rates of NO3- uptake than spruce in severely burned sites. Our results suggested that the composition and magnitude of inorganic N supply 14 years after the fire was nearly identical in high-severity and low-severity sites, and nitrate represented nearly 50% of the supply. However, both aspen and spruce took up substantially more NH4+-N than NO3- -N regardless of fire severity. Surprisingly, spruce exhibited only a moderately lower rate of NO3- uptake (μg N/g root-1h-1) than aspen. At the stand level, aspen took up nearly an order-of-magnitude more N per hectare in severely burned sites compared to lightly burned sites, while spruce exhibited the opposite pattern of N uptake with respect to fire severity. Whereas ammonium appeared to be preferred by both species, nitrate represented a larger component of N uptake (based on the NO3-:NH4+ uptake ratio) in aspen (0.7) than in spruce (0.4). We suggest that these species

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

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    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.

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

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    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.

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

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    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.

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

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    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.

  9. Emergence of synchronization and regularity in firing patterns in time-varying neural hypernetworks

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    Rakshit, Sarbendu; Bera, Bidesh K.; Ghosh, Dibakar; Sinha, Sudeshna

    2018-05-01

    We study synchronization of dynamical systems coupled in time-varying network architectures, composed of two or more network topologies, corresponding to different interaction schemes. As a representative example of this class of time-varying hypernetworks, we consider coupled Hindmarsh-Rose neurons, involving two distinct types of networks, mimicking interactions that occur through the electrical gap junctions and the chemical synapses. Specifically, we consider the connections corresponding to the electrical gap junctions to form a small-world network, while the chemical synaptic interactions form a unidirectional random network. Further, all the connections in the hypernetwork are allowed to change in time, modeling a more realistic neurobiological scenario. We model this time variation by rewiring the links stochastically with a characteristic rewiring frequency f . We find that the coupling strength necessary to achieve complete neuronal synchrony is lower when the links are switched rapidly. Further, the average time required to reach the synchronized state decreases as synaptic coupling strength and/or rewiring frequency increases. To quantify the local stability of complete synchronous state we use the Master Stability Function approach, and for global stability we employ the concept of basin stability. The analytically derived necessary condition for synchrony is in excellent agreement with numerical results. Further we investigate the resilience of the synchronous states with respect to increasing network size, and we find that synchrony can be maintained up to larger network sizes by increasing either synaptic strength or rewiring frequency. Last, we find that time-varying links not only promote complete synchronization, but also have the capacity to change the local dynamics of each single neuron. Specifically, in a window of rewiring frequency and synaptic coupling strength, we observe that the spiking behavior becomes more regular.

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

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    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.

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

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    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.

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

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    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...

  13. Temporal patterns of fire sequences observed in Canton of Ticino (southern Switzerland

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    L. Telesca

    2010-04-01

    Full Text Available Temporal dynamical analysis in fire sequences recorded from 1969 to 2008 in Canton Ticino (Switzerland was carried out by using the Allan Factor statistics. The obtained results show the presence of daily periodicities, superimposed to two time-scaling regimes. The daily cycle vanishes for sequences of higher altitude fires, for which a single scaling behaviour is observed.

  14. The BDNF Val66Met Polymorphism Influences Reading Ability and Patterns of Neural Activation in Children.

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    Kaja K Jasińska

    Full Text Available Understanding how genes impact the brain's functional activation for learning and cognition during development remains limited. We asked whether a common genetic variant in the BDNF gene (the Val66Met polymorphism modulates neural activation in the young brain during a critical period for the emergence and maturation of the neural circuitry for reading. In animal models, the bdnf variation has been shown to be associated with the structure and function of the developing brain and in humans it has been associated with multiple aspects of cognition, particularly memory, which are relevant for the development of skilled reading. Yet, little is known about the impact of the Val66Met polymorphism on functional brain activation in development, either in animal models or in humans. Here, we examined whether the BDNF Val66Met polymorphism (dbSNP rs6265 is associated with children's (age 6-10 neural activation patterns during a reading task (n = 81 using functional magnetic resonance imaging (fMRI, genotyping, and standardized behavioral assessments of cognitive and reading development. Children homozygous for the Val allele at the SNP rs6265 of the BDNF gene outperformed Met allele carriers on reading comprehension and phonological memory, tasks that have a strong memory component. Consistent with these behavioral findings, Met allele carriers showed greater activation in reading-related brain regions including the fusiform gyrus, the left inferior frontal gyrus and left superior temporal gyrus as well as greater activation in the hippocampus during a word and pseudoword reading task. Increased engagement of memory and spoken language regions for Met allele carriers relative to Val/Val homozygotes during reading suggests that Met carriers have to exert greater effort required to retrieve phonological codes.

  15. The BDNF Val66Met Polymorphism Influences Reading Ability and Patterns of Neural Activation in Children.

    Science.gov (United States)

    Jasińska, Kaja K; Molfese, Peter J; Kornilov, Sergey A; Mencl, W Einar; Frost, Stephen J; Lee, Maria; Pugh, Kenneth R; Grigorenko, Elena L; Landi, Nicole

    2016-01-01

    Understanding how genes impact the brain's functional activation for learning and cognition during development remains limited. We asked whether a common genetic variant in the BDNF gene (the Val66Met polymorphism) modulates neural activation in the young brain during a critical period for the emergence and maturation of the neural circuitry for reading. In animal models, the bdnf variation has been shown to be associated with the structure and function of the developing brain and in humans it has been associated with multiple aspects of cognition, particularly memory, which are relevant for the development of skilled reading. Yet, little is known about the impact of the Val66Met polymorphism on functional brain activation in development, either in animal models or in humans. Here, we examined whether the BDNF Val66Met polymorphism (dbSNP rs6265) is associated with children's (age 6-10) neural activation patterns during a reading task (n = 81) using functional magnetic resonance imaging (fMRI), genotyping, and standardized behavioral assessments of cognitive and reading development. Children homozygous for the Val allele at the SNP rs6265 of the BDNF gene outperformed Met allele carriers on reading comprehension and phonological memory, tasks that have a strong memory component. Consistent with these behavioral findings, Met allele carriers showed greater activation in reading-related brain regions including the fusiform gyrus, the left inferior frontal gyrus and left superior temporal gyrus as well as greater activation in the hippocampus during a word and pseudoword reading task. Increased engagement of memory and spoken language regions for Met allele carriers relative to Val/Val homozygotes during reading suggests that Met carriers have to exert greater effort required to retrieve phonological codes.

  16. 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

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

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    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

  18. BDNFval66met affects neural activation pattern during fear conditioning and 24 h delayed fear recall.

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    Lonsdorf, Tina B; Golkar, Armita; Lindström, Kara M; Haaker, Jan; Öhman, Arne; Schalling, Martin; Ingvar, Martin

    2015-05-01

    Brain-derived neurotrophic factor (BDNF), the most abundant neutrophin in the mammalian central nervous system, is critically involved in synaptic plasticity. In both rodents and humans, BDNF has been implicated in hippocampus- and amygdala-dependent learning and memory and has more recently been linked to fear extinction processes. Fifty-nine healthy participants, genotyped for the functional BDNFval66met polymorphism, underwent a fear conditioning and 24h-delayed extinction protocol while skin conductance and blood oxygenation level dependent (BOLD) responses (functional magnetic resonance imaging) were acquired. We present the first report of neural activation pattern during fear acquisition 'and' extinction for the BDNFval66met polymorphism using a differential conditioned stimulus (CS)+ > CS- comparison. During conditioning, we observed heightened allele dose-dependent responses in the amygdala and reduced responses in the subgenual anterior cingulate cortex in BDNFval66met met-carriers. During early extinction, 24h later, we again observed heightened responses in several regions ascribed to the fear network in met-carriers as opposed to val-carriers (insula, amygdala, hippocampus), which likely reflects fear memory recall. No differences were observed during late extinction, which likely reflects learned extinction. Our data thus support previous associations of the BDNFval66met polymorphism with neural activation in the fear and extinction network, but speak against a specific association with fear extinction processes. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  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. Fluid pipeline system leak detection based on neural network and pattern recognition

    International Nuclear Information System (INIS)

    Tang Xiujia

    1998-01-01

    The mechanism of the stress wave propagation along the pipeline system of NPP, caused by turbulent ejection from pipeline leakage, is researched. A series of characteristic index are described in time domain or frequency domain, and compress numerical algorithm is developed for original data compression. A back propagation neural networks (BPNN) with the input matrix composed by stress wave characteristics in time domain or frequency domain is first proposed to classify various situations of the pipeline, in order to detect the leakage in the fluid flow pipelines. The capability of the new method had been demonstrated by experiments and finally used to design a handy instrument for the pipeline leakage detection. Usually a pipeline system has many inner branches and often in adjusting dynamic condition, it is difficult for traditional pipeline diagnosis facilities to identify the difference between inner pipeline operation and pipeline fault. The author first proposed pipeline wave propagation identification by pattern recognition to diagnose pipeline leak. A series of pattern primitives such as peaks, valleys, horizon lines, capstan peaks, dominant relations, slave relations, etc., are used to extract features of the negative pressure wave form. The context-free grammar of symbolic representation of the negative wave form is used, and a negative wave form parsing system with application to structural pattern recognition based on the representation is first proposed to detect and localize leaks of the fluid pipelines

  1. Genetic algorithms and artificial neural networks for loading pattern optimisation of advanced gas-cooled reactors

    Energy Technology Data Exchange (ETDEWEB)

    Ziver, A.K. E-mail: a.k.ziver@imperial.ac.uk; Pain, C.C; Carter, J.N.; Oliveira, C.R.E. de; Goddard, A.J.H.; Overton, R.S

    2004-03-01

    A non-generational genetic algorithm (GA) has been developed for fuel management optimisation of Advanced Gas-Cooled Reactors, which are operated by British Energy and produce around 20% of the UK's electricity requirements. An evolutionary search is coded using the genetic operators; namely selection by tournament, two-point crossover, mutation and random assessment of population for multi-cycle loading pattern (LP) optimisation. A detailed description of the chromosomes in the genetic algorithm coded is presented. Artificial Neural Networks (ANNs) have been constructed and trained to accelerate the GA-based search during the optimisation process. The whole package, called GAOPT, is linked to the reactor analysis code PANTHER, which performs fresh fuel loading, burn-up and power shaping calculations for each reactor cycle by imposing station-specific safety and operational constraints. GAOPT has been verified by performing a number of tests, which are applied to the Hinkley Point B and Hartlepool reactors. The test results giving loading pattern (LP) scenarios obtained from single and multi-cycle optimisation calculations applied to realistic reactor states of the Hartlepool and Hinkley Point B reactors are discussed. The results have shown that the GA/ANN algorithms developed can help the fuel engineer to optimise loading patterns in an efficient and more profitable way than currently available for multi-cycle refuelling of AGRs. Research leading to parallel GAs applied to LP optimisation are outlined, which can be adapted to present day LWR fuel management problems.

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

    International Nuclear Information System (INIS)

    Reynoso V, M.R.; Vega C, J.J.

    2003-01-01

    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)

  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. 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)

  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. Methods for discriminating gas-liquid two phase flow patterns based on gray neural networks and SVM

    International Nuclear Information System (INIS)

    Li Jingjing; Zhou Tao; Duan Jun; Zhang Lei

    2013-01-01

    Background: The flow patterns of two phase flow will directly influence the heat transfer and mass transfer of the flow. Purpose: By wavelet analysis of the pressure drop experimental data, the wavelet coefficients of different frequency can be obtained. Methods: Get the wavelet energy and then train them in the model of BP neural network to distinguish the flow patterns. Introduced the implant gray neural networks model and use it for the two phase flow for the first time. At the same time, set up the method of training the pressure data and wavelet energy data in the support vector machine. Results: Through treatment of the gray layer, the result of the neural network is more accuracy. It can obviously reduce the effect of data marginalization. The accuracy of the pressure drop Lib-SVM method is 95.2%. Conclusions: The results show that these three methods can make a distinction among the different flow patterns and the Lib-SVM method gets the best result, then the gray neural networks, and at last the BP neural networks. (authors)

  7. Firing properties of identified interneuron populations in the mammalian hindlimb central pattern generator

    DEFF Research Database (Denmark)

    Butt, S. J B; Harris-Warrick, Ronald M.; Kiehn, Ole

    2002-01-01

    a heterogenous population with neurons that fired in all phases of the locomotor cycle and exhibited varying degrees of rhythmicity, from strongly rhythmic to nonrhythmic. Among the rhythmic, putative CPG dCINs were populations that fired inphase with the ipsilateral or with the contralateral L2 locomotorlike......, with little direct contribution from the intrinsic pacemaker hyperpolarization-activated inward current. For both ipsilaterally and contralaterally firing dCINs the dominant synaptic drive was in-phase with the ipsilateral L2 motor activity. This study provides the first characterization of putative CPG...

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

    Science.gov (United States)

    Sihvonen, Aleksi J; Ripollés, Pablo; Rodríguez-Fornells, Antoni; Soinila, Seppo; Särkämö, Teppo

    2017-01-01

    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 the lesions

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

    Science.gov (United States)

    Sihvonen, Aleksi J.; Ripollés, Pablo; Rodríguez-Fornells, Antoni; Soinila, Seppo; Särkämö, Teppo

    2017-01-01

    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 the lesions

  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. Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments

    International Nuclear Information System (INIS)

    Tahat Amani; Marti Jordi; Khwaldeh Ali; Tahat Kaher

    2014-01-01

    In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories: transfer ‘occurred’ and transfer ‘not occurred’. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation (multilayer perceptron algorithms) could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties (e.g., temperature, density) show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies. (condensed matter: structural, mechanical, and thermal properties)

  12. Local community detection as pattern restoration by attractor dynamics of recurrent neural networks.

    Science.gov (United States)

    Okamoto, Hiroshi

    2016-08-01

    Densely connected parts in networks are referred to as "communities". Community structure is a hallmark of a variety of real-world networks. Individual communities in networks form functional modules of complex systems described by networks. Therefore, finding communities in networks is essential to approaching and understanding complex systems described by networks. In fact, network science has made a great deal of effort to develop effective and efficient methods for detecting communities in networks. Here we put forward a type of community detection, which has been little examined so far but will be practically useful. Suppose that we are given a set of source nodes that includes some (but not all) of "true" members of a particular community; suppose also that the set includes some nodes that are not the members of this community (i.e., "false" members of the community). We propose to detect the community from this "imperfect" and "inaccurate" set of source nodes using attractor dynamics of recurrent neural networks. Community detection by the proposed method can be viewed as restoration of the original pattern from a deteriorated pattern, which is analogous to cue-triggered recall of short-term memory in the brain. We demonstrate the effectiveness of the proposed method using synthetic networks and real social networks for which correct communities are known. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  13. 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

  14. Schema benefit vs. proactive interference: Contradicting behavioral outcomes and coexisting neural patterns.

    Science.gov (United States)

    Oren, Noga; Shapira-Lichter, Irit; Lerner, Yulia; Tarrasch, Ricardo; Hendler, Talma; Giladi, Nir; Ash, Elissa L

    2017-09-01

    Prior knowledge can either assist or hinder the ability to learn new information. These contradicting behavioral outcomes, referred to as schema benefit and proactive interference respectively, have been studied separately. Here we examined whether the known neural correlates of each process coexist, and how they are influenced by attentional loading and aging. To this end we used an fMRI task that affected both processes simultaneously by presenting pairs of related short movies in succession. The first movie of each pair provided context for the second movie, which could evoke schema benefit and/or proactive interference. Inclusion of an easy or hard secondary task performed during encoding of the movies, as well as testing both younger (22-35y) and older (65-79y) adults, allowed examining the effect of attentional load and older age on the neural patterns associated with context. Analyses focused on three predefined regions and examined how their inter-subject correlation (inter-SC) and functional connectivity (FC) with the hippocampi changed between the first and second movie. The results in the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) matched and expanded previous findings: higher inter-SC and lower FC were observed during the second compared to the first movie; yet the differentiation between the first and second movies in these regions was attenuated under high attentional load, pointing to dependency on attentional resources. Instead, at high load there was a significant context effect in the FC of the left ventrolateral prefrontal cortex (vlPFC), and greater FC in the second movie was related to greater proactive interference. Further, older adults showed context effect in the PCC and vlPFC. Intriguingly, older adults with inter-SC mPFC patterns similar to younger adults exhibited schema benefit in our task, while those with inter-SC PCC patterns similar to younger adults showed proactive interference in an independent task. The

  15. Firing patterns and synchronization in nonsynaptic epileptiform activity: the effect of gap junctions modulated by potassium accumulation

    International Nuclear Information System (INIS)

    Santos, D O C; Dickman, R; Rodrigues, A M; De Almeida, A C G

    2009-01-01

    Several lines of evidence point to the modification of firing patterns and of synchronization due to gap junctions (GJs) as having a role in the establishment of epileptiform activity (EA). However, previous studies consider GJs as ohmic resistors, ignoring the effects of intense variations in ionic concentration known to occur during seizures. In addition to GJs, extracellular potassium is regarded as a further important factor involved in seizure initiation and sustainment. To analyze how these two mechanisms act together to shape firing and synchronization, we use a detailed computational model for in vitro high-K + and low-Ca 2+ nonsynaptic EA. The model permits us to explore the modulation of electrotonic interactions under ionic concentration changes caused by electrodiffusion in the extracellular space, altered by tortuosity. In addition, we investigate the special case of null GJ current. Increased electrotonic interaction alters bursts and action potential frequencies, favoring synchronization. The particularities of pattern changes depend on the tortuosity and array size. Extracellular potassium accumulation alone modifies firing and synchronization when the GJ coupling is null

  16. 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

  17. 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 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...... 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...

  18. Drug-like and non drug-like pattern classification based on simple topology descriptor using hybrid neural network.

    Science.gov (United States)

    Wan-Mamat, Wan Mohd Fahmi; Isa, Nor Ashidi Mat; Wahab, Habibah A; Wan-Mamat, Wan Mohd Fairuz

    2009-01-01

    An intelligent prediction system has been developed to discriminate drug-like and non drug-like molecules pattern. The system is constructed by using the application of advanced version of standard multilayer perceptron (MLP) neural network called Hybrid Multilayer Perceptron (HMLP) neural network and trained using Modified Recursive Prediction Error (MRPE) training algorithm. In this work, a well understood and easy excess Rule of Five + Veber filter properties are selected as the topological descriptor. The main idea behind the selection of this simple descriptor is to assure that the system could be used widely, beneficial and more advantageous regardless at all user level within a drug discovery organization.

  19. Topographic Patterns of Mortality and Succession in the Alpine Treeline Ecotone Suggest Hydrologic Controls on Post-Fire Tree Establishment

    Science.gov (United States)

    McCaffrey, D. R.; Hopkinson, C.

    2017-12-01

    Alpine Treeline Ecotone (ATE), the transition zone between closed canopy forest and alpine tundra, is a prominent vegetation pattern in mountain regions. At continental scales, the elevation of ATE is negatively correlated with latitude and is generally explained by thermal limitations. However, at landscape scales, precipitation and moisture regimes can suppress ATE elevation below thermal limits, causing variability and patterning in ATE position. Recent studies have investigated the relative effects of hydroclimatic variables on ATE position at multiple scales, but less attention has been given to interactions between hydroclimatic variables and disturbance agents, such as fire. Observing change in the ATE at sufficient spatial resolution and temporal extent to identify correlations between topographic variables and disturbance agents has proved challenging. Recent advances in monoplotting have enabled the extraction of canopy cover information from oblique photography, at a resolution of 20 m. Using airborne lidar and repeat photography from the Mountain Legacy Project, we observed canopy cover change in West Castle Watershed (Alberta, Canada; 103 km2; 49.3° N, 114.4° W) over a 92-year period (i.e. 1914-2006). Two wildfires, occurring 1934 and 1936, affected 63% of the watershed area, providing an opportunity to contrast topographic patterns of mortality and succession in the ATE, while factoring by exposure to fire. Slope aspect was a strong predictor of mortality and succession: the frequency of mortality was four times higher in fire-exposed areas, with 72% of all mortality occurring on south- and east-facing slope aspects; the frequency of succession was balanced between fire-exposed and unexposed areas, with 66% of all succession occurred on north- and east-facing slope aspects. Given previous experiments have demonstrated that moisture limitation inhibits tree establishment, suppressing elevation of ATE below thermal growth boundaries, we hypothesize

  20. Fuel treatments and landform modify landscape patterns of burn severity in an extreme fire event

    Science.gov (United States)

    Susan J. Prichard; Maureen C. Kennedy

    2014-01-01

    Under a rapidly warming climate, a critical management issue in semiarid forests of western North America is how to increase forest resilience to wildfire. We evaluated relationships between fuel reduction treatments and burn severity in the 2006 Tripod Complex fires, which burned over 70 000 ha of mixed-conifer forests in the North Cascades range of Washington State...

  1. 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...

  2. 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...

  3. Efficient spiking neural network model of pattern motion selectivity in visual cortex.

    Science.gov (United States)

    Beyeler, Michael; Richert, Micah; Dutt, Nikil D; Krichmar, Jeffrey L

    2014-07-01

    Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction-selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation outperforms a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40 × 40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available.

  4. Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

    Science.gov (United States)

    Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

    2018-04-01

    Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

  5. Underlying neural alpha frequency patterns associated with intra-hemispheric inhibition during an interhemispheric transfer task.

    Science.gov (United States)

    Simon-Dack, Stephanie L; Kraus, Brian; Walter, Zachary; Smith, Shelby; Cadle, Chelsea

    2018-05-18

    Interhemispheric transfer measured via differences in right- or left-handed motoric responses to lateralized visual stimuli, known as the crossed-uncrossed difference (CUD), is one way of identifying patterns of processing that are vital for understanding the transfer of neural signals. Examination of interhemispheric transfer by means of the CUD is not entirely explained by simple measures of response time. Multiple processes contribute to wide variability observed in CUD reaction times. Prior research has suggested that intra-hemispheric inhibitory processes may be involved in regulation of speed of transfer. Our study examined electroencephalography recordings and time-locked alpha frequency activity while 18 participants responded to lateralized targets during performance of the Poffenberger Paradigm. Our results suggest that there are alpha frequency differences at fronto-central lateral electrodes based on target, hand-of-response, and receiving hemisphere. These findings suggest that early motoric inhibitory mechanisms may help explain the wide range of variability typically seen with the CUD. Copyright © 2018 Elsevier B.V. All rights reserved.

  6. 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.

  7. Neural activation patterns of successful episodic encoding: Reorganization during childhood, maintenance in old age

    Directory of Open Access Journals (Sweden)

    Yee Lee Shing

    2016-08-01

    Full Text Available The two-component framework of episodic memory (EM development posits that the contributions of medial temporal lobe (MTL and prefrontal cortex (PFC to successful encoding differ across the lifespan. To test the framework’s hypotheses, we compared subsequent memory effects (SME of 10–12 year-old children, younger adults, and older adults using functional magnetic resonance imaging (fMRI. Memory was probed by cued recall, and SME were defined as regional activation differences during encoding between subsequently correctly recalled versus omitted items. In MTL areas, children’s SME did not differ in magnitude from those of younger and older adults. In contrast, children’s SME in PFC were weaker than the corresponding SME in younger and older adults, in line with the hypothesis that PFC contributes less to successful encoding in childhood. Differences in SME between younger and older adults were negligible. The present results suggest that, among individuals with high memory functioning, the neural circuitry contributing to successful episodic encoding is reorganized from middle childhood to adulthood. Successful episodic encoding in later adulthood, however, is characterized by the ability to maintain the activation patterns that emerged in young adulthood.

  8. 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.

  9. Memory and pattern storage in neural networks with activity dependent synapses

    Science.gov (United States)

    Mejias, J. F.; Torres, J. J.

    2009-01-01

    We present recently obtained results on the influence of the interplay between several activity dependent synaptic mechanisms, such as short-term depression and facilitation, on the maximum memory storage capacity in an attractor neural network [1]. In contrast with the case of synaptic depression, which drastically reduces the capacity of the network to store and retrieve activity patterns [2], synaptic facilitation is able to enhance the memory capacity in different situations. In particular, we find that a convenient balance between depression and facilitation can enhance the memory capacity, reaching maximal values similar to those obtained with static synapses, that is, without activity-dependent processes. We also argue, employing simple arguments, that this level of balance is compatible with experimental data recorded from some cortical areas, where depression and facilitation may play an important role for both memory-oriented tasks and information processing. We conclude that depressing synapses with a certain level of facilitation allow to recover the good retrieval properties of networks with static synapses while maintaining the nonlinear properties of dynamic synapses, convenient for information processing and coding.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. Adaptive Forming of the Beam Pattern of Microstrip Antenna with the Use of an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Janusz Dudczyk

    2012-01-01

    Full Text Available Microstrip antenna has been recently one of the most innovative fields of antenna techniques. The main advantage of such an antenna is the simplicity of its production, little weight, a narrow profile, and easiness of integration of the radiating elements with the net of generators power systems. As a result of using arrays consisting of microstrip antennas; it is possible to decrease the size and weight and also to reduce the costs of components production as well as whole application systems. This paper presents possibilities of using artificial neural networks (ANNs in the process of forming a beam from radiating complex microstrip antenna. Algorithms which base on artificial neural networks use high parallelism of actions which results in considerable acceleration of the process of forming the antenna pattern. The appropriate selection of learning constants makes it possible to get theoretically a solution which will be close to the real time. This paper presents the training neural network algorithm with the selection of optimal network structure. The analysis above was made in case of following the emission source, setting to zero the pattern of direction of expecting interference, and following emission source compared with two constant interferences. Computer simulation was made in MATLAB environment on the basis of Flex Tool, a programme which creates artificial neural networks.

  16. 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.

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

    OpenAIRE

    Edia E.O.; Gevrey M.; Ouattara A.; Brosse S.; Gourène G.; Lek S.

    2010-01-01

    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 metho...

  18. 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

  19. Methodology for automatic process of the fired ceramic tile's internal defect using IR images and artificial neural network

    OpenAIRE

    Andrade, Roberto Márcio de; Eduardo, Alexandre Carlos

    2011-01-01

    In the ceramic industry, rarely testing systems were employed to on-line detect the presence of defects in ceramic tiles. This paper is concerned with the problem of automatic inspection of ceramic tiles using Infrared Images and Artificial Neural Network (ANN). The performance of the technique has been evaluated theoretically and experimentally from laboratory and on line tile samples. It has been performed system for IR image processing and, utilizing an Artificial Neural Network (ANN), det...

  20. Neural networks engaged in tactile object manipulation: patterns of expression among healthy individuals

    Directory of Open Access Journals (Sweden)

    Seitz Rüdiger J

    2010-11-01

    Full Text Available Abstract Background Somatosensory object discrimination has been shown to involve widespread cortical and subcortical structures in both cerebral hemispheres. In this study we aimed to identify the networks involved in tactile object manipulation by principal component analysis (PCA of individual subjects. We expected to find more than one network. Methods Seven healthy right-handed male volunteers (aged 22 to 44 yrs manipulated with their right hand aluminium spheres during 5 s with a repetition frequency of 0.5-0.7 Hz. The correlation coefficients between the principal component temporal expression coefficients and the hemodynamic response modelled by SPM (ecc determined the task-related components. To establish reproducibility within subjects and similarity of functional connectivity patterns among subjects, regional correlation coefficients (rcc were computed between task-related component image volumes. By hierarchically categorizing, selecting and averaging the task-related component image volumes across subjects according to the rccs, mean component images (MCIs were derived describing neural networks associated with tactile object manipulation. Results Two independent mean component images emerged. Each included the primary sensorimotor cortex contralateral to the manipulating hand. The region extended to the premotor cortex in MCI 1, whereas it was restricted to the hand area of the primary sensorimotor cortex in MCI 2. MCI 1 showed bilateral involvement of the paralimbic anterior cingulate cortex (ACC, whereas MCI 2 implicated the midline thalamic nuclei and two areas of the rostral dorsal pons. Conclusions Two distinct networks participate in tactile object manipulation as revealed by the intra- and interindividual comparison of individual scans. Both were employed by most subjects, suggesting that both are involved in normal somatosensory object discrimination.

  1. 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.

  2. Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition.

    Science.gov (United States)

    Kasabov, Nikola; Dhoble, Kshitij; Nuntalid, Nuttapod; Indiveri, Giacomo

    2013-05-01

    On-line learning and recognition of spatio- and spectro-temporal data (SSTD) is a very challenging task and an important one for the future development of autonomous machine learning systems with broad applications. Models based on spiking neural networks (SNN) have already proved their potential in capturing spatial and temporal data. One class of them, the evolving SNN (eSNN), uses a one-pass rank-order learning mechanism and a strategy to evolve a new spiking neuron and new connections to learn new patterns from incoming data. So far these networks have been mainly used for fast image and speech frame-based recognition. Alternative spike-time learning methods, such as Spike-Timing Dependent Plasticity (STDP) and its variant Spike Driven Synaptic Plasticity (SDSP), can also be used to learn spatio-temporal representations, but they usually require many iterations in an unsupervised or semi-supervised mode of learning. This paper introduces a new class of eSNN, dynamic eSNN, that utilise both rank-order learning and dynamic synapses to learn SSTD in a fast, on-line mode. The paper also introduces a new model called deSNN, that utilises rank-order learning and SDSP spike-time learning in unsupervised, supervised, or semi-supervised modes. The SDSP learning is used to evolve dynamically the network changing connection weights that capture spatio-temporal spike data clusters both during training and during recall. The new deSNN model is first illustrated on simple examples and then applied on two case study applications: (1) moving object recognition using address-event representation (AER) with data collected using a silicon retina device; (2) EEG SSTD recognition for brain-computer interfaces. The deSNN models resulted in a superior performance in terms of accuracy and speed when compared with other SNN models that use either rank-order or STDP learning. The reason is that the deSNN makes use of both the information contained in the order of the first input spikes

  3. 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)

  4. 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.

  5. On the relative role of fire and rainfall in determining vegetation patterns in tropical savannas: a simulation study

    Science.gov (United States)

    Spessa, Allan; Fisher, Rosie

    2010-05-01

    LPJ-GUESS vegetation model. Recently, SPIFTIRE has been coupled to the Ecosystem Demography (ED) model, which simulates global vegetation dynamics as part of the new land surface scheme JULES (Joint UK Environment Simulator) within the QUEST Earth System Model (http://www.quest-esm.ac.uk/). This study forms part of on-going work to further improve and test the ability of JULES to accurately simulate the terrestrial carbon cycle and land-atmosphere exchanges under different climates. Using the JULES (ED-SPITFIRE) model driven by observed climate (1901-2002), and focusing on large-scale rainfall gradients in the tropical savannas of west Africa, the Northern Territory (Australia) and central-southern Brazil, this study assesses: i) simulated versus observed vegetation dynamics and distributions, and ii) the relative importance of fire versus rainfall in determining vegetation patterns. A sensitivity analysis approach was used.

  6. The use of global image characteristics for neural network pattern recognitions

    Science.gov (United States)

    Kulyas, Maksim O.; Kulyas, Oleg L.; Loshkarev, Aleksey S.

    2017-04-01

    The recognition system is observed, where the information is transferred by images of symbols generated by a television camera. For descriptors of objects the coefficients of two-dimensional Fourier transformation generated in a special way. For solution of the task of classification the one-layer neural network trained on reference images is used. Fast learning of a neural network with a single neuron calculation of coefficients is applied.

  7. 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.

  8. Analysis of In-Flight Collision Process During V-Type Firing Pattern in Surface Blasting Using Simple Physics

    Science.gov (United States)

    Chouhan, Lalit Singh; Raina, Avtar K.

    2015-10-01

    Blasting is a unit operation in Mine-Mill Fragmentation System (MMFS) and plays a vital role in mining cost. One of the goals of MMFS is to achieve optimum fragment size at minimal cost. Blast fragmentation optimization is known to result in better explosive energy utilization. Fragmentation depends on the rock, explosive and blast design variables. If burden, spacing and type of explosive used in a mine are kept constant, the firing sequence of blast-holes plays a vital role in rock fragmentation. To obtain smaller fragmentation size, mining professionals and relevant publications recommend V- or extended V-pattern of firing sequence. In doing so, it is assumed that the in-flight air collision breaks larger rock fragments into smaller ones, thus aiding further fragmentation. There is very little support to the phenomenon of breakage during in-flight collision of fragments during blasting in published literature. In order to assess the breakage of in-flight fragments due to collision, a mathematical simulation was carried over using basic principles of physics. The calculations revealed that the collision breakage is dependent on velocity of fragments, mass of fragments, the strength of the rock and the area of fragments over which collision takes place. For higher strength rocks, the in-flight collision breakage is very difficult to achieve. This leads to the conclusion that the concept demands an in-depth investigation and validation.

  9. Study on pattern recognition of Raman spectrum based on fuzzy neural network

    Science.gov (United States)

    Zheng, Xiangxiang; Lv, Xiaoyi; Mo, Jiaqing

    2017-10-01

    Hydatid disease is a serious parasitic disease in many regions worldwide, especially in Xinjiang, China. Raman spectrum of the serum of patients with echinococcosis was selected as the research object in this paper. The Raman spectrum of blood samples from healthy people and patients with echinococcosis are measured, of which the spectrum characteristics are analyzed. The fuzzy neural network not only has the ability of fuzzy logic to deal with uncertain information, but also has the ability to store knowledge of neural network, so it is combined with the Raman spectrum on the disease diagnosis problem based on Raman spectrum. Firstly, principal component analysis (PCA) is used to extract the principal components of the Raman spectrum, reducing the network input and accelerating the prediction speed and accuracy of Network based on remaining the original data. Then, the information of the extracted principal component is used as the input of the neural network, the hidden layer of the network is the generation of rules and the inference process, and the output layer of the network is fuzzy classification output. Finally, a part of samples are randomly selected for the use of training network, then the trained network is used for predicting the rest of the samples, and the predicted results are compared with general BP neural network to illustrate the feasibility and advantages of fuzzy neural network. Success in this endeavor would be helpful for the research work of spectroscopic diagnosis of disease and it can be applied in practice in many other spectral analysis technique fields.

  10. Techniques for extracting single-trial activity patterns from large-scale neural recordings

    Science.gov (United States)

    Churchland, Mark M; Yu, Byron M; Sahani, Maneesh; Shenoy, Krishna V

    2008-01-01

    Summary Large, chronically-implanted arrays of microelectrodes are an increasingly common tool for recording from primate cortex, and can provide extracellular recordings from many (order of 100) neurons. While the desire for cortically-based motor prostheses has helped drive their development, such arrays also offer great potential to advance basic neuroscience research. Here we discuss the utility of array recording for the study of neural dynamics. Neural activity often has dynamics beyond that driven directly by the stimulus. While governed by those dynamics, neural responses may nevertheless unfold differently for nominally identical trials, rendering many traditional analysis methods ineffective. We review recent studies – some employing simultaneous recording, some not – indicating that such variability is indeed present both during movement generation, and during the preceding premotor computations. In such cases, large-scale simultaneous recordings have the potential to provide an unprecedented view of neural dynamics at the level of single trials. However, this enterprise will depend not only on techniques for simultaneous recording, but also on the use and further development of analysis techniques that can appropriately reduce the dimensionality of the data, and allow visualization of single-trial neural behavior. PMID:18093826

  11. 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.

  12. Neural stem cell-encoded temporal patterning delineates an early window of malignant susceptibility in Drosophila.

    Science.gov (United States)

    Narbonne-Reveau, Karine; Lanet, Elodie; Dillard, Caroline; Foppolo, Sophie; Chen, Ching-Huan; Parrinello, Hugues; Rialle, Stéphanie; Sokol, Nicholas S; Maurange, Cédric

    2016-06-14

    Pediatric neural tumors are often initiated during early development and can undergo very rapid transformation. However, the molecular basis of this early malignant susceptibility remains unknown. During Drosophila development, neural stem cells (NSCs) divide asymmetrically and generate intermediate progenitors that rapidly differentiate in neurons. Upon gene inactivation, these progeny can dedifferentiate and generate malignant tumors. Here, we find that intermediate progenitors are prone to malignancy only when born during an early window of development while expressing the transcription factor Chinmo, and the mRNA-binding proteins Imp/IGF2BP and Lin-28. These genes compose an oncogenic module that is coopted upon dedifferentiation of early-born intermediate progenitors to drive unlimited tumor growth. In late larvae, temporal transcription factor progression in NSCs silences the module, thereby limiting mitotic potential and terminating the window of malignant susceptibility. Thus, this study identifies the gene regulatory network that confers malignant potential to neural tumors with early developmental origins.

  13. 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.

  14. Stability Switches, Hopf Bifurcations, and Spatio-temporal Patterns in a Delayed Neural Model with Bidirectional Coupling

    Science.gov (United States)

    Song, Yongli; Zhang, Tonghua; Tadé, Moses O.

    2009-12-01

    The dynamical behavior of a delayed neural network with bi-directional coupling is investigated by taking the delay as the bifurcating parameter. Some parameter regions are given for conditional/absolute stability and Hopf bifurcations by using the theory of functional differential equations. As the propagation time delay in the coupling varies, stability switches for the trivial solution are found. Conditions ensuring the stability and direction of the Hopf bifurcation are determined by applying the normal form theory and the center manifold theorem. We also discuss the spatio-temporal patterns of bifurcating periodic oscillations by using the symmetric bifurcation theory of delay differential equations combined with representation theory of Lie groups. In particular, we obtain that the spatio-temporal patterns of bifurcating periodic oscillations will alternate according to the change of the propagation time delay in the coupling, i.e., different ranges of delays correspond to different patterns of neural activities. Numerical simulations are given to illustrate the obtained results and show the existence of bursts in some interval of the time for large enough delay.

  15. 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.

  16. Toward a multipoint optical fibre sensor system for use in process water systems based on artificial neural network pattern recognition

    International Nuclear Information System (INIS)

    King, D; Lyons, W B; Flanagan, C; Lewis, E

    2005-01-01

    An optical fibre sensor capable of detecting various concentrations of ethanol in water supplies is reported. The sensor is based on a U-bend sensor configuration and is incorporated into a 170-metre length of silica cladding silica core optical fibre. The sensor is interrogated using Optical Time Domain Reflectometry (OTDR) and it is proposed to apply artificial neural network (ANN) pattern recognition techniques to the resulting OTDR signals to accurately classify the sensor test conditions. It is also proposed that additional U-bend configuration sensors will be added to the fibre measurement length, in order to implement a multipoint optical fibre sensor system

  17. 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…

  18. Firing patterns of spontaneously active motor units in spinal cord-injured subjects

    NARCIS (Netherlands)

    Zijdewind, Inge; Thomas, Christine K.

    Involuntary motor unit activity at low rates is common in hand muscles paralysed by spinal cord injury. Our aim was to describe these patterns of motor unit behaviour in relation to motoneurone and motor unit properties. Intramuscular electromyographic activity (EMG), surface EMG and force were

  19. Firing pattern of fasciculations in ALS: evidence for axonal and neuronal origin.

    NARCIS (Netherlands)

    Kleine, B.U.; Stegeman, D.F.; Schelhaas, H.J.; Zwarts, M.J.

    2008-01-01

    BACKGROUND: In amyotrophic lateral sclerosis (ALS), the origin of fasciculations is disputed. We hypothesized that the discharge pattern of fasciculation potentials (FPs) would be different for FPs arising in the motor axon or in the spinal motor neuron. METHOD: FPs were recorded by high-density

  20. Using Neural Pattern Classifiers to Quantify the Modularity of Conflict–Control Mechanisms in the Human Brain

    Science.gov (United States)

    Jiang, Jiefeng; Egner, Tobias

    2014-01-01

    Resolving conflicting sensory and motor representations is a core function of cognitive control, but it remains uncertain to what degree control over different sources of conflict is implemented by shared (domain general) or distinct (domain specific) neural resources. Behavioral data suggest conflict–control to be domain specific, but results from neuroimaging studies have been ambivalent. Here, we employed multivoxel pattern analyses that can decode a brain region's informational content, allowing us to distinguish incidental activation overlap from actual shared information processing. We trained independent sets of “searchlight” classifiers on functional magnetic resonance imaging data to decode control processes associated with stimulus-conflict (Stroop task) and ideomotor-conflict (Simon task). Quantifying the proportion of domain-specific searchlights (capable of decoding only one type of conflict) and domain-general searchlights (capable of decoding both conflict types) in each subject, we found both domain-specific and domain-general searchlights, though the former were more common. When mapping anatomical loci of these searchlights across subjects, neural substrates of stimulus- and ideomotor-specific conflict–control were found to be anatomically consistent across subjects, whereas the substrates of domain-general conflict–control were not. Overall, these findings suggest a hybrid neural architecture of conflict–control that entails both modular (domain specific) and global (domain general) components. PMID:23402762

  1. Using neural pattern classifiers to quantify the modularity of conflict-control mechanisms in the human brain.

    Science.gov (United States)

    Jiang, Jiefeng; Egner, Tobias

    2014-07-01

    Resolving conflicting sensory and motor representations is a core function of cognitive control, but it remains uncertain to what degree control over different sources of conflict is implemented by shared (domain general) or distinct (domain specific) neural resources. Behavioral data suggest conflict-control to be domain specific, but results from neuroimaging studies have been ambivalent. Here, we employed multivoxel pattern analyses that can decode a brain region's informational content, allowing us to distinguish incidental activation overlap from actual shared information processing. We trained independent sets of "searchlight" classifiers on functional magnetic resonance imaging data to decode control processes associated with stimulus-conflict (Stroop task) and ideomotor-conflict (Simon task). Quantifying the proportion of domain-specific searchlights (capable of decoding only one type of conflict) and domain-general searchlights (capable of decoding both conflict types) in each subject, we found both domain-specific and domain-general searchlights, though the former were more common. When mapping anatomical loci of these searchlights across subjects, neural substrates of stimulus- and ideomotor-specific conflict-control were found to be anatomically consistent across subjects, whereas the substrates of domain-general conflict-control were not. Overall, these findings suggest a hybrid neural architecture of conflict-control that entails both modular (domain specific) and global (domain general) components. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  2. Species-abundance distribution patterns of soil fungi: contribution to the ecological understanding of their response to experimental fire in Mediterranean maquis (southern Italy).

    Science.gov (United States)

    Persiani, Anna Maria; Maggi, Oriana

    2013-01-01

    Experimental fires, of both low and high intensity, were lit during summer 2000 and the following 2 y in the Castel Volturno Nature Reserve, southern Italy. Soil samples were collected Jul 2000-Jul 2002 to analyze the soil fungal community dynamics. Species abundance distribution patterns (geometric, logarithmic, log normal, broken-stick) were compared. We plotted datasets with information both on species richness and abundance for total, xerotolerant and heat-stimulated soil microfungi. The xerotolerant fungi conformed to a broken-stick model for both the low- and high intensity fires at 7 and 84 d after the fire; their distribution subsequently followed logarithmic models in the 2 y following the fire. The distribution of the heat-stimulated fungi changed from broken-stick to logarithmic models and eventually to a log-normal model during the post-fire recovery. Xerotolerant and, to a far greater extent, heat-stimulated soil fungi acquire an important functional role following soil water stress and/or fire disturbance; these disturbances let them occupy unsaturated habitats and become increasingly abundant over time.

  3. Lead intoxication induces noradrenaline depletion, motor nonmotor disabilities, and changes in the firing pattern of subthalamic nucleus neurons.

    Science.gov (United States)

    Sabbar, M; Delaville, C; De Deurwaerdère, P; Benazzouz, A; Lakhdar-Ghazal, N

    2012-05-17

    Lead intoxication has been suggested as a high risk factor for the development of Parkinson disease. However, its impact on motor and nonmotor functions and the mechanism by which it can be involved in the disease are still unclear. In the present study, we studied the effects of lead intoxication on the following: (1) locomotor activity using an open field actimeter and motor coordination using the rotarod test, (2) anxiety behavior using the elevated plus maze, (3) "depression-like" behavior using sucrose preference test, and (4) subthalamic nucleus (STN) neuronal activity using extracellular single unit recordings. Male Sprague-Dawley rats were treated once a day with lead acetate or sodium acetate (20 mg/kg/d i.p.) during 3 weeks. The tissue content of monoamines was used to determine alteration of these systems at the end of experiments. Results show that lead significantly reduced exploratory activity, locomotor activity and the time spent on the rotarod bar. Furthermore, lead induced anxiety but not "depressive-like" behavior. The electrophysiological results show that lead altered the discharge pattern of STN neurons with an increase in the number of bursting and irregular cells without affecting the firing rate. Moreover, lead intoxication resulted in a decrease of tissue noradrenaline content without any change in the levels of dopamine and serotonin. Together, these results show for the first time that lead intoxication resulted in motor and nonmotor behavioral changes paralleled by noradrenaline depletion and changes in the firing activity of STN neurons, providing evidence consistent with the induction of atypical parkinsonian-like deficits. Copyright © 2012 IBRO. Published by Elsevier Ltd. All rights reserved.

  4. 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.

  5. The characteristic patterns of neuronal avalanches in mice under anesthesia and at rest: An investigation using constrained artificial neural networks

    Science.gov (United States)

    Knöpfel, Thomas; Leech, Robert

    2018-01-01

    Local perturbations within complex dynamical systems can trigger cascade-like events that spread across significant portions of the system. Cascades of this type have been observed across a broad range of scales in the brain. Studies of these cascades, known as neuronal avalanches, usually report the statistics of large numbers of avalanches, without probing the characteristic patterns produced by the avalanches themselves. This is partly due to limitations in the extent or spatiotemporal resolution of commonly used neuroimaging techniques. In this study, we overcome these limitations by using optical voltage (genetically encoded voltage indicators) imaging. This allows us to record cortical activity in vivo across an entire cortical hemisphere, at both high spatial (~30um) and temporal (~20ms) resolution in mice that are either in an anesthetized or awake state. We then use artificial neural networks to identify the characteristic patterns created by neuronal avalanches in our data. The avalanches in the anesthetized cortex are most accurately classified by an artificial neural network architecture that simultaneously connects spatial and temporal information. This is in contrast with the awake cortex, in which avalanches are most accurately classified by an architecture that treats spatial and temporal information separately, due to the increased levels of spatiotemporal complexity. This is in keeping with reports of higher levels of spatiotemporal complexity in the awake brain coinciding with features of a dynamical system operating close to criticality. PMID:29795654

  6. Exemplar-based optical neural net classifier for color pattern recognition

    Science.gov (United States)

    Yu, Francis T. S.; Uang, Chii-Maw; Yang, Xiangyang

    1992-10-01

    We present a color exemplar-based neural network that can be used as an optimum image classifier or an associative memory. Color decomposition and composition technique is used for constructing the polychromatic interconnection weight matrix (IWM). The Hamming net algorithm is modified to relax the dynamic range requirement of the spatial light modulator and to reduce the number of iteration cycles in the winner-take-all layer. Computer simulation results demonstrated the feasibility of this approach

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

    Science.gov (United States)

    Lee, Tae-Ho; Miernicki, Michelle E; Telzer, Eva H

    2017-08-01

    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 M age =42.8years; adolescent M age =14.9years; 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. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  8. 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.

  9. 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

    2013-01-01

    Introduction 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). Methods 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. Results 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

  10. 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.

  11. 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.

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

    Directory of Open Access Journals (Sweden)

    Pedram Gharani

    2017-12-01

    Full Text Available 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.

  13. 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

    Feature ordering is a significant data preprocessing method in Incremental Attribute Learning (IAL), a novel machine learning approach which gradually trains features according to a given order. Previous research has shown that, similar to feature selection, feature ordering is also important based...... 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...

  14. Application of neural network and pattern recognition software to the automated analysis of continuous nuclear monitoring of on-load reactors

    Energy Technology Data Exchange (ETDEWEB)

    Howell, J.A.; Eccleston, G.W.; Halbig, J.K.; Klosterbuer, S.F. [Los Alamos National Lab., NM (United States); Larson, T.W. [California Polytechnic State Univ., San Luis Obispo, CA (US)

    1993-08-01

    Automated analysis using pattern recognition and neural network software can help interpret data, call attention to potential anomalies, and improve safeguards effectiveness. Automated software analysis, based on pattern recognition and neural networks, was applied to data collected from a radiation core discharge monitor system located adjacent to an on-load reactor core. Unattended radiation sensors continuously collect data to monitor on-line refueling operations in the reactor. The huge volume of data collected from a number of radiation channels makes it difficult for a safeguards inspector to review it all, check for consistency among the measurement channels, and find anomalies. Pattern recognition and neural network software can analyze large volumes of data from continuous, unattended measurements, thereby improving and automating the detection of anomalies. The authors developed a prototype pattern recognition program that determines the reactor power level and identifies the times when fuel bundles are pushed through the core during on-line refueling. Neural network models were also developed to predict fuel bundle burnup to calculate the region on the on-load reactor face from which fuel bundles were discharged based on the radiation signals. In the preliminary data set, which was limited and consisted of four distinct burnup regions, the neural network model correctly predicted the burnup region with an accuracy of 92%.

  15. 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.

  16. 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.

  17. Neural network pattern recognition of lingual-palatal pressure for automated detection of swallow.

    Science.gov (United States)

    Hadley, Aaron J; Krival, Kate R; Ridgel, Angela L; Hahn, Elizabeth C; Tyler, Dustin J

    2015-04-01

    We describe a novel device and method for real-time measurement of lingual-palatal pressure and automatic identification of the oral transfer phase of deglutition. Clinical measurement of the oral transport phase of swallowing is a complicated process requiring either placement of obstructive sensors or sitting within a fluoroscope or articulograph for recording. Existing detection algorithms distinguish oral events with EMG, sound, and pressure signals from the head and neck, but are imprecise and frequently result in false detection. We placed seven pressure sensors on a molded mouthpiece fitting over the upper teeth and hard palate and recorded pressure during a variety of swallow and non-swallow activities. Pressure measures and swallow times from 12 healthy and 7 Parkinson's subjects provided training data for a time-delay artificial neural network to categorize the recordings as swallow or non-swallow events. User-specific neural networks properly categorized 96 % of swallow and non-swallow events, while a generalized population-trained network was able to properly categorize 93 % of swallow and non-swallow events across all recordings. Lingual-palatal pressure signals are sufficient to selectively and specifically recognize the initiation of swallowing in healthy and dysphagic patients.

  18. Temporal and Spatial Patterns of Neural Activity Associated with Information Selection in Open-ended Creativity.

    Science.gov (United States)

    Zhou, Siyuan; Chen, Shi; Wang, Shuang; Zhao, Qingbai; Zhou, Zhijin; Lu, Chunming

    2018-02-10

    Novel information selection is a crucial process in creativity and was found to be associated with frontal-temporal functional connectivity in the right brain in closed-ended creativity. Since it has distinct cognitive processing from closed-ended creativity, the information selection in open-ended creativity might be underlain by different neural activity. To address this issue, a creative generation task of Chinese two-part allegorical sayings was adopted, and the trials were classified into novel and normal solutions according to participants' self-ratings. The results showed that (1) novel solutions induced a higher lower alpha power in the temporal area, which might be associated with the automatic, unconscious mental process of retrieving extensive semantic information, and (2) upper alpha power in both frontal and temporal areas and frontal-temporal alpha coherence were higher in novel solutions than in normal solutions, which might reflect the selective inhibition of semantic information. Furthermore, lower alpha power in the temporal area showed a reduction with time, while the frontal-temporal and temporal-temporal coherence in the upper alpha band appeared to increase from the early to the middle phase. These dynamic changes in neural activity might reflect the transformation from divergent thinking to convergent thinking in the creative progress. The advantage of the right brain in frontal-temporal connectivity was not found in the present work, which might result from the diversity of solutions in open-ended creativity. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

  19. The behavioural patterns and neural correlates of concrete and abstract verb processing in aphasia: A novel verb semantic battery

    Directory of Open Access Journals (Sweden)

    Reem S.W. Alyahya

    2018-01-01

    Full Text Available Typically, processing is more accurate and efficient for concrete than abstract concepts in both healthy adults and individuals with aphasia. While, concreteness effects have been thoroughly documented with respect to noun processing, other words classes have received little attention despite tending to be less concrete than nouns. The aim of the current study was to explore concrete-abstract differences in verbs and identify their neural correlates in post-stroke aphasia. Given the dearth of comprehension tests for verbs, a battery of neuropsychological tests was developed in this study to assess the comprehension of concrete and abstract verbs. Specifically, a sensitive verb synonym judgment test was generated that varied both the items' imageability and frequency, and a picture-to-word matching test with numerous concrete verbs. Normative data were then collected and the tests were administered to a cohort of 48 individuals with chronic post-stroke aphasia to explore the behavioural patterns and neural correlates of verb processing. The results revealed significantly better comprehension of concrete than abstract verbs, aligning with the existing aphasiological literature on noun processing. In addition, the patients performed better during verb comprehension than verb production. Lesion-symptom correlational analyses revealed common areas that support processing of concrete and abstract verbs, including the left anterior temporal lobe, posterior supramarginal gyrus and superior lateral occipital cortex. A direct contrast between them revealed additional regions with graded differences. Specifically, the left frontal regions were associated with processing abstract verbs; whereas, the left posterior temporal and occipital regions were associated with processing concrete verbs. Moreover, overlapping and distinct neural correlates were identified in association with the comprehension and production of concrete verbs. These patient findings

  20. The behavioural patterns and neural correlates of concrete and abstract verb processing in aphasia: A novel verb semantic battery.

    Science.gov (United States)

    Alyahya, Reem S W; Halai, Ajay D; Conroy, Paul; Lambon Ralph, Matthew A

    2018-01-01

    Typically, processing is more accurate and efficient for concrete than abstract concepts in both healthy adults and individuals with aphasia. While, concreteness effects have been thoroughly documented with respect to noun processing, other words classes have received little attention despite tending to be less concrete than nouns. The aim of the current study was to explore concrete-abstract differences in verbs and identify their neural correlates in post-stroke aphasia. Given the dearth of comprehension tests for verbs, a battery of neuropsychological tests was developed in this study to assess the comprehension of concrete and abstract verbs. Specifically, a sensitive verb synonym judgment test was generated that varied both the items' imageability and frequency, and a picture-to-word matching test with numerous concrete verbs. Normative data were then collected and the tests were administered to a cohort of 48 individuals with chronic post-stroke aphasia to explore the behavioural patterns and neural correlates of verb processing. The results revealed significantly better comprehension of concrete than abstract verbs, aligning with the existing aphasiological literature on noun processing. In addition, the patients performed better during verb comprehension than verb production. Lesion-symptom correlational analyses revealed common areas that support processing of concrete and abstract verbs, including the left anterior temporal lobe, posterior supramarginal gyrus and superior lateral occipital cortex. A direct contrast between them revealed additional regions with graded differences. Specifically, the left frontal regions were associated with processing abstract verbs; whereas, the left posterior temporal and occipital regions were associated with processing concrete verbs. Moreover, overlapping and distinct neural correlates were identified in association with the comprehension and production of concrete verbs. These patient findings align with data from

  1. Probabilistic Neural Networks for Chemical Sensor Array Pattern Recognition: Comparison Studies, Improvements and Automated Outlier Rejection

    National Research Council Canada - National Science Library

    Shaffer, Ronald E

    1998-01-01

    For application to chemical sensor arrays, the ideal pattern recognition is accurate, fast, simple to train, robust to outliers, has low memory requirements, and has the ability to produce a measure...

  2. Stability switches, oscillatory multistability, and spatio-temporal patterns of nonlinear oscillations in recurrently delay coupled neural networks.

    Science.gov (United States)

    Song, Yongli; Makarov, Valeri A; Velarde, Manuel G

    2009-08-01

    A model of time-delay recurrently coupled spatially segregated neural assemblies is here proposed. We show that it operates like some of the hierarchical architectures of the brain. Each assembly is a neural network with no delay in the local couplings between the units. The delay appears in the long range feedforward and feedback inter-assemblies communications. Bifurcation analysis of a simple four-units system in the autonomous case shows the richness of the dynamical behaviors in a biophysically plausible parameter region. We find oscillatory multistability, hysteresis, and stability switches of the rest state provoked by the time delay. Then we investigate the spatio-temporal patterns of bifurcating periodic solutions by using the symmetric local Hopf bifurcation theory of delay differential equations and derive the equation describing the flow on the center manifold that enables us determining the direction of Hopf bifurcations and stability of the bifurcating periodic orbits. We also discuss computational properties of the system due to the delay when an external drive of the network mimicks external sensory input.

  3. Training spiking neural networks to associate spatio-temporal input-output spike patterns

    OpenAIRE

    Mohemmed, A; Schliebs, S; Matsuda, S; Kasabov, N

    2013-01-01

    In a previous work (Mohemmed et al., Method for training a spiking neuron to associate input–output spike trains) [1] we have proposed a supervised learning algorithm based on temporal coding to train a spiking neuron to associate input spatiotemporal spike patterns to desired output spike patterns. The algorithm is based on the conversion of spike trains into analogue signals and the application of the Widrow–Hoff learning rule. In this paper we present a mathematical formulation of the prop...

  4. Neuronal patterning of the tubular collar cord is highly conserved among enteropneusts but dissimilar to the chordate neural tube.

    Science.gov (United States)

    Kaul-Strehlow, Sabrina; Urata, Makoto; Praher, Daniela; Wanninger, Andreas

    2017-08-01

    A tubular nervous system is present in the deuterostome groups Chordata (cephalochordates, tunicates, vertebrates) and in the non-chordate Enteropneusta. However, the worm-shaped enteropneusts possess a less complex nervous system featuring only a short hollow neural tube, whereby homology to its chordate counterpart remains elusive. Since the majority of data on enteropneusts stem from the harrimaniid Saccoglossus kowalevskii, putative interspecific variations remain undetected resulting in an unreliable ground pattern that impedes homology assessments. In order to complement the missing data from another enteropneust family, we investigated expression of key neuronal patterning genes in the ptychoderid Balanoglossus misakiensis. The collar cord of B. misakiensis shows anterior Six3/6 and posterior Otx + Engrailed expression, in a region corresponding to the chordate brain. Neuronal Nk2.1/Nk2.2 expression is absent. Interestingly, we found median Dlx and lateral Pax6 expression domains, i.e., a condition that is reversed compared to chordates. Comparative analyses reveal that adult nervous system patterning is highly conserved among the enteropneust families Harrimaniidae, Spengelidae and Ptychoderidae. BmiDlx and BmiPax6 have no corresponding expression domains in the chordate brain, which may be indicative of independent acquisition of a tubular nervous system in Enteropneusta and Chordata.

  5. Bifurcation analysis and spatio-temporal patterns of nonlinear oscillations in a delayed neural network with unidirectional coupling

    International Nuclear Information System (INIS)

    Song Yongli; Tadé, Moses O; Zhang Tonghua

    2009-01-01

    In this paper, a delayed neural network with unidirectional coupling is considered which consists of two two-dimensional nonlinear differential equation systems with exponential decay where one system receives a delayed input from the other system. Some parameter regions are given for conditional/absolute stability and Hopf bifurcations by using the theory of functional differential equations. Conditions ensuring the stability and direction of the Hopf bifurcation are determined by applying the normal form theory and the centre manifold theorem. We also investigate the spatio-temporal patterns of bifurcating periodic oscillations by using the symmetric bifurcation theory of delay-differential equations combined with representation theory of Lie groups. Then the global continuation of phase-locked periodic solutions is investigated. Numerical simulations are given to illustrate the results obtained

  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. Neural activity patterns in response to interspecific and intraspecific variation in mating calls in the túngara frog.

    Directory of Open Access Journals (Sweden)

    Mukta Chakraborty

    2010-09-01

    Full Text Available During mate choice, individuals must classify potential mates according to species identity and relative attractiveness. In many species, females do so by evaluating variation in the signals produced by males. Male túngara frogs (Physalaemus pustulosus can produce single note calls (whines and multi-note calls (whine-chucks. While the whine alone is sufficient for species recognition, females greatly prefer the whine-chuck when given a choice.To better understand how the brain responds to variation in male mating signals, we mapped neural activity patterns evoked by interspecific and intraspecific variation in mating calls in túngara frogs by measuring expression of egr-1. We predicted that egr-1 responses to conspecific calls would identify brain regions that are potentially important for species recognition and that at least some of those brain regions would vary in their egr-1 responses to mating calls that vary in attractiveness. We measured egr-1 in the auditory brainstem and its forebrain targets and found that conspecific whine-chucks elicited greater egr-1 expression than heterospecific whines in all but three regions. We found no evidence that preferred whine-chuck calls elicited greater egr-1 expression than conspecific whines in any of eleven brain regions examined, in contrast to predictions that mating preferences in túngara frogs emerge from greater responses in the auditory system.Although selectivity for species-specific signals is apparent throughout the túngara frog brain, further studies are necessary to elucidate how neural activity patterns vary with the attractiveness of conspecific mating calls.

  8. Impact and Recovery Pattern of a Spring Fire on a Pacific Coast Marsh - Observations and Implications for Endangered Species

    Science.gov (United States)

    Brown, L. N.; Willis, K. S.; Ambrose, R. F.; MacDonald, G. M.

    2015-12-01

    The flammability of California coastal marsh vegetation is highest in winter and spring when dominant high marsh plants such as Sarcocornia pacifica are dormant. With climate change the number of cool-season fires are increasing in the state, and marsh systems are becoming more vulnerable to fire disturbance. Very little information exists in peer-reviewed or grey literature on the presence of fire in Pacific Coast tidal marshes. In 1993, the Green Meadows fire in Ventura County, California burned a small portion of tidally influenced Sarcocornia­-dominated marsh at Point Mugu. After the May 2013 Springs Fire burned a similar portion of the salt marsh vegetation, we conducted a two-year vegetation recovery survey using transects of surface vegetation plots and MODIS derived NDVI remote sensing monitoring. Recovery during the first year was limited. Sixteen months into the recovery period, percent plant coverage reached an average of approximately 60% for all plots in the burned area, as opposed to an average of 100% in control plots, and remained at that level for the duration of the study. NDVI did not approach near pre-fire conditions until 19 months after the fire. While recovery may have been influenced by California's current extreme drought conditions, the recurrence of fire and rate of recovery raise many important questions as to the role of fire in Pacific coast tidal marshes. For example, the lack of Salicornia cover over more than an entire breeding season would be detrimental to protected species such as Rallus obsoletus. Fire adds new vulnerabilities on critical tidal marsh habitat already taxed by the threat of sea-level rise, coastal squeeze and invasive species.

  9. 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...

  10. Cognitive methodology for forecasting oil and gas industry using pattern-based neural information technologies

    Science.gov (United States)

    Gafurov, O.; Gafurov, D.; Syryamkin, V.

    2018-05-01

    The paper analyses a field of computer science formed at the intersection of such areas of natural science as artificial intelligence, mathematical statistics, and database theory, which is referred to as "Data Mining" (discovery of knowledge in data). The theory of neural networks is applied along with classical methods of mathematical analysis and numerical simulation. The paper describes the technique protected by the patent of the Russian Federation for the invention “A Method for Determining Location of Production Wells during the Development of Hydrocarbon Fields” [1–3] and implemented using the geoinformation system NeuroInformGeo. There are no analogues in domestic and international practice. The paper gives an example of comparing the forecast of the oil reservoir quality made by the geophysicist interpreter using standard methods and the forecast of the oil reservoir quality made using this technology. The technical result achieved shows the increase of efficiency, effectiveness, and ecological compatibility of development of mineral deposits and discovery of a new oil deposit.

  11. Frequency decoding of periodically timed action potentials through distinct activity patterns in a random neural network

    International Nuclear Information System (INIS)

    Reichenbach, Tobias; Hudspeth, A J

    2012-01-01

    Frequency discrimination is a fundamental task of the auditory system. The mammalian inner ear, or cochlea, provides a place code in which different frequencies are detected at different spatial locations. However, a temporal code based on spike timing is also available: action potentials evoked in an auditory-nerve fiber by a low-frequency tone occur at a preferred phase of the stimulus—they exhibit phase locking—and thus provide temporal information about the tone's frequency. Humans employ this temporal information for discrimination of low frequencies. How might such temporal information be read out in the brain? Here we employ statistical and numerical methods to demonstrate that recurrent random neural networks in which connections between neurons introduce characteristic time delays, and in which neurons require temporally coinciding inputs for spike initiation, can perform sharp frequency discrimination when stimulated with phase-locked inputs. Although the frequency resolution achieved by such networks is limited by the noise in phase locking, the resolution for realistic values reaches the tiny frequency difference of 0.2% that has been measured in humans. (paper)

  12. 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, B J

    2014-06-01

    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 present 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 did 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 toward 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 suggest possible explanations for how peers may motivate adolescent behavior.

  13. Adhesion modification of neural stem cells induced by nanoscale ripple patterns

    International Nuclear Information System (INIS)

    Pedraz, P; Casado, S; Rodriguez, V; Ayuso-Sacido, A; Gnecco, E; Giordano, M C; Mongeot, F Buatier de

    2016-01-01

    We have studied the influence of anisotropic nanopatterns (ripples) on the adhesion and morphology of mouse neural stem cells (C17.2) on glass substrates using cell viability assay, optical microscopy and atomic force microscopy. The ripples were produced by defocused ion beam sputtering with inert Ar ions, which physically remove atoms from the surface at the energy of 800 eV. The ripple periodicity (∼200 nm) is comparable to the thickness of the cytoplasmatic microspikes (filopodia) which link the stem cells to the substrate. All methods show that the cell adhesion is significantly lowered compared to the same type of cells on flat glass surfaces. Furthermore, the AFM analysis reveals that the filopodia tend to be trapped parallel or perpendicular to the ripples, which limits the spreading of the stem cell on the rippled substrate. This opens the perspective of controlling the micro-adhesion of stem cells and the orientation of their filopodia by tuning the anisotropic substrate morphology without chemical reactions occurring at the surface. (paper)

  14. Motivation alters response bias and neural activation patterns in a perceptual decision-making task.

    Science.gov (United States)

    Reckless, G E; Bolstad, I; Nakstad, P H; Andreassen, O A; Jensen, J

    2013-05-15

    Motivation has been demonstrated to affect individuals' response strategies in economic decision-making, however, little is known about how motivation influences perceptual decision-making behavior or its related neural activity. Given the important role motivation plays in shaping our behavior, a better understanding of this relationship is needed. A block-design, continuous performance, perceptual decision-making task where participants were asked to detect a picture of an animal among distractors was used during functional magnetic resonance imaging (fMRI). The effect of positive and negative motivation on sustained activity within regions of the brain thought to underlie decision-making was examined by altering the monetary contingency associated with the task. In addition, signal detection theory was used to investigate the effect of motivation on detection sensitivity, response bias and response time. While both positive and negative motivation resulted in increased sustained activation in the ventral striatum, fusiform gyrus, left dorsolateral prefrontal cortex (DLPFC) and ventromedial prefrontal cortex, only negative motivation resulted in the adoption of a more liberal, closer to optimal response bias. This shift toward a liberal response bias correlated with increased activation in the left DLPFC, but did not result in improved task performance. The present findings suggest that motivation alters aspects of the way perceptual decisions are made. Further, this altered response behavior is reflected in a change in left DLPFC activation, a region involved in the computation of perceptual decisions. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

  15. Effects of self-coupling and asymmetric output on metastable dynamical transient firing patterns in arrays of neurons with bidirectional inhibitory coupling.

    Science.gov (United States)

    Horikawa, Yo

    2016-04-01

    Metastable dynamical transient patterns in arrays of bidirectionally coupled neurons with self-coupling and asymmetric output were studied. First, an array of asymmetric sigmoidal neurons with symmetric inhibitory bidirectional coupling and self-coupling was considered and the bifurcations of its steady solutions were shown. Metastable dynamical transient spatially nonuniform states existed in the presence of a pair of spatially symmetric stable solutions as well as unstable spatially nonuniform solutions in a restricted range of the output gain of a neuron. The duration of the transients increased exponentially with the number of neurons up to the maximum number at which the spatially nonuniform steady solutions were stabilized. The range of the output gain for which they existed reduced as asymmetry in a sigmoidal output function of a neuron increased, while the existence range expanded as the strength of inhibitory self-coupling increased. Next, arrays of spiking neuron models with slow synaptic inhibitory bidirectional coupling and self-coupling were considered with computer simulation. In an array of Class 1 Hindmarsh-Rose type models, in which each neuron showed a graded firing rate, metastable dynamical transient firing patterns were observed in the presence of inhibitory self-coupling. This agreed with the condition for the existence of metastable dynamical transients in an array of sigmoidal neurons. In an array of Class 2 Bonhoeffer-van der Pol models, in which each neuron had a clear threshold between firing and resting, long-lasting transient firing patterns with bursting and irregular motion were observed. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. 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.

  17. 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.

  18. Risk-taking on the road and in the mind: behavioural and neural patterns of decision making between risky and safe drivers.

    Science.gov (United States)

    Ba, Yutao; Zhang, Wei; Peng, QiJia; Salvendy, Gavriel; Crundall, David

    2016-01-01

    Drivers' risk-taking is a key issue of road safety. This study explored individual differences in drivers' decision-making, linking external behaviours to internal neural activity, to reveal the cognitive mechanisms of risky driving. Twenty-four male drivers were split into two groups (risky vs. safe drivers) via the Drivier Behaviour Questionnaire-violation. The risky drivers demonstrated higher preference for the risky choices in the paradigms of Iowa Gambling Task and Balloon Analogue Risk Task. More importantly, the risky drivers showed lower amplitudes of feedback-related negativity (FRN) and loss-minus-gain FRN in both paradigms, which indicated their neural processing of error-detection. A significant difference of P300 amplitudes was also reported between groups, which indicated their neural processing of reward-evaluation and were modified by specific paradigm and feedback. These results suggested that the neural basis of risky driving was the decision patterns less revised by losses and more motivated by rewards. Risk-taking on the road is largely determined by inherent cognitive mechanisms, which can be indicated by the behavioural and neural patterns of decision-making. In this regard, it is feasible to quantize drivers’ riskiness in the cognitive stage before actual risky driving or accidents, and intervene accordingly.

  19. Neural correlates of intentional switching from ternary to binary meter in a musical hemiola pattern.

    Science.gov (United States)

    Fujioka, Takako; Fidali, Brian C; Ross, Bernhard

    2014-01-01

    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 sixteenth 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 transitions.

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

    Science.gov (United States)

    2015-12-31

    classification of liquid–vapor structures into flow patterns is useful for predicting heat transfer rates and, ultimately, system performance. Most flow and...Here, ~x represents the spa- tial variables, x and y, and t is time. This normalization assigns εð~x; tÞ to be zero for only vapor (εg) and one for...tube surface [17,22]. As in stratified wavy flow, interfacial waves were also present in stratified wavy transitional flow. The waves were more fre

  1. 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.

  2. Evidence for similar patterns of neural activity elicited by picture- and word-based representations of natural scenes.

    Science.gov (United States)

    Kumar, Manoj; Federmeier, Kara D; Fei-Fei, Li; Beck, Diane M

    2017-07-15

    frontal cortices. This similarity of neural activity patterns across the two input types, for categories that co-activate local brain regions, provides strong evidence of a common semantic code for pictures and words in the brain. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. Time trends in the levels and patterns of polycyclic aromatic hydrocarbons (PAHs) in pine bark, litter, and soil after a forest fire.

    Science.gov (United States)

    Choi, Sung-Deuk

    2014-02-01

    Forest fires are known as an important natural source of polycyclic aromatic hydrocarbons (PAHs), but time trends of PAH levels and patterns in various environmental compartments after forest fires have not been thoroughly studied yet. In this study, 16 US-EPA priority PAHs were analyzed for pine bark, litter, and soil samples collected one, three, five, and seven months after a forest fire in Pohang, South Korea. At the first sampling event, the highest levels of ∑16 PAHs were measured for the three types of samples (pine bark: 5,920 ng/g, litter: 1,540 ng/g, and soil: 133 ng/g). Thereafter, there were apparent decreasing trends in PAH levels; the control samples showed the lowest levels (pine bark: 124 ng/g, litter: 75 ng/g, and soil: 26 ng/g). The levels of PAHs in the litter and soil samples normalized by organic carbon (OC) fractions also showed decreasing trends, indicating a direct influence of the forest fire. Among the 16 target PAHs, naphthalene was a dominant compound for all types of samples. Light PAHs with 2-4 rings significantly contributed to the total concentration, and their contribution decreased in the course of time. Runoff by heavy precipitation, evaporation, and degradation of PAHs in the summer were probably the main reasons for the observed time trends. The results of principal component analysis (PCA) and diagnostic ratio also supported that the forest fire was indeed an important source of PAHs in the study area. © 2013.

  4. 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.

  5. Neural-network-based system for recognition of partially occluded shapes and patterns

    Science.gov (United States)

    Mital, Dinesh P.; Teoh, Eam-Khwang; Amarasinghe, S. K.; Suganthan, P. N.

    1996-10-01

    The purpose of this paper is to demonstrate how a structural matching approach can be used to perfonn effective rotational invariant fingerprint identification. In this approach, each of the exiracted features is correlated with Live of its nearest neighbouring features to form a local feature gmup for a first-stage matching. After that, the feature with the highest match is used as a central feature whereby all the other features are correlated to form a global feature group for a second.stage matching. The correlation between the features is in terms of distance and relative angle. This approach actually make the matching method rotational invariant A substantial amount of testing was carried out and it shows that this matching technique is capable of matching the four basic fingerprint patterns with an average matching time of4 seconds on a 66Mhz, 486 DX personal computer.

  6. Spiking patterns of a hippocampus model in electric fields

    International Nuclear Information System (INIS)

    Men Cong; Wang Jiang; Qin Ying-Mei; Wei Xi-Le; Deng Bin; Che Yan-Qiu

    2011-01-01

    We develop a model of CA3 neurons embedded in a resistive array to mimic the effects of electric fields from a new perspective. Effects of DC and sinusoidal electric fields on firing patterns in CA3 neurons are investigated in this study. The firing patterns can be switched from no firing pattern to burst or from burst to fast periodic firing pattern with the increase of DC electric field intensity. It is also found that the firing activities are sensitive to the frequency and amplitude of the sinusoidal electric field. Different phase-locking states and chaotic firing regions are observed in the parameter space of frequency and amplitude. These findings are qualitatively in accordance with the results of relevant experimental and numerical studies. It is implied that the external or endogenous electric field can modulate the neural code in the brain. Furthermore, it is helpful to develop control strategies based on electric fields to control neural diseases such as epilepsy. (interdisciplinary physics and related areas of science and technology)

  7. 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.

  8. The COMT Val/Met polymorphism is associated with reading related skills and consistent patterns of functional neural activation

    Science.gov (United States)

    Landi, Nicole; Frost, Stephen J.; Mencl, W. Einar; Preston, Jonathan L.; Jacobsen, Leslie K.; Lee, Maria; Yrigollen, Carolyn; Pugh, Kenneth R.; Grigorenko, Elena L.

    2013-01-01

    In both children and adults there is large variability in reading skill, with approximately 5–10% of individuals characterized as having reading disability; these individuals struggle to learn to read despite adequate intelligence and opportunity. Although it is well established that a substantial portion of this variability is attributed to the genetic differences between individuals, specifics of the connections between reading and the genome are not understood. This article presents data that suggest that variation in the COMT gene, which has previously been associated with variation in higher-order cognition, is associated with reading and reading-related skills, both at the level of brain and behavior. In particular, we found that the COMT Val/Met polymorphism at rs4680, which results in the substitution of the ancestral Valine (Val) by Methionine (Met), was associated with better performance on a number of critical reading measures and with patterns of functional neural activation that have been linked to better readers. We argue that this polymorphism, known for its broad effects on cognition, may modulate (likely through frontal lobe function) reading skill. PMID:23278923

  9. The COMT Val/Met polymorphism is associated with reading-related skills and consistent patterns of functional neural activation.

    Science.gov (United States)

    Landi, Nicole; Frost, Stephen J; Mencl, W Einar; Preston, Jonathan L; Jacobsen, Leslie K; Lee, Maria; Yrigollen, Carolyn; Pugh, Kenneth R; Grigorenko, Elena L

    2013-01-01

    In both children and adults there is large variability in reading skill, with approximately 5-10% of individuals characterized as having reading disability; these individuals struggle to learn to read despite adequate intelligence and opportunity. Although it is well established that a substantial portion of this variability is attributed to the genetic differences between individuals, specifics of the connections between reading and the genome are not understood. This article presents data that suggest that variation in the COMT gene, which has previously been associated with variation in higher-order cognition, is associated with reading and reading-related skills, at the level of both brain and behavior. In particular, we found that the COMT Val/Met polymorphism at rs4680, which results in the substitution of the ancestral Valine (Val) by Methionine (Met), was associated with better performance on a number of critical reading measures and with patterns of functional neural activation that have been linked to better readers. We argue that this polymorphism, known for its broad effects on cognition, may modulate (likely through frontal lobe function) reading skill. © 2012 Blackwell Publishing Ltd.

  10. 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

    2018-04-02

    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.

  11. A study on the optimal fuel loading pattern design in pressurized water reactor using the artificial neural network and the fuzzy rule based system

    International Nuclear Information System (INIS)

    Kim, Han Gon; Chang, Soon Heung; Lee, Byung

    2004-01-01

    The Optimal Fuel Shuffling System (OFSS) is developed for optimal design of PWR fuel loading pattern. In this paper, an optimal loading pattern is defined that the local power peaking factor is lower than predetermined value during one cycle and the effective multiplication factor is maximized in order to extract maximum energy. OFSS is a hybrid system that a rule based system, a fuzzy logic, and an artificial neural network are connected each other. The rule based system classifies loading patterns into two classes using several heuristic rules and a fuzzy rule. A fuzzy rule is introduced to achieve more effective and fast searching. Its membership function is automatically updated in accordance with the prediction results. The artificial neural network predicts core parameters for the patterns generated from the rule based system. The back-propagation network is used for fast prediction of core parameters. The artificial neural network and the fuzzy logic can be used as the tool for improvement of existing algorithm's capabilities. OFSS was demonstrated and validated for cycle 1 of Kori unit 1 PWR. (author)

  12. A study on the optimal fuel loading pattern design in pressurized water reactor using the artificial neural network and the fuzzy rule based system

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Han Gon; Chang, Soon Heung; Lee, Byung [Department of Nuclear Engineering, Korea Advanced Institute of Science and Technology, Yusong-gu, Taejon (Korea, Republic of)

    2004-07-01

    The Optimal Fuel Shuffling System (OFSS) is developed for optimal design of PWR fuel loading pattern. In this paper, an optimal loading pattern is defined that the local power peaking factor is lower than predetermined value during one cycle and the effective multiplication factor is maximized in order to extract maximum energy. OFSS is a hybrid system that a rule based system, a fuzzy logic, and an artificial neural network are connected each other. The rule based system classifies loading patterns into two classes using several heuristic rules and a fuzzy rule. A fuzzy rule is introduced to achieve more effective and fast searching. Its membership function is automatically updated in accordance with the prediction results. The artificial neural network predicts core parameters for the patterns generated from the rule based system. The back-propagation network is used for fast prediction of core parameters. The artificial neural network and the fuzzy logic can be used as the tool for improvement of existing algorithm's capabilities. OFSS was demonstrated and validated for cycle 1 of Kori unit 1 PWR. (author)

  13. Fire in the Earth system.

    Science.gov (United States)

    Bowman, David M J S; Balch, Jennifer K; Artaxo, Paulo; Bond, William J; Carlson, Jean M; Cochrane, Mark A; D'Antonio, Carla M; Defries, Ruth S; Doyle, John C; Harrison, Sandy P; Johnston, Fay H; Keeley, Jon E; Krawchuk, Meg A; Kull, Christian A; Marston, J Brad; Moritz, Max A; Prentice, I Colin; Roos, Christopher I; Scott, Andrew C; Swetnam, Thomas W; van der Werf, Guido R; Pyne, Stephen J

    2009-04-24

    Fire is a worldwide phenomenon that appears in the geological record soon after the appearance of terrestrial plants. Fire influences global ecosystem patterns and processes, including vegetation distribution and structure, the carbon cycle, and climate. Although humans and fire have always coexisted, our capacity to manage fire remains imperfect and may become more difficult in the future as climate change alters fire regimes. This risk is difficult to assess, however, because fires are still poorly represented in global models. Here, we discuss some of the most important issues involved in developing a better understanding of the role of fire in the Earth system.

  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. 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.

  17. 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.

  18. Fire Perimeters

    Data.gov (United States)

    California Natural Resource Agency — 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...

  19. Fire History

    Data.gov (United States)

    California Natural Resource Agency — 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...

  20. Notch signaling patterns neurogenic ectoderm and regulates the asymmetric division of neural progenitors in sea urchin embryos.

    Science.gov (United States)

    Mellott, Dan O; Thisdelle, Jordan; Burke, Robert D

    2017-10-01

    We have examined regulation of neurogenesis by Delta/Notch signaling in sea urchin embryos. At gastrulation, neural progenitors enter S phase coincident with expression of Sp-SoxC. We used a BAC containing GFP knocked into the Sp-SoxC locus to label neural progenitors. Live imaging and immunolocalizations indicate that Sp-SoxC-expressing cells divide to produce pairs of adjacent cells expressing GFP. Over an interval of about 6 h, one cell fragments, undergoes apoptosis and expresses high levels of activated Caspase3. A Notch reporter indicates that Notch signaling is activated in cells adjacent to cells expressing Sp-SoxC. Inhibition of γ-secretase, injection of Sp-Delta morpholinos or CRISPR/Cas9-induced mutation of Sp-Delta results in supernumerary neural progenitors and neurons. Interfering with Notch signaling increases neural progenitor recruitment and pairs of neural progenitors. Thus, Notch signaling restricts the number of neural progenitors recruited and regulates the fate of progeny of the asymmetric division. We propose a model in which localized signaling converts ectodermal and ciliary band cells to neural progenitors that divide asymmetrically to produce a neural precursor and an apoptotic cell. © 2017. Published by The Company of Biologists Ltd.

  1. 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.

  2. 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.

  3. Neural coding in graphs of bidirectional associative memories.

    Science.gov (United States)

    Bouchain, A David; Palm, Günther

    2012-01-24

    In the last years we have developed large neural network models for the realization of complex cognitive tasks in a neural network architecture that resembles the network of the cerebral cortex. We have used networks of several cortical modules that contain two populations of neurons (one excitatory, one inhibitory). The excitatory populations in these so-called "cortical networks" are organized as a graph of Bidirectional Associative Memories (BAMs), where edges of the graph correspond to BAMs connecting two neural modules and nodes of the graph correspond to excitatory populations with associative feedback connections (and inhibitory interneurons). The neural code in each of these modules consists essentially of the firing pattern of the excitatory population, where mainly it is the subset of active neurons that codes the contents to be represented. The overall activity can be used to distinguish different properties of the patterns that are represented which we need to distinguish and control when performing complex tasks like language understanding with these cortical networks. The most important pattern properties or situations are: exactly fitting or matching input, incomplete information or partially matching pattern, superposition of several patterns, conflicting information, and new information that is to be learned. We show simple simulations of these situations in one area or module and discuss how to distinguish these situations based on the overall internal activation of the module. This article is part of a Special Issue entitled "Neural Coding". Copyright © 2011 Elsevier B.V. All rights reserved.

  4. The role of fuels for understanding fire behavior and fire effects

    Science.gov (United States)

    E. Louise Loudermilk; J. Kevin Hiers; Joseph J. O' Brien

    2018-01-01

    Fire ecology, which has emerged as a critical discipline, links the complex interactions that occur between fire regimes and ecosystems. The ecology of fuels, a first principle in fire ecology, identifies feedbacks between vegetation and fire behavior-a cyclic process that starts with fuels influencing fire behavior, which in turn governs patterns of postfire...

  5. Nanoengineered Polystyrene Surfaces with Nanopore Array Pattern Alters Cytoskeleton Organization and Enhances Induction of Neural Differentiation of Human Adipose-Derived Stem Cells.

    Science.gov (United States)

    Jung, Ae Ryang; Kim, Richard Y; Kim, Hyung Woo; Shrestha, Kshitiz Raj; Jeon, Seung Hwan; Cha, Kyoung Je; Park, Yong Hyun; Kim, Dong Sung; Lee, Ji Youl

    2015-07-01

    Human adipose-derived stem cells (hADSCs) can differentiate into various cell types depending on chemical and topographical cues. One topographical cue recently noted to be successful in inducing differentiation is the nanoengineered polystyrene surface containing nanopore array-patterned substrate (NP substrate), which is designed to mimic the nanoscale topographical features of the extracellular matrix. In this study, efficacies of NP and flat substrates in inducing neural differentiation of hADSCs were examined by comparing their substrate-cell adhesion rates, filopodia growth, nuclei elongation, and expression of neural-specific markers. The polystyrene nano Petri dishes containing NP substrates were fabricated by a nano injection molding process using a nickel electroformed nano-mold insert (Diameter: 200 nm. Depth of pore: 500 nm. Center-to-center distance: 500 nm). Cytoskeleton and filopodia structures were observed by scanning electron microscopy and F-actin staining, while cell adhesion was tested by vinculin staining after 24 and 48 h of seeding. Expression of neural specific markers was examined by real-time quantitative polymerase chain reaction and immunocytochemistry. Results showed that NP substrates lead to greater substrate-cell adhesion, filopodia growth, nuclei elongation, and expression of neural specific markers compared to flat substrates. These results not only show the advantages of NP substrates, but they also suggest that further study into cell-substrate interactions may yield great benefits for biomaterial engineering.

  6. 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.

  7. Fire in the Earth System

    NARCIS (Netherlands)

    Bowman, D.M.J.S.; Balch, J.K.; Artaxo, P.; Bond, W.J.; Carlson, J.M.; Cochrane, M.A.; D'Antonio, C.M.; DeFries, R.S.; Doyle, J.C.; Harrison, S.P.; Johnston, F.H.; Keeley, J.E.; Krawchuk, M.A.; Kull, C.A.; Marston, J.B.; Moritz, M.A.; Prentice, I.C.; Roos, C.I.; Scott, A.C.; Swetnam, T.W.; van der Werf, G.R.; Pyne, S.J.

    2009-01-01

    Fire is a worldwide phenomenon that appears in the geological record soon after the appearance of terrestrial plants. Fire influences global ecosystem patterns and processes, including vegetation distribution and structure, the carbon cycle, and climate. Although humans and fire have always

  8. 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

  9. Assessing the suitability of soft computing approaches for forest fires prediction

    Directory of Open Access Journals (Sweden)

    Samaher Al_Janabi

    2018-07-01

    Full Text Available Forest fires present one of the main causes of environmental hazards that have many negative results in different aspect of life. Therefore, early prediction, fast detection and rapid action are the key elements for controlling such phenomenon and saving lives. Through this work, 517 different entries were selected at different times for montesinho natural park (MNP in Portugal to determine the best predictor that has the ability to detect forest fires, The principle component analysis (PCA was applied to find the critical patterns and particle swarm optimization (PSO technique was used to segment the fire regions (clusters. In the next stage, five soft computing (SC Techniques based on neural network were used in parallel to identify the best technique that would potentially give more accurate and optimum results in predicting of forest fires, these techniques namely; cascade correlation network (CCN, multilayer perceptron neural network (MPNN, polynomial neural network (PNN, radial basis function (RBF and support vector machine (SVM In the final stage, the predictors and their performance were evaluated based on five quality measures including root mean squared error (RMSE, mean squared error (MSE, relative absolute error (RAE, mean absolute error (MAE and information gain (IG. The results indicate that SVM technique was more effective and efficient than the RBF, MPNN, PNN and CCN predictors. The results also show that the SVM algorithm provides more precise predictions compared with other predictors with small estimation error. The obtained results confirm that the SVM improves the prediction accuracy and suitable for forest fires prediction compared to other methods. Keywords: Forest fires, Soft computing, Prediction, Principle component analysis, Particle swarm optimization, Cascade correlation network, Multilayer perceptron neural network, Polynomial neural networks, Radial basis function, Support vector machine

  10. 1/f neural noise and electrophysiological indices of contextual prediction in aging.

    Science.gov (United States)

    Dave, S; Brothers, T A; Swaab, T Y

    2018-07-15

    Prediction of upcoming words during reading has been suggested to enhance the efficiency of discourse processing. Emerging models have postulated that predictive mechanisms require synchronous firing of neural networks, but to date, this relationship has been investigated primarily through oscillatory activity in narrow frequency bands. A recently-developed measure proposed to reflect broadband neural activity - and thereby synchronous neuronal firing - is 1/f neural noise extracted from EEG spectral power. Previous research has indicated that this measure of 1/f neural noise changes across the lifespan, and these neural changes predict age-related behavioral impairments in visual working memory. Using a cross-sectional sample of young and older adults, we examined age-related changes in 1/f neural noise and whether this measure predicted ERP correlates of successful lexical prediction during discourse comprehension. 1/f neural noise across two different language tasks revealed high within-subject correlations, indicating that this measure can provide a reliable index of individualized patterns of neural activation. In addition to age, 1/f noise was a significant predictor of N400 effects of successful lexical prediction; however, noise did not mediate age-related declines in other ERP effects. We discuss broader implications of these findings for theories of predictive processing, as well as potential applications of 1/f noise across research populations. Copyright © 2018 Elsevier B.V. All rights reserved.

  11. Patterns of cortical oscillations organize neural activity into whole-brain functional networks evident in the fMRI BOLD signal

    Directory of Open Access Journals (Sweden)

    Jennifer C Whitman

    2013-03-01

    Full Text Available Recent findings from electrophysiology and multimodal neuroimaging have elucidated the relationship between patterns of cortical oscillations evident in EEG / MEG and the functional brain networks evident in the BOLD signal. Much of the existing literature emphasized how high-frequency cortical oscillations are thought to coordinate neural activity locally, while low-frequency oscillations play a role in coordinating activity between more distant brain regions. However, the assignment of different frequencies to different spatial scales is an oversimplification. A more informative approach is to explore the arrangements by which these low- and high-frequency oscillations work in concert, coordinating neural activity into whole-brain functional networks. When relating such networks to the BOLD signal, we must consider how the patterns of cortical oscillations change at the same speed as cognitive states, which often last less than a second. Consequently, the slower BOLD signal may often reflect the summed neural activity of several transient network configurations. This temporal mismatch can be circumvented if we use spatial maps to assess correspondence between oscillatory networks and BOLD networks.

  12. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.

    2003-01-01

    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

  13. Hip position and sex differences in motor unit firing patterns of the vastus medialis and vastus medialis oblique in healthy individuals.

    Science.gov (United States)

    Peng, Yi-Ling; Tenan, Matthew S; Griffin, Lisa

    2018-06-01

    Weakness of the vastus medialis oblique (VMO) has been proposed to explain the high prevalence of knee pain in female subjects. Clinicians commonly use exercises in an attempt to preferentially activate the VMO. Recently, our group found evidence to support clinical theory that the VMO is neurologically distinct from the vastus medialis (VM). However, the ability to voluntarily activate these muscle subsections is still disputed. The aim of this study was to determine if VM and VMO activation varies between sexes and if control of the two muscles is different between rehabilitation exercises. Thirteen men and 13 women performed isometric straight leg raises in two hip positions, neutral hip rotation and 30 degrees lateral hip rotation. Bipolar intramuscular fine-wire electrodes were inserted into the VM and VMO to obtain motor unit recruitment thresholds and initial firing rates at recruitment. Linear mixed models and Tukey post hoc tests were used to assess significant differences in 654 motor units. Women demonstrated faster motor unit firing rate at recruitment, 1.18 ± 0.56 Hz higher than men. Motor units fired 0.47 ± 0.19 Hz faster during neutral hip rotation compared with lateral hip rotation. The VMO motor units were recruited 2.92 ± 1.28% earlier than the VM. All motor units were recruited 3.74 ± 1.27% earlier during neutral hip rotation than lateral hip rotation. Thus the VM and the VMO can be activated differentially, and their motor unit recruitment properties are affected by sex and hip position. NEW & NOTEWORTHY This is the first study to reveal differential activation of the vastus medialis oblique from the vastus medialis in clinical exercise protocols. Our research group used fine-wire electrodes to examine EMG signals of the vastus medialis oblique and vastus medialis to avoid possible cross talk. We also consider the effect of sex on motor unit firing patterns because of higher prevalence of knee pain in women, and yet few

  14. Spike Neural Models Part II: Abstract Neural Models

    Directory of Open Access Journals (Sweden)

    Johnson, Melissa G.

    2018-02-01

    Full Text Available 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 which is not biologically realistic but does quickly and easily integrate input to produce spikes. Izhikevich's model is based on Hodgkin-Huxley's model but simplified such that it uses only two differentiation equations and four parameters to produce various realistic spike patterns. LIF is based on a standard electrical circuit and contains one equation. Either of these two models, or any of the many other models in literature can be used in a SNN. Choosing a neural model is an important task that depends on the goal of the research and the resources available. Once a model is chosen, network decisions such as connectivity, delay, and sparseness, need to be made. Understanding neural models and how they are incorporated into the network is the first step in creating a SNN.

  15. 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.

  16. 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…

  17. 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.

  18. Multi-mode energy management strategy for fuel cell electric vehicles based on driving pattern identification using learning vector quantization neural network algorithm

    Science.gov (United States)

    Song, Ke; Li, Feiqiang; Hu, Xiao; He, Lin; Niu, Wenxu; Lu, Sihao; Zhang, Tong

    2018-06-01

    The development of fuel cell electric vehicles can to a certain extent alleviate worldwide energy and environmental issues. While a single energy management strategy cannot meet the complex road conditions of an actual vehicle, this article proposes a multi-mode energy management strategy for electric vehicles with a fuel cell range extender based on driving condition recognition technology, which contains a patterns recognizer and a multi-mode energy management controller. This paper introduces a learning vector quantization (LVQ) neural network to design the driving patterns recognizer according to a vehicle's driving information. This multi-mode strategy can automatically switch to the genetic algorithm optimized thermostat strategy under specific driving conditions in the light of the differences in condition recognition results. Simulation experiments were carried out based on the model's validity verification using a dynamometer test bench. Simulation results show that the proposed strategy can obtain better economic performance than the single-mode thermostat strategy under dynamic driving conditions.

  19. Patterns of altered neural synchrony in the default mode network in autism spectrum disorder revealed with magnetoencephalography (MEG): Relationship to clinical symptomatology.

    Science.gov (United States)

    Lajiness-O'Neill, Renée; Brennan, Jonathan R; Moran, John E; Richard, Annette E; Flores, Ana-Mercedes; Swick, Casey; Goodcase, Ryan; Andersen, Tiffany; McFarlane, Kaitlyn; Rusiniak, Kenneth; Kovelman, Ioulia; Wagley, Neelima; Ugolini, Maggie; Albright, Jeremy; Bowyer, Susan M

    2018-03-01

    Disrupted neural synchrony may be a primary electrophysiological abnormality in autism spectrum disorders (ASD), altering communication between discrete brain regions and contributing to abnormalities in patterns of connectivity within identified neural networks. Studies exploring brain dynamics to comprehensively characterize and link connectivity to large-scale cortical networks and clinical symptoms are lagging considerably. Patterns of neural coherence within the Default Mode Network (DMN) and Salience Network (SN) during resting state were investigated in 12 children with ASD (M Age  = 9.2) and 13 age and gender-matched neurotypicals (NT) (M Age  = 9.3) with magnetoencephalography. Coherence between 231 brain region pairs within four frequency bands (theta (4-7 Hz), alpha, (8-12 Hz), beta (13-30 Hz), and gamma (30-80 Hz)) was calculated. Relationships between neural coherence and social functioning were examined. ASD was characterized by lower synchronization across all frequencies, reaching clinical significance in the gamma band. Lower gamma synchrony between fronto-temporo-parietal regions was observed, partially consistent with diminished default mode network (DMN) connectivity. Lower gamma coherence in ASD was evident in cross-hemispheric connections between: angular with inferior/middle frontal; middle temporal with middle/inferior frontal; and within right-hemispheric connections between angular, middle temporal, and inferior/middle frontal cortices. Lower gamma coherence between left angular and left superior frontal, right inferior/middle frontal, and right precuneus and between right angular and inferior/middle frontal cortices was related to lower social/social-communication functioning. Results suggest a pattern of lower gamma band coherence in a subset of regions within the DMN in ASD (angular and middle temporal cortical areas) related to lower social/social-communicative functioning. Autism Res 2018, 11: 434-449. © 2017 International

  20. Comparing success levels of different neural network structures in extracting discriminative information from the response patterns of a temperature-modulated resistive gas sensor

    Science.gov (United States)

    Hosseini-Golgoo, S. M.; Bozorgi, H.; Saberkari, A.

    2015-06-01

    Performances of three neural networks, consisting of a multi-layer perceptron, a radial basis function, and a neuro-fuzzy network with local linear model tree training algorithm, in modeling and extracting discriminative features from the response patterns of a temperature-modulated resistive gas sensor are quantitatively compared. For response pattern recording, a voltage staircase containing five steps each with a 20 s plateau is applied to the micro-heater of the sensor, when 12 different target gases, each at 11 concentration levels, are present. In each test, the hidden layer neuron weights are taken as the discriminatory feature vector of the target gas. These vectors are then mapped to a 3D feature space using linear discriminant analysis. The discriminative information content of the feature vectors are determined by the calculation of the Fisher’s discriminant ratio, affording quantitative comparison among the success rates achieved by the different neural network structures. The results demonstrate a superior discrimination ratio for features extracted from local linear neuro-fuzzy and radial-basis-function networks with recognition rates of 96.27% and 90.74%, respectively.

  1. Comparing success levels of different neural network structures in extracting discriminative information from the response patterns of a temperature-modulated resistive gas sensor

    International Nuclear Information System (INIS)

    Hosseini-Golgoo, S M; Bozorgi, H; Saberkari, A

    2015-01-01

    Performances of three neural networks, consisting of a multi-layer perceptron, a radial basis function, and a neuro-fuzzy network with local linear model tree training algorithm, in modeling and extracting discriminative features from the response patterns of a temperature-modulated resistive gas sensor are quantitatively compared. For response pattern recording, a voltage staircase containing five steps each with a 20 s plateau is applied to the micro-heater of the sensor, when 12 different target gases, each at 11 concentration levels, are present. In each test, the hidden layer neuron weights are taken as the discriminatory feature vector of the target gas. These vectors are then mapped to a 3D feature space using linear discriminant analysis. The discriminative information content of the feature vectors are determined by the calculation of the Fisher’s discriminant ratio, affording quantitative comparison among the success rates achieved by the different neural network structures. The results demonstrate a superior discrimination ratio for features extracted from local linear neuro-fuzzy and radial-basis-function networks with recognition rates of 96.27% and 90.74%, respectively. (paper)

  2. A plausible neural circuit for decision making and its formation based on reinforcement learning.

    Science.gov (United States)

    Wei, Hui; Dai, Dawei; Bu, Yijie

    2017-06-01

    A human's, or lower insects', behavior is dominated by its nervous system. Each stable behavior has its own inner steps and control rules, and is regulated by a neural circuit. Understanding how the brain influences perception, thought, and behavior is a central mandate of neuroscience. The phototactic flight of insects is a widely observed deterministic behavior. Since its movement is not stochastic, the behavior should be dominated by a neural circuit. Based on the basic firing characteristics of biological neurons and the neural circuit's constitution, we designed a plausible neural circuit for this phototactic behavior from logic perspective. The circuit's output layer, which generates a stable spike firing rate to encode flight commands, controls the insect's angular velocity when flying. The firing pattern and connection type of excitatory and inhibitory neurons are considered in this computational model. We simulated the circuit's information processing using a distributed PC array, and used the real-time average firing rate of output neuron clusters to drive a flying behavior simulation. In this paper, we also explored how a correct neural decision circuit is generated from network flow view through a bee's behavior experiment based on the reward and punishment feedback mechanism. The significance of this study: firstly, we designed a neural circuit to achieve the behavioral logic rules by strictly following the electrophysiological characteristics of biological neurons and anatomical facts. Secondly, our circuit's generality permits the design and implementation of behavioral logic rules based on the most general information processing and activity mode of biological neurons. Thirdly, through computer simulation, we achieved new understanding about the cooperative condition upon which multi-neurons achieve some behavioral control. Fourthly, this study aims in understanding the information encoding mechanism and how neural circuits achieve behavior control

  3. 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.

  4. Stimulus-dependent suppression of chaos in recurrent neural networks

    International Nuclear Information System (INIS)

    Rajan, Kanaka; Abbott, L. F.; Sompolinsky, Haim

    2010-01-01

    Neuronal activity arises from an interaction between ongoing firing generated spontaneously by neural circuits and responses driven by external stimuli. Using mean-field analysis, we ask how a neural network that intrinsically generates chaotic patterns of activity can remain sensitive to extrinsic input. We find that inputs not only drive network responses, but they also actively suppress ongoing activity, ultimately leading to a phase transition in which chaos is completely eliminated. The critical input intensity at the phase transition is a nonmonotonic function of stimulus frequency, revealing a 'resonant' frequency at which the input is most effective at suppressing chaos even though the power spectrum of the spontaneous activity peaks at zero and falls exponentially. A prediction of our analysis is that the variance of neural responses should be most strongly suppressed at frequencies matching the range over which many sensory systems operate.

  5. Effects of potassium concentration on firing patterns of low-calcium epileptiform activity in anesthetized rat hippocampus: inducing of persistent spike activity.

    Science.gov (United States)

    Feng, Zhouyan; Durand, Dominique M

    2006-04-01

    It has been shown that a low-calcium high-potassium solution can generate ictal-like epileptiform activity in vitro and in vivo. Moreover, during status epileptiform activity, the concentration of [K+]o increases, and the concentration of [Ca2+]o decreases in brain tissue. Therefore we tested the hypothesis that long-lasting persistent spike activity, similar to one of the patterns of status epilepticus, could be generated by a high-potassium, low-calcium solution in the hippocampus in vivo. Artificial cerebrospinal fluid was perfused over the surface of the exposed left dorsal hippocampus of anesthetized rats. A stimulating electrode and a recording probe were placed in the CA1 region. By elevating K+ concentration from 6 to 12 mM in the perfusate solution, the typical firing pattern of low-calcium ictal bursts was transformed into persistent spike activity in the CA1 region with synaptic transmission being suppressed by calcium chelator EGTA. The activity was characterized by double spikes repeated at a frequency approximately 4 Hz that could last for >1 h. The analysis of multiple unit activity showed that both elevating [K+]o and lowering [Ca2+]o decreased the inhibition period after the response of paired-pulse stimulation, indicating a suppression of the after-hyperpolarization (AHP) activity. These results suggest that persistent status epilepticus-like spike activity can be induced by nonsynaptic mechanisms when synaptic transmission is blocked. The unique double-spike pattern of this activity is presumably caused by higher K+ concentration augmenting the frequency of typical low-calcium nonsynaptic burst activity.

  6. Creative-Dynamics Approach To Neural Intelligence

    Science.gov (United States)

    Zak, Michail A.

    1992-01-01

    Paper discusses approach to mathematical modeling of artificial neural networks exhibiting complicated behaviors reminiscent of creativity and intelligence of biological neural networks. Neural network treated as non-Lipschitzian dynamical system - as described in "Non-Lipschitzian Dynamics For Modeling Neural Networks" (NPO-17814). System serves as tool for modeling of temporal-pattern memories and recognition of complicated spatial patterns.

  7. 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.

  8. 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.

  9. Synoptic weather types associated with critical fire weather

    Science.gov (United States)

    Mark J. Schroeder; Monte Glovinsky; Virgil F. Hendricks; Frank C. Hood; Melvin K. Hull; Henry L. Jacobson; Robert Kirkpatrick; Daniel W. Krueger; Lester P. Mallory; Albert G. Oeztel; Robert H. Reese; Leo A. Sergius; Charles E. Syverson

    1964-01-01

    Recognizing that weather is an important factor in the spread of both urban and wildland fires, a study was made of the synoptic weather patterns and types which produce strong winds, low relative humidities, high temperatures, and lack of rainfall--the conditions conducive to rapid fire spread. Such historic fires as the San Francisco fire of 1906, the Berkeley fire...

  10. 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...

  11. The role of the mesenchyme in mouse neural fold elevation. II. Patterns of hyaluronate synthesis and distribution in embryos developing in vitro

    International Nuclear Information System (INIS)

    Morris-Wiman, J.; Brinkley, L.L.

    1990-01-01

    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

  12. Fire protection

    International Nuclear Information System (INIS)

    Janetzky, E.

    1980-01-01

    Safety and fire prevention measurements have to be treated like the activities developing, planning, construction and erection. Therefore it is necessary that these measurements have to be integrated into the activities mentioned above at an early stage in order to guarantee their effectiveness. With regard to fire accidents the statistics of the insurance companies concerned show that the damage caused increased in the last years mainly due to high concentration of material. Organization of fire prevention and fire fighting, reasons of fire break out, characteristics and behaviour of fire, smoke and fire detection, smoke and heat venting, fire extinguishers (portable and stationary), construction material in presence of fire, respiratory protection etc. will be discussed. (orig./RW)

  13. A Heuristic Approach to Intra-Brain Communications Using Chaos in a Recurrent Neural Network Model

    Science.gov (United States)

    Soma, Ken-ichiro; Mori, Ryota; Sato, Ryuichi; Nara, Shigetoshi

    2011-09-01

    To approach functional roles of chaos in brain, a heuristic model to consider mechanisms of intra-brain communications is proposed. The key idea is to use chaos in firing pattern dynamics of a recurrent neural network consisting of birary state neurons, as propagation medium of pulse signals. Computer experiments and numerical methods are introduced to evaluate signal transport characteristics by calculating correlation functions between sending neurons and receiving neurons of pulse signals.

  14. Wildland fire as a self-regulating mechanism: the role of previous burns and weather in limiting fire progression

    Science.gov (United States)

    Sean A. Parks; Lisa M. Holsinger; Carol Miller; Cara R. Nelson

    2015-01-01

    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-...

  15. 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

  16. AN UNUSUAL PATTERN OF GENE FLOW BETWEEN THE TWO SOCIAL FORMS OF THE FIRE ANT SOLENOPSIS INVICTA.

    Science.gov (United States)

    Ross, Kenneth G; Shoemaker, D DeWayne

    1993-10-01

    Uncertainty over the role of shifts in social behavior in the process of speciation in social insects has stimulated interest in determining the extent of gene flow between conspecific populations differing in colony social organization. Allele and genotype frequencies at 12 neutral polymorphic protein markers, as well as the numbers of alleles at the sex-determining locus (loci), are shown here to be consistent with significant ongoing gene flow between two geographically adjacent populations of Solenopsis invicta that differ in colony queen number. Data from a thirteenth protein marker that is under strong differential selection in the two social forms confirm that such gene flow occurs. Data from this selected locus, combined with knowledge of the reproductive biology of the two social forms, further suggest that interform gene flow is largely unidirectional and mediated through males only. This unusual pattern of gene flow results from the influence of the unique social enviroments of the two forms on the behavior of workers and on the reproductive physiology of sexuals. © 1993 The Society for the Study of Evolution.

  17. Paraconsistents artificial neural networks applied to the study of mutational patterns of the F subtype of the viral strains of HIV-1 to antiretroviral therapy

    Directory of Open Access Journals (Sweden)

    PAULO C.C. DOS SANTOS

    2016-03-01

    Full Text Available ABSTRACT The high variability of HIV-1 as well as the lack of efficient repair mechanisms during the stages of viral replication, contribute to the rapid emergence of HIV-1 strains resistant to antiretroviral drugs. The selective pressure exerted by the drug leads to fixation of mutations capable of imparting varying degrees of resistance. The presence of these mutations is one of the most important factors in the failure of therapeutic response to medications. Thus, it is of critical to understand the resistance patterns and mechanisms associated with them, allowing the choice of an appropriate therapeutic scheme, which considers the frequency, and other characteristics of mutations. Utilizing Paraconsistents Artificial Neural Networks, seated in Paraconsistent Annotated Logic Et which has the capability of measuring uncertainties and inconsistencies, we have achieved levels of agreement above 90% when compared to the methodology proposed with the current methodology used to classify HIV-1 subtypes. The results demonstrate that Paraconsistents Artificial Neural Networks can serve as a promising tool of analysis.

  18. A study on the optimal fuel loading pattern design in pressurized water reactors using the artificial neural network and the fuzzy rule based system

    International Nuclear Information System (INIS)

    Kim, Han Gon

    1993-02-01

    In pressurized water reactors, the fuel reloading problem has significant meaning in terms of both safety and economic aspects. Therefore the general problem of incore fuel management for a PWR consists of determining the fuel reloading policy for each cycle that minimize unit energy cost under the constraints imposed on various core parameters, e.g., a local power peaking factor and an assembly burnup. This is equivalent that a cycle length is maximized for a given energy cost under the various constraints. Existing optimization methods do not ensure the global optimum solution because of the essential limitation of their searching algorithms. They only find near optimal solutions. To solve this limitation, a hybrid artificial neural network system is developed for the optimal fuel loading pattern design using a fuzzy rule based system and an artificial neural networks. This system finds the patterns that P max is lower than the predetermined value and K eff is larger than the reference value. The back-propagation networks are developed to predict PWR core parameters. Reference PWR is an 121-assembly typical PWR. The local power peaking factor and the effective multiplication factor at BOC condition are predicted. To obtain target values of these two parameters, the QCC code are used. Using this code, 1000 training patterns are obtained, randomly. Two networks are constructed, one for P max and another for K eff Both of two networks have 21 input layer neurons, 18 output layer neurons, and 120 and 393 hidden layer neurons, respectively. A new learning algorithm is proposed. This is called the advanced adaptive learning algorithm. The weight change step size of this algorithm is optimally varied inversely proportional to the average difference between an actual output value and an ideal target value. This algorithm greatly enhances the convergence speed of a BPN. In case of P max prediction, 98% of the untrained patterns are predicted within 6% error, and in case

  19. 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...

  20. Pattern recognition, neural networks, genetic algorithms and high performance computing in nuclear reactor diagnostics. Results and perspectives

    International Nuclear Information System (INIS)

    Dzwinel, W.; Pepyolyshev, N.

    1996-01-01

    The main goal of this paper is the presentation of our experience in development of the diagnostic system for the IBR-2 (Russia - Dubna) nuclear reactor. The authors show the principal results of the system modifications to make it work more reliable and much faster. The former needs the adaptation of new techniques of data processing, the latter, implementation of the newest computational facilities. The results of application of the clustering techniques and a method of visualization of the multi-dimensional information directly on the operator display are presented. The experiences with neural nets, used for prediction of the reactor operation, are discussed. The genetic algorithms were also tested, to reduce the quantity of data nd extracting the most informative components of the analyzed spectra. (authors)

  1. Adsorber fires

    International Nuclear Information System (INIS)

    Holmes, W.

    1987-01-01

    The following conclusions are offered with respect to activated charcoal filter systems in nuclear power plants: (1) The use of activated charcoal in nuclear facilities presents a potential for deep-seated fires. (2) The defense-in-depth approach to nuclear fire safety requires that if an ignition should occur, fires must be detected quickly and subsequently suppressed. (3) Deep-seated fires in charcoal beds are difficult to extinguish. (4) Automatic water sprays can be used to extinguish fires rapidly and reliably when properly introduced into the burning medium. The second part of the conclusions offered are more like challenges: (1) The problem associated with inadvertent actuations of fire protection systems is not a major one, and it can be reduced further by proper design review, installation, testing, and maintenance. Eliminating automatic fire extinguishing systems for the protection of charcoal adsorbers is not justified. (2) Removal of automatic fire protection systems due to fear of inadvertent fire protection system operation is a case of treating the effect rather than the cause. On the other hand, properly maintaining automatic fire protection systems will preserve the risk of fire loss at acceptable levels while at the same time reducing the risk of damage presented by inadvertent operation of fire protection systems

  2. Hypothetical neural mechanism that may play a role in mental rotation: an attractor neural network model.

    Science.gov (United States)

    Benusková, L; Estok, S

    1998-11-01

    We propose an attractor neural network (ANN) model that performs rotation-invariant pattern recognition in such a way that it can account for a neural mechanism being involved in the image transformation accompanying the experience of mental rotation. We compared the performance of our ANN model with the results of the chronometric psychophysical experiments of Cooper and Shepard (Cooper L A and Shepard R N 1973 Visual Information Processing (New York: Academic) pp 204-7) on discrimination of alphanumeric characters presented in various angular departures from their canonical upright position. Comparing the times required for pattern retrieval in its canonical upright position with the reaction times of human subjects, we found agreement in that (i) retrieval times for clockwise and anticlockwise departures of the same angular magnitude (up to 180 degrees) were not different, (ii) retrieval times increased with departure from upright and (iii) increased more sharply as departure from upright approached 180 degrees. The rotation-invariant retrieval of the activity pattern has been accomplished by means of the modified algorithm of Dotsenko (Dotsenko V S 1988 J. Phys. A: Math. Gen. 21 L783-7) proposed for translation-, rotation- and size-invariant pattern recognition, which uses relaxation of neuronal firing thresholds to guide the evolution of the ANN in state space towards the desired memory attractor. The dynamics of neuronal relaxation has been modified for storage and retrieval of low-activity patterns and the original gradient optimization of threshold dynamics has been replaced with optimization by simulated annealing.

  3. 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.

  4. Feedback enhances feedforward figure-ground segmentation by changing firing mode.

    Science.gov (United States)

    Supèr, Hans; Romeo, August

    2011-01-01

    In the visual cortex, feedback projections are conjectured to be crucial in figure-ground segregation. However, the precise function of feedback herein is unclear. Here we tested a hypothetical model of reentrant feedback. We used a previous developed 2-layered feedforward spiking network that is able to segregate figure from ground and included feedback connections. Our computer model data show that without feedback, neurons respond with regular low-frequency (∼9 Hz) bursting to a figure-ground stimulus. After including feedback the firing pattern changed into a regular (tonic) spiking pattern. In this state, we found an extra enhancement of figure responses and a further suppression of background responses resulting in a stronger figure-ground signal. Such push-pull effect was confirmed by comparing the figure-ground responses with the responses to a homogenous texture. We propose that feedback controls figure-ground segregation by influencing the neural firing patterns of feedforward projecting neurons.

  5. Feedback enhances feedforward figure-ground segmentation by changing firing mode.

    Directory of Open Access Journals (Sweden)

    Hans Supèr

    Full Text Available In the visual cortex, feedback projections are conjectured to be crucial in figure-ground segregation. However, the precise function of feedback herein is unclear. Here we tested a hypothetical model of reentrant feedback. We used a previous developed 2-layered feedforward spiking network that is able to segregate figure from ground and included feedback connections. Our computer model data show that without feedback, neurons respond with regular low-frequency (∼9 Hz bursting to a figure-ground stimulus. After including feedback the firing pattern changed into a regular (tonic spiking pattern. In this state, we found an extra enhancement of figure responses and a further suppression of background responses resulting in a stronger figure-ground signal. Such push-pull effect was confirmed by comparing the figure-ground responses with the responses to a homogenous texture. We propose that feedback controls figure-ground segregation by influencing the neural firing patterns of feedforward projecting neurons.

  6. Feedback Enhances Feedforward Figure-Ground Segmentation by Changing Firing Mode

    Science.gov (United States)

    Supèr, Hans; Romeo, August

    2011-01-01

    In the visual cortex, feedback projections are conjectured to be crucial in figure-ground segregation. However, the precise function of feedback herein is unclear. Here we tested a hypothetical model of reentrant feedback. We used a previous developed 2-layered feedforwardspiking network that is able to segregate figure from ground and included feedback connections. Our computer model data show that without feedback, neurons respond with regular low-frequency (∼9 Hz) bursting to a figure-ground stimulus. After including feedback the firing pattern changed into a regular (tonic) spiking pattern. In this state, we found an extra enhancement of figure responses and a further suppression of background responses resulting in a stronger figure-ground signal. Such push-pull effect was confirmed by comparing the figure-ground responses withthe responses to a homogenous texture. We propose that feedback controlsfigure-ground segregation by influencing the neural firing patterns of feedforward projecting neurons. PMID:21738747

  7. 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

  8. Neural dynamics in reconfigurable silicon.

    Science.gov (United States)

    Basu, A; Ramakrishnan, S; Petre, C; Koziol, S; Brink, S; Hasler, P E

    2010-10-01

    A neuromorphic analog chip is presented that is capable of implementing massively parallel neural computations while retaining the programmability of digital systems. We show measurements from neurons with Hopf bifurcations and integrate and fire neurons, excitatory and inhibitory synapses, passive dendrite cables, coupled spiking neurons, and central pattern generators implemented on the chip. This chip provides a platform for not only simulating detailed neuron dynamics but also uses the same to interface with actual cells in applications such as a dynamic clamp. There are 28 computational analog blocks (CAB), each consisting of ion channels with tunable parameters, synapses, winner-take-all elements, current sources, transconductance amplifiers, and capacitors. There are four other CABs which have programmable bias generators. The programmability is achieved using floating gate transistors with on-chip programming control. The switch matrix for interconnecting the components in CABs also consists of floating-gate transistors. Emphasis is placed on replicating the detailed dynamics of computational neural models. Massive computational area efficiency is obtained by using the reconfigurable interconnect as synaptic weights, resulting in more than 50 000 possible 9-b accurate synapses in 9 mm(2).

  9. 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...

  10. Daily Access to Sucrose Impairs Aspects of Spatial Memory Tasks Reliant on Pattern Separation and Neural Proliferation in Rats

    Science.gov (United States)

    Reichelt, Amy C.; Morris, Margaret J.; Westbrook, Reginald Frederick

    2016-01-01

    High sugar diets reduce hippocampal neurogenesis, which is required for minimizing interference between memories, a process that involves "pattern separation." We provided rats with 2 h daily access to a sucrose solution for 28 d and assessed their performance on a spatial memory task. Sucrose consuming rats discriminated between objects…

  11. 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

  12. Forest fires

    International Nuclear Information System (INIS)

    Fuller, M.

    1991-01-01

    This book examines the many complex and sensitive issues relating to wildland fires. Beginning with an overview of the fires of 1980s, the book discusses the implications of continued drought and considers the behavior of wildland fires, from ignition and spread to spotting and firestorms. Topics include the effects of weather, forest fuels, fire ecology, and the effects of fire on plants and animals. In addition, the book examines firefighting methods and equipment, including new minimum impact techniques and compressed air foam; prescribed burning; and steps that can be taken to protect individuals and human structures. A history of forest fire policies in the U.S. and a discussion of solutions to fire problems around the world completes the coverage. With one percent of the earth's surface burning every year in the last decade, this is a penetrating book on a subject of undeniable importance

  13. Pattern recognition based on time-frequency analysis and convolutional neural networks for vibrational events in φ-OTDR

    Science.gov (United States)

    Xu, Chengjin; Guan, Junjun; Bao, Ming; Lu, Jiangang; Ye, Wei

    2018-01-01

    Based on vibration signals detected by a phase-sensitive optical time-domain reflectometer distributed optical fiber sensing system, this paper presents an implement of time-frequency analysis and convolutional neural network (CNN), used to classify different types of vibrational events. First, spectral subtraction and the short-time Fourier transform are used to enhance time-frequency features of vibration signals and transform different types of vibration signals into spectrograms, which are input to the CNN for automatic feature extraction and classification. Finally, by replacing the soft-max layer in the CNN with a multiclass support vector machine, the performance of the classifier is enhanced. Experiments show that after using this method to process 4000 vibration signal samples generated by four different vibration events, namely, digging, walking, vehicles passing, and damaging, the recognition rates of vibration events are over 90%. The experimental results prove that this method can automatically make an effective feature selection and greatly improve the classification accuracy of vibrational events in distributed optical fiber sensing systems.

  14. 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.

  15. 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 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.

  16. Forest fires in Pennsylvania.

    Science.gov (United States)

    Donald A. Haines; William A. Main; Eugene F. McNamara

    1978-01-01

    Describes factors that contribute to forest fires in Pennsylvania. Includes an analysis of basic statistics; distribution of fires during normal, drought, and wet years; fire cause, fire activity by day-of-week; multiple-fire day; and fire climatology.

  17. 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

  18. Neural activation patterns and connectivity in visual attention during Number and Non-number processing: An ERP study using the Ishihara pseudoisochromatic plates.

    Science.gov (United States)

    Al-Marri, Faraj; Reza, Faruque; Begum, Tahamina; Hitam, Wan Hazabbah Wan; Jin, Goh Khean; Xiang, Jing

    2017-10-25

    Visual cognitive function is important to build up executive function in daily life. Perception of visual Number form (e.g., Arabic digit) and numerosity (magnitude of the Number) is of interest to cognitive neuroscientists. Neural correlates and the functional measurement of Number representations are complex occurrences when their semantic categories are assimilated with other concepts of shape and colour. Colour perception can be processed further to modulate visual cognition. The Ishihara pseudoisochromatic plates are one of the best and most common screening tools for basic red-green colour vision testing. However, there is a lack of study of visual cognitive function assessment using these pseudoisochromatic plates. We recruited 25 healthy normal trichromat volunteers and extended these studies using a 128-sensor net to record event-related EEG. Subjects were asked to respond by pressing Numbered buttons when they saw the Number and Non-number plates of the Ishihara colour vision test. Amplitudes and latencies of N100 and P300 event related potential (ERP) components were analysed from 19 electrode sites in the international 10-20 system. A brain topographic map, cortical activation patterns and Granger causation (effective connectivity) were analysed from 128 electrode sites. No major significant differences between N100 ERP components in either stimulus indicate early selective attention processing was similar for Number and Non-number plate stimuli, but Non-number plate stimuli evoked significantly higher amplitudes, longer latencies of the P300 ERP component with a slower reaction time compared to Number plate stimuli imply the allocation of attentional load was more in Non-number plate processing. A different pattern of asymmetric scalp voltage map was noticed for P300 components with a higher intensity in the left hemisphere for Number plate tasks and higher intensity in the right hemisphere for Non-number plate tasks. Asymmetric cortical activation

  19. 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

  20. Experimental evidence of a chaotic region in a neural pacemaker

    Energy Technology Data Exchange (ETDEWEB)

    Gu, Hua-Guang, E-mail: guhuaguang@tongji.edu.cn [School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092 (China); Department of Electronic Engineering, City University of Hong Kong, Hong Kong SAR (China); Jia, Bing [School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092 (China); Chen, Guan-Rong [Department of Electronic Engineering, City University of Hong Kong, Hong Kong SAR (China)

    2013-03-15

    In this Letter, we report the finding of period-adding scenarios with chaos in firing patterns, observed in biological experiments on a neural pacemaker, with fixed extra-cellular potassium concentration at different levels and taken extra-cellular calcium concentration as the bifurcation parameter. The experimental bifurcations in the two-dimensional parameter space demonstrate the existence of a chaotic region interwoven with the periodic region thereby forming a period-adding sequence with chaos. The behavior of the pacemaker in this region is qualitatively similar to that of the Hindmarsh–Rose neuron model in a well-known comb-shaped chaotic region in two-dimensional parameter spaces.

  1. 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

    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...... 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...

  2. Neural electrical activity and neural network growth.

    Science.gov (United States)

    Gafarov, F M

    2018-05-01

    The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

  3. Neural network based pattern matching and spike detection tools and services--in the CARMEN neuroinformatics project.

    Science.gov (United States)

    Fletcher, Martyn; Liang, Bojian; Smith, Leslie; Knowles, Alastair; Jackson, Tom; Jessop, Mark; Austin, Jim

    2008-10-01

    In the study of information flow in the nervous system, component processes can be investigated using a range of electrophysiological and imaging techniques. Although data is difficult and expensive to produce, it is rarely shared and collaboratively exploited. The Code Analysis, Repository and Modelling for e-Neuroscience (CARMEN) project addresses this challenge through the provision of a virtual neuroscience laboratory: an infrastructure for sharing data, tools and services. Central to the CARMEN concept are federated CARMEN nodes, which provide: data and metadata storage, new, thirdparty and legacy services, and tools. In this paper, we describe the CARMEN project as well as the node infrastructure and an associated thick client tool for pattern visualisation and searching, the Signal Data Explorer (SDE). We also discuss new spike detection methods, which are central to the services provided by CARMEN. The SDE is a client application which can be used to explore data in the CARMEN repository, providing data visualization, signal processing and a pattern matching capability. It performs extremely fast pattern matching and can be used to search for complex conditions composed of many different patterns across the large datasets that are typical in neuroinformatics. Searches can also be constrained by specifying text based metadata filters. Spike detection services which use wavelet and morphology techniques are discussed, and have been shown to outperform traditional thresholding and template based systems. A number of different spike detection and sorting techniques will be deployed as services within the CARMEN infrastructure, to allow users to benchmark their performance against a wide range of reference datasets.

  4. 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...

  5. Neural activation patterns during retrieval of schema-related memories: differences and commonalities between children and adults.

    Science.gov (United States)

    Brod, Garvin; Lindenberger, Ulman; Shing, Yee Lee

    2017-11-01

    Schemas represent stable properties of individuals' experiences, and allow them to classify new events as being congruent or incongruent with existing knowledge. Research with adults indicates that the prefrontal cortex (PFC) is involved in memory retrieval of schema-related information. However, developmental differences between children and adults in the neural correlates of schema-related memories are not well understood. One reason for this is the inherent confound between schema-relevant experience and maturation, as both are related to time. To overcome this limitation, we used a novel paradigm that experimentally induces, and then probes for, task-relevant knowledge during encoding of new information. Thirty-one children aged 8-12 years and 26 young adults participated in the experiment. While successfully retrieving schema-congruent events, children showed less medial PFC activity than adults. In addition, medial PFC activity during successful retrieval correlated positively with children's age. While successfully retrieving schema-incongruent events, children showed stronger hippocampus (HC) activation as well as weaker connectivity between the striatum and the dorsolateral PFC than adults. These findings were corroborated by an exploratory full-factorial analysis investigating age differences in the retrieval of schema-congruent versus schema-incongruent events, comparing the two conditions directly. Consistent with the findings of the separate analyses, two clusters, one in the medial PFC, one in the HC, were identified that exhibited a memory × congruency × age group interaction. In line with the two-component model of episodic memory development, the present findings point to an age-related shift from a more HC-bound processing to an increasing recruitment of prefrontal brain regions in the retrieval of schema-related events. © 2016 John Wiley & Sons Ltd.

  6. 46 CFR 28.820 - 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... REQUIREMENTS FOR COMMERCIAL FISHING INDUSTRY VESSELS Aleutian Trade Act Vessels § 28.820 Fire pumps, fire mains, fire hydrants, and fire hoses. (a) Each vessel must be equipped with a self-priming, power driven fire...

  7. 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...

  8. A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system

    Science.gov (United States)

    Kaplan, Bernhard A.; Lansner, Anders

    2014-01-01

    Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin–Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian–Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian–Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures. PMID

  9. 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.

  10. Neural Networks for the Beginner.

    Science.gov (United States)

    Snyder, Robin M.

    Motivated by the brain, neural networks are a right-brained approach to artificial intelligence that is used to recognize patterns based on previous training. In practice, one would not program an expert system to recognize a pattern and one would not train a neural network to make decisions from rules; but one could combine the best features of…

  11. 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.

  12. SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

    Science.gov (United States)

    Zenke, Friedemann; Ganguli, Surya

    2018-04-13

    A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

  13. Operating room fires: a closed claims analysis.

    Science.gov (United States)

    Mehta, Sonya P; Bhananker, Sanjay M; Posner, Karen L; Domino, Karen B

    2013-05-01

    To assess patterns of injury and liability associated with operating room (OR) fires, closed malpractice claims in the American Society of Anesthesiologists Closed Claims Database since 1985 were reviewed. All claims related to fires in the OR were compared with nonfire-related surgical anesthesia claims. An analysis of fire-related claims was performed to identify causative factors. There were 103 OR fire claims (1.9% of 5,297 surgical claims). Electrocautery was the ignition source in 90% of fire claims. OR fire claims more frequently involved older outpatients compared with other surgical anesthesia claims (P fire claims (P fires (n = 93) increased over time (P fires occurred during head, neck, or upper chest procedures (high-fire-risk procedures). Oxygen served as the oxidizer in 95% of electrocautery-induced OR fires (84% with open delivery system). Most electrocautery-induced fires (n = 75, 81%) occurred during monitored anesthesia care. Oxygen was administered via an open delivery system in all high-risk procedures during monitored anesthesia care. In contrast, alcohol-containing prep solutions and volatile compounds were present in only 15% of OR fires during monitored anesthesia care. Electrocautery-induced fires during monitored anesthesia care were the most common cause of OR fires claims. Recognition of the fire triad (oxidizer, fuel, and ignition source), particularly the critical role of supplemental oxygen by an open delivery system during use of the electrocautery, is crucial to prevent OR fires. Continuing education and communication among OR personnel along with fire prevention protocols in high-fire-risk procedures may reduce the occurrence of OR fires.

  14. Angora Fire, Lake Tahoe

    Science.gov (United States)

    2007-01-01

    On the weekend of June 23, 2007, a wildfire broke out south of Lake Tahoe, which stretches across the California-Nevada border. By June 28, the Angora Fire had burned more than 200 homes and forced some 2,000 residents to evacuate, according to The Seattle Times and the Central Valley Business Times. On June 27, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on NASA's Terra satellite captured this image of the burn scar left by the Angora fire. The burn scar is dark gray, or charcoal. Water bodies, including the southern tip of Lake Tahoe and Fallen Leaf Lake, are pale silvery blue, the silver color a result of sunlight reflecting off the surface of the water. Vegetation ranges in color from dark to bright green. Streets are light gray, and the customary pattern of meandering residential streets and cul-de-sacs appears throughout the image, including the area that burned. The burn scar shows where the fire obliterated some of the residential areas just east of Fallen Leaf Lake. According to news reports, the U.S. Forest Service had expressed optimism about containing the fire within a week of the outbreak, but a few days after the fire started, it jumped a defense, forcing the evacuation of hundreds more residents. Strong winds that had been forecast for June 27, however, did not materialize, allowing firefighters to regain ground in controlling the blaze. On June 27, authorities hoped that the fire would be completely contained by July 3. According to estimates provided in the daily report from the National Interagency Fire Center, the fire had burned 3,100 acres (about 12.5 square kilometers) and was about 55 percent contained as of June 28. Some mandatory evacuations remained in effect. NASA image by Jesse Allen, using data provided courtesy of the NASA/GSFC/MITI/ERSDAC/JAROS, and U.S./Japan ASTER Science Team.

  15. Delay-induced diversity of firing behavior and ordered chaotic firing in adaptive neuronal networks

    International Nuclear Information System (INIS)

    Gong Yubing; Wang Li; Xu Bo

    2012-01-01

    In this paper, we study the effect of time delay on the firing behavior and temporal coherence and synchronization in Newman–Watts thermosensitive neuron networks with adaptive coupling. At beginning, the firing exhibit disordered spiking in absence of time delay. As time delay is increased, the neurons exhibit diversity of firing behaviors including bursting with multiple spikes in a burst, spiking, bursting with four, three and two spikes, firing death, and bursting with increasing amplitude. The spiking is the most ordered, exhibiting coherence resonance (CR)-like behavior, and the firing synchronization becomes enhanced with the increase of time delay. As growth rate of coupling strength or network randomness increases, CR-like behavior shifts to smaller time delay and the synchronization of firing increases. These results show that time delay can induce diversity of firing behaviors in adaptive neuronal networks, and can order the chaotic firing by enhancing and optimizing the temporal coherence and enhancing the synchronization of firing. However, the phenomenon of firing death shows that time delay may inhibit the firing of adaptive neuronal networks. These findings provide new insight into the role of time delay in the firing activity of adaptive neuronal networks, and can help to better understand the complex firing phenomena in neural networks.

  16. 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.

  17. Classifying and comparing spatial models of fire dynamics

    Science.gov (United States)

    Geoffrey J. Cary; Robert E. Keane; Mike D. Flannigan

    2007-01-01

    Wildland fire is a significant disturbance in many ecosystems worldwide and the interaction of fire with climate and vegetation over long time spans has major effects on vegetation dynamics, ecosystem carbon budgets, and patterns of biodiversity. Landscape-Fire-Succession Models (LFSMs) that simulate the linked processes of fire and vegetation development in a spatial...

  18. 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.

  19. 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...

  20. 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

  1. 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...

  2. 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…

  3. 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...

  4. 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...

  5. Bistable firing properties of soleus motor units in unrestrained rats

    DEFF Research Database (Denmark)

    EKEN, T.; KIEHN, O.

    1989-01-01

    of the motoneuron pool by stimulation of la afferents, or inhibition by stimulation of skin afferents. The shifts were not related to gross limb movements. This phenomenon is referred to as a bistable firing pattern. Bistable firing also occurred spontaneously during quiet standing. Typically the firing frequency...... was unchanged or only phasically influenced. These results demonstrate for the first time a bistable firing pattern during postural activity in the intact animal. The firing pattern closely resembles the bistable behaviour described in spinal motoneurons in reduced preparations, where it is due to the presence...... of a plateau potential. This suggests that the bistable firing is unexplained by plateau potentials also in the intact animal....

  6. 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.

  7. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

    International Nuclear Information System (INIS)

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

    2015-01-01

    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

  8. The largest forest fires in Portugal: the constraints of burned area size on the comprehension of fire severity.

    Science.gov (United States)

    Tedim, Fantina; Remelgado, Ruben; Martins, João; Carvalho, Salete

    2015-01-01

    Portugal is a European country with highest forest fires density and burned area. Since beginning of official forest fires database in 1980, an increase in number of fires and burned area as well as appearance of large and catastrophic fires have characterized fire activity in Portugal. In 1980s, the largest fires were just a little bit over 10,000 ha. However, in the beginning of 21st century several fires occurred with a burned area over 20,000 ha. Some of these events can be classified as mega-fires due to their ecological and socioeconomic severity. The present study aimed to discuss the characterization of large forest fires trend, in order to understand if the largest fires that occurred in Portugal were exceptional events or evidences of a new trend, and the constraints of fire size to characterize fire effects because, usually, it is assumed that larger the fire higher the damages. Using Portuguese forest fire database and satellite imagery, the present study showed that the largest fires could be seen at the same time as exceptional events and as evidence of a new fire regime. It highlighted the importance of size and patterns of unburned patches within fire perimeter as well as heterogeneity of fire ecological severity, usually not included in fire regime description, which are critical to fire management and research. The findings of this research can be used in forest risk reduction and suppression planning.

  9. 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...

  10. 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

  11. 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

  12. 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

  13. 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...

  14. Chaos in integrate-and-fire dynamical systems

    International Nuclear Information System (INIS)

    Coombes, S.

    2000-01-01

    Integrate-and-fire (IF) mechanisms are often studied within the context of neural dynamics. From a mathematical perspective they represent a minimal yet biologically realistic model of a spiking neuron. The non-smooth nature of the dynamics leads to extremely rich spike train behavior capable of explaining a variety of biological phenomenon including phase-locked states, mode-locking, bursting and pattern formation. The conditions under which chaotic spike trains may be generated in synaptically interacting networks of neural oscillators is an important open question. Using techniques originally introduced for the study of impact oscillators we develop the notion of a Liapunov exponent for IF systems. In the strong coupling regime a network may undergo a discrete Turing-Hopf bifurcation of the firing times from a synchronous state to a state with periodic or quasiperiodic variations of the interspike intervals on closed orbits. Away from the bifurcation point these invariant circles may break up. We establish numerically that in this case the largest IF Liapunov exponent becomes positive. Hence, one route to chaos in networks of synaptically coupled IF neurons is via the breakup of invariant circles

  15. Excitation of lateral habenula neurons as a neural mechanism underlying ethanol‐induced conditioned taste aversion

    Science.gov (United States)

    Keefe, Kristen A.; Taha, Sharif A.

    2016-01-01

    Key points The lateral habenula (LHb) has been implicated in regulation of drug‐seeking behaviours through aversion‐mediated learning.In this study, we recorded neuronal activity in the LHb of rats during an operant task before and after ethanol‐induced conditioned taste aversion (CTA) to saccharin.Ethanol‐induced CTA caused significantly higher baseline firing rates in LHb neurons, as well as elevated firing rates in response to cue presentation, lever press and saccharin taste.In a separate cohort of rats, we found that bilateral LHb lesions blocked ethanol‐induced CTA.Our results strongly suggest that excitation of LHb neurons is required for ethanol‐induced CTA, and point towards a mechanism through which LHb firing may regulate voluntary ethanol consumption. Abstract Ethanol, like other drugs of abuse, has both rewarding and aversive properties. Previous work suggests that sensitivity to ethanol's aversive effects negatively modulates voluntary alcohol intake and thus may be important in vulnerability to developing alcohol use disorders. We previously found that rats with lesions of the lateral habenula (LHb), which is implicated in aversion‐mediated learning, show accelerated escalation of voluntary ethanol consumption. To understand neural encoding in the LHb contributing to ethanol‐induced aversion, we recorded neural firing in the LHb of freely behaving, water‐deprived rats before and after an ethanol‐induced (1.5 g kg−1 20% ethanol, i.p.) conditioned taste aversion (CTA) to saccharin taste. Ethanol‐induced CTA strongly decreased motivation for saccharin in an operant task to obtain the tastant. Comparison of LHb neural firing before and after CTA induction revealed four main differences in firing properties. First, baseline firing after CTA induction was significantly higher. Second, firing evoked by cues signalling saccharin availability shifted from a pattern of primarily inhibition before CTA to primarily excitation after CTA

  16. Excitation of lateral habenula neurons as a neural mechanism underlying ethanol-induced conditioned taste aversion.

    Science.gov (United States)

    Tandon, Shashank; Keefe, Kristen A; Taha, Sharif A

    2017-02-15

    The lateral habenula (LHb) has been implicated in regulation of drug-seeking behaviours through aversion-mediated learning. In this study, we recorded neuronal activity in the LHb of rats during an operant task before and after ethanol-induced conditioned taste aversion (CTA) to saccharin. Ethanol-induced CTA caused significantly higher baseline firing rates in LHb neurons, as well as elevated firing rates in response to cue presentation, lever press and saccharin taste. In a separate cohort of rats, we found that bilateral LHb lesions blocked ethanol-induced CTA. Our results strongly suggest that excitation of LHb neurons is required for ethanol-induced CTA, and point towards a mechanism through which LHb firing may regulate voluntary ethanol consumption. Ethanol, like other drugs of abuse, has both rewarding and aversive properties. Previous work suggests that sensitivity to ethanol's aversive effects negatively modulates voluntary alcohol intake and thus may be important in vulnerability to developing alcohol use disorders. We previously found that rats with lesions of the lateral habenula (LHb), which is implicated in aversion-mediated learning, show accelerated escalation of voluntary ethanol consumption. To understand neural encoding in the LHb contributing to ethanol-induced aversion, we recorded neural firing in the LHb of freely behaving, water-deprived rats before and after an ethanol-induced (1.5 g kg -1 20% ethanol, i.p.) conditioned taste aversion (CTA) to saccharin taste. Ethanol-induced CTA strongly decreased motivation for saccharin in an operant task to obtain the tastant. Comparison of LHb neural firing before and after CTA induction revealed four main differences in firing properties. First, baseline firing after CTA induction was significantly higher. Second, firing evoked by cues signalling saccharin availability shifted from a pattern of primarily inhibition before CTA to primarily excitation after CTA induction. Third, CTA induction reduced

  17. Fire safety

    International Nuclear Information System (INIS)

    Keski-Rahkonen, O.; Bjoerkman, J.; Hostikka, S.; Mangs, J.; Huhtanen, R.; Palmen, H.; Salminen, A.; Turtola, A.

    1998-01-01

    According to experience and probabilistic risk assessments, fires present a significant hazard in a nuclear power plant. Fires may be initial events for accidents or affect safety systems planned to prevent accidents and to mitigate their consequences. The project consists of theoretical work, experiments and simulations aiming to increase the fire safety at nuclear power plants. The project has four target areas: (1) to produce validated models for numerical simulation programmes, (2) to produce new information on the behavior of equipment in case of fire, (3) to study applicability of new active fire protecting systems in nuclear power plants, and (4) to obtain quantitative knowledge of ignitions induced by important electric devices in nuclear power plants. These topics have been solved mainly experimentally, but modelling at different level is used to interpret experimental data, and to allow easy generalisation and engineering use of the obtained data. Numerical fire simulation has concentrated in comparison of CFD modelling of room fires, and fire spreading on cables on experimental data. So far the success has been good to fair. A simple analytical and numerical model has been developed for fire effluents spreading beyond the room of origin in mechanically strongly ventilated compartments. For behaviour of equipment in fire several full scale and scaled down calorimetric experiments were carried out on electronic cabinets, as well as on horizontal and vertical cable trays. These were carried out to supply material for CFD numerical simulation code validation. Several analytical models were developed and validated against obtained experimental results to allow quick calculations for PSA estimates as well as inter- and extrapolations to slightly different objects. Response times of different commercial fire detectors were determined for different types of smoke, especially emanating from smoldering and flaming cables to facilitate selection of proper detector

  18. 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.

  19. Windscale fire

    International Nuclear Information System (INIS)

    Auxier, J.A.

    1986-01-01

    A graphite fire in the Windscale No. 1 reactor occurred during the period October 8-12, 1957. The Windscale reactors were located on a coastal plain in northwest England and were used to produce plutonium. A great wealth of information was gathered on the causes, handling, decontamination, and environmental effects of reactor accidents. Topics of discussion include: the cause of the fire; handling of the incident; radiation doses to the population; and radiation effects on the population

  20. Trends and causes of severity, size, and number of fires in northwestern California, USA

    Science.gov (United States)

    J. D. Miller; Carl Skinner; H. D. Safford; Eric E. Knapp; C. M. Ramirez

    2012-01-01

    Research in the last several years has indicated that fire size and frequency are on the rise in western U.S. forests. Although fire size and frequency are important, they do not necessarily scale with ecosystem effects of fire, as different ecosystems have different ecological and evolutionary relationships with fire. Our study assessed trends and patterns in fire...

  1. 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...

  2. An assessment of fire occurrence regime and performance of Canadian fire weather index in south central Siberian boreal region

    OpenAIRE

    Chu, T.; Guo, X.

    2014-01-01

    Wildfire is the dominant natural disturbance in Eurasian boreal region, which acts as a major driver of the global carbon cycle. An effectiveness of wildfire management requires suitable tools for fire prevention and fire risk assessment. This study aims to investigate fire occurrence patterns in relation to fire weather conditions in the remote south central Siberia region. The Canadian Fire Weather Index derived from large-scale meteorol...

  3. 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.

  4. 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.

  5. Self-sustained firing activities of the cortical network with plastic rules in weak AC electrical fields

    International Nuclear Information System (INIS)

    Qin Ying-Mei; Wang Jiang; Men Cong; Zhao Jia; Wei Xi-Le; Deng Bin

    2012-01-01

    Both external and endogenous electrical fields widely exist in the environment of cortical neurons. The effects of a weak alternating current (AC) field on a neural network model with synaptic plasticity are studied. It is found that self-sustained rhythmic firing patterns, which are closely correlated with the cognitive functions, are significantly modified due to the self-organizing of the network in the weak AC field. The activities of the neural networks are affected by the synaptic connection strength, the external stimuli, and so on. In the presence of learning rules, the synaptic connections can be modulated by the external stimuli, which will further enhance the sensitivity of the network to the external signal. The properties of the external AC stimuli can serve as control parameters in modulating the evolution of the neural network. (interdisciplinary physics and related areas of science and technology)

  6. Establishing a Statistical Link between Network Oscillations and Neural Synchrony.

    Directory of Open Access Journals (Sweden)

    Pengcheng Zhou

    2015-10-01

    Full Text Available Pairs of active neurons frequently fire action potentials or "spikes" nearly synchronously (i.e., within 5 ms of each other. This spike synchrony may occur by chance, based solely on the neurons' fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spike synchrony above chances levels is present, it may subserve computation for a specific cognitive process, or it could be an irrelevant byproduct of such computation. Either way, spike synchrony is a feature of neural data that should be explained. A point process regression framework has been developed previously for this purpose, using generalized linear models (GLMs. In this framework, the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron's firing-rate functions. An important possible source of spike synchrony is network-wide oscillations, which may provide an essential mechanism of network information flow. To establish the statistical link between spike synchrony and network-wide oscillations, we have integrated oscillatory field potentials into our point process regression framework. We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates. We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1 simulated neurons, 2 in vitro recordings of hippocampal CA1 pyramidal cells, and 3 in vivo recordings of neocortical V4 neurons. Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony.

  7. Wetland fire remote sensing research--The Greater Everglades example

    Science.gov (United States)

    Jones, John W.

    2012-01-01

    Fire is a major factor in the Everglades ecosystem. For thousands of years, lightning-strike fires from summer thunderstorms have helped create and maintain a dynamic landscape suited both to withstand fire and recover quickly in the wake of frequent fires. Today, managers in the Everglades National Park are implementing controlled burns to promote healthy, sustainable vegetation patterns and ecosystem functions. The U.S. Geological Survey (USGS) is using remote sensing to improve fire-management databases in the Everglades, gain insights into post-fire land-cover dynamics, and develop spatially and temporally explicit fire-scar data for habitat and hydrologic modeling.

  8. Reliability of neural encoding

    DEFF Research Database (Denmark)

    Alstrøm, Preben; Beierholm, Ulrik; Nielsen, Carsten Dahl

    2002-01-01

    The reliability with which a neuron is able to create the same firing pattern when presented with the same stimulus is of critical importance to the understanding of neuronal information processing. We show that reliability is closely related to the process of phaselocking. Experimental results f...

  9. Characterizing neural activities evoked by manual acupuncture through spiking irregularity measures

    International Nuclear Information System (INIS)

    Xue Ming; Wang Jiang; Deng Bin; Wei Xi-Le; Yu Hai-Tao; Chen Ying-Yuan

    2013-01-01

    The neural system characterizes information in external stimulations by different spiking patterns. In order to examine how neural spiking patterns are related to acupuncture manipulations, experiments are designed in such a way that different types of manual acupuncture (MA) manipulations are taken at the ‘Zusanli’ point of experimental rats, and the induced electrical signals in the spinal dorsal root ganglion are detected and recorded. The interspike interval (ISI) statistical histogram is fitted by the gamma distribution, which has two parameters: one is the time-dependent firing rate and the other is a shape parameter characterizing the spiking irregularities. The shape parameter is the measure of spiking irregularities and can be used to identify the type of MA manipulations. The coefficient of variation is mostly used to measure the spike time irregularity, but it overestimates the irregularity in the case of pronounced firing rate changes. However, experiments show that each acupuncture manipulation will lead to changes in the firing rate. So we combine four relatively rate-independent measures to study the irregularity of spike trains evoked by different types of MA manipulations. Results suggest that the MA manipulations possess unique spiking statistics and characteristics and can be distinguished according to the spiking irregularity measures. These studies have offered new insights into the coding processes and information transfer of acupuncture. (interdisciplinary physics and related areas of science and technology)

  10. Characterization of the Fire Regime and Drivers of Fires in the West African Tropical Forest

    Science.gov (United States)

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

    2016-12-01

    The Upper Guinean forest (UGF), encompassing the tropical regions of West Africa, is a globally significant biodiversity hotspot and a critically important socio-economic and ecological resource for the region. However, the UGF is one of the most human-disturbed tropical forest ecosystems with the only remaining large patches of original forests distributed in protected areas, which are embedded in a hotspot of climate stress & land use pressures, increasing their vulnerability to fire. We hypothesized that human impacts and climate interact to drive spatial and temporal variability in fire, with fire exhibiting distinctive seasonality and sensitivity to drought in areas characterized by different population densities, agricultural practices, vegetation types, and levels of forest degradation. We used the MODIS active fire product to identify and characterize fire activity in the major ecoregions of the UGF. We used TRMM rainfall data to measure climatic variability and derived indicators of human land use from a variety of geospatial datasets. We employed time series modeling to identify the influences of drought indices and other antecedent climatic indicators on temporal patterns of active fire occurrence. We used a variety of modeling approaches to assess the influences of human activities and land cover variables on the spatial pattern of fire activity. Our results showed that temporal patterns of fire activity in the UGF were related to precipitation, but these relationships were spatially heterogeneous. The pattern of fire seasonality varied geographically, reflecting both climatological patterns and agricultural practices. The spatial pattern of fire activity was strongly associated with vegetation gradients and anthropogenic activities occurring at fine spatial scales. The Guinean forest-savanna mosaic ecoregion had the most fires. This study contributes to our understanding of UGF fire regime and the spatio-temporal dynamics of tropical forest fires in

  11. 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.

  12. Sequence memory based on coherent spin-interaction neural networks.

    Science.gov (United States)

    Xia, Min; Wong, W K; Wang, Zhijie

    2014-12-01

    Sequence information processing, for instance, the sequence memory, plays an important role on many functions of brain. In the workings of the human brain, the steady-state period is alterable. However, in the existing sequence memory models using heteroassociations, the steady-state period cannot be changed in the sequence recall. In this work, a novel neural network model for sequence memory with controllable steady-state period based on coherent spininteraction is proposed. In the proposed model, neurons fire collectively in a phase-coherent manner, which lets a neuron group respond differently to different patterns and also lets different neuron groups respond differently to one pattern. The simulation results demonstrating the performance of the sequence memory are presented. By introducing a new coherent spin-interaction sequence memory model, the steady-state period can be controlled by dimension parameters and the overlap between the input pattern and the stored patterns. The sequence storage capacity is enlarged by coherent spin interaction compared with the existing sequence memory models. Furthermore, the sequence storage capacity has an exponential relationship to the dimension of the neural network.

  13. The gamma model : a new neural network for temporal processing

    NARCIS (Netherlands)

    Vries, de B.

    1992-01-01

    In this paper we develop the gamma neural model, a new neural net architecture for processing of temporal patterns. Time varying patterns are normally segmented into a sequence of static patterns that are successively presented to a neural net. In the approach presented here segmentation is avoided.

  14. Connectivities and synchronous firing in cortical neuronal networks

    International Nuclear Information System (INIS)

    Jia, L.C.; Sano, M.; Lai, P.-Y.; Chan, C.K.

    2004-01-01

    Network connectivities (k-bar) of cortical neural cultures are studied by synchronized firing and determined from measured correlations between fluorescence intensities of firing neurons. The bursting frequency (f) during synchronized firing of the networks is found to be an increasing function of k-bar. With f taken to be proportional to k-bar, a simple random model with a k-bar dependent connection probability p(k-bar) has been constructed to explain our experimental findings successfully

  15. A memristive spiking neuron with firing rate coding

    Directory of Open Access Journals (Sweden)

    Marina eIgnatov

    2015-10-01

    Full Text Available Perception, decisions, and sensations are all encoded into trains of action potentials in the brain. The relation between stimulus strength and all-or-nothing spiking of neurons is widely believed to be the basis of this coding. This initiated the development of spiking neuron models; one of today's most powerful conceptual tool for the analysis and emulation of neural dynamics. The success of electronic circuit models and their physical realization within silicon field-effect transistor circuits lead to elegant technical approaches. Recently, the spectrum of electronic devices for neural computing has been extended by memristive devices, mainly used to emulate static synaptic functionality. Their capabilities for emulations of neural activity were recently demonstrated using a memristive neuristor circuit, while a memristive neuron circuit has so far been elusive. Here, a spiking neuron model is experimentally realized in a compact circuit comprising memristive and memcapacitive devices based on the strongly correlated electron material vanadium dioxide (VO2 and on the chemical electromigration cell Ag/TiO2-x/Al. The circuit can emulate dynamical spiking patterns in response to an external stimulus including adaptation, which is at the heart of firing rate coding as first observed by E.D. Adrian in 1926.

  16. Soil shapes community structure through fire.

    Science.gov (United States)

    Ojeda, Fernando; Pausas, Juli G; Verdú, Miguel

    2010-07-01

    Recurrent wildfires constitute a major selecting force in shaping the structure of plant communities. At the regional scale, fire favours phenotypic and phylogenetic clustering in Mediterranean woody plant communities. Nevertheless, the incidence of fire within a fire-prone region may present strong variations at the local, landscape scale. This study tests the prediction that woody communities on acid, nutrient-poor soils should exhibit more pronounced phenotypic and phylogenetic clustering patterns than woody communities on fertile soils, as a consequence of their higher flammability and, hence, presumably higher propensity to recurrent fire. Results confirm the predictions and show that habitat filtering driven by fire may be detected even in local communities from an already fire-filtered regional flora. They also provide a new perspective from which to consider a preponderant role of fire as a key evolutionary force in acid, infertile Mediterranean heathlands.

  17. 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 ...

  18. Fire Safety (For Parents)

    Science.gov (United States)

    ... Staying Safe Videos for Educators Search English Español Fire Safety KidsHealth / For Parents / Fire Safety What's in ... event of a fire emergency in your home. Fire Prevention Of course, the best way to practice ...

  19. 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...

  20. 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

  1. Fire regime characterization in Mediterranean ecosystems of Southern Italy

    Science.gov (United States)

    Lanorte, A.; Lasaponara, R.

    2009-04-01

    This paper addresses the wildfire regime in Mediterranean ecosystems of Southern Italy. Fire regimes refer to average fire conditions (including fire size, fire density, fire frequency, fire seasonality, fire intensity, fire severity, fire thresholds, etc.) occurring over a long period of time. Information on spatial pattern of forest fire locations is a key point in the study of the dynamics of fire disturbance, and allows us to improve the knowledge of past and current role of fire. Historical evidence clearly shows what did happen and this can fruitfully help to understand what is happening and what could happen in the next future. Mapping fire regimes is very challenging, because fire ocurrence features are the expression of the interactions between climate, fire, vegetation, topography, social factors. The main objective of this work is to provide a comprehensive characterization of the fire regime in Italy based on a recently updated national wildfire database. Fire data were obtained from the Italian National Forestry Service. This national database is comprised of information contained in individual fire reports completed for every fire that occurs on public lands in the Italian peninsula. Complete data were only available for 1996-2006 at the time we accessed the database, which determined the years we analysed. The primary fire history variables that we reported were number of fires, area burned, burning time and duration, and fire size (average size of individual fires) The wildfire records (wildfire area, location, time, vegetation) were analysed with other environmental (fuel availability and type), topographic features, and meteorological/climatological data. Results of our analysis could help better understand the different factors on the wildfire regime in Mediterranean ecosystems of Southern Italy.

  2. Identifying the location of fire refuges in wet forest ecosystems.

    Science.gov (United States)

    Berry, Laurence E; Driscoll, Don A; Stein, John A; Blanchard, Wade; Banks, Sam C; Bradstock, Ross A; Lindenmayer, David B

    2015-12-01

    The increasing frequency of large, high-severity fires threatens the survival of old-growth specialist fauna in fire-prone forests. Within topographically diverse montane forests, areas that experience less severe or fewer fires compared with those prevailing in the landscape may present unique resource opportunities enabling old-growth specialist fauna to survive. Statistical landscape models that identify the extent and distribution of potential fire refuges may assist land managers to incorporate these areas into relevant biodiversity conservation strategies. We used a case study in an Australian wet montane forest to establish how predictive fire simulation models can be interpreted as management tools to identify potential fire refuges. We examined the relationship between the probability of fire refuge occurrence as predicted by an existing fire refuge model and fire severity experienced during a large wildfire. We also examined the extent to which local fire severity was influenced by fire severity in the surrounding landscape. We used a combination of statistical approaches, including generalized linear modeling, variogram analysis, and receiver operating characteristics and area under the curve analysis (ROC AUC). We found that the amount of unburned habitat and the factors influencing the retention and location of fire refuges varied with fire conditions. Under extreme fire conditions, the distribution of fire refuges was limited to only extremely sheltered, fire-resistant regions of the landscape. During extreme fire conditions, fire severity patterns were largely determined by stochastic factors that could not be predicted by the model. When fire conditions were moderate, physical landscape properties appeared to mediate fire severity distribution. Our study demonstrates that land managers can employ predictive landscape fire models to identify the broader climatic and spatial domain within which fire refuges are likely to be present. It is essential

  3. Effects of fire on major forest ecosystem processes: an overview.

    Science.gov (United States)

    Chen, Zhong

    2006-09-01

    Fire and fire ecology are among the best-studied topics in contemporary ecosystem ecology. The large body of existing literature on fire and fire ecology indicates an urgent need to synthesize the information on the pattern of fire effects on ecosystem composition, structure, and functions for application in fire and ecosystem management. Understanding fire effects and underlying principles are critical to reduce the risk of uncharacteristic wildfires and for proper use of fire as an effective management tool toward management goals. This overview is a synthesis of current knowledge on major effects of fire on fire-prone ecosystems, particularly those in the boreal and temperate regions of the North America. Four closely related ecosystem processes in vegetation dynamics, nutrient cycling, soil and belowground process and water relations were discussed with emphases on fire as the driving force. Clearly, fire can shape ecosystem composition, structure and functions by selecting fire adapted species and removing other susceptible species, releasing nutrients from the biomass and improving nutrient cycling, affecting soil properties through changing soil microbial activities and water relations, and creating heterogeneous mosaics, which in turn, can further influence fire behavior and ecological processes. Fire as a destructive force can rapidly consume large amount of biomass and cause negative impacts such as post-fire soil erosion and water runoff, and air pollution; however, as a constructive force fire is also responsible for maintaining the health and perpetuity of certain fire-dependent ecosystems. Considering the unique ecological roles of fire in mediating and regulating ecosystems, fire should be incorporated as an integral component of ecosystems and management. However, the effects of fire on an ecosystem depend on the fire regime, vegetation type, climate, physical environments, and the scale of time and space of assessment. More ecosystem

  4. Dynamical systems, attractors, and neural circuits.

    Science.gov (United States)

    Miller, Paul

    2016-01-01

    Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic-they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

  5. 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

  6. Influences of climate, fire, and topography on contemporary age structure patterns of Douglas-fir at 205 old forest sites in western Oregon

    Science.gov (United States)

    Nathan J. Poage; Peter J. Weisberg; Peter C. Impara; John C. Tappeiner; Thomas S. Sensenig

    2009-01-01

    Knowledge of forest development is basic to understanding the ecology, dynamics, and management of forest ecosystems. We hypothesized that the age structure patterns of Douglas-fir at 205 old forest sites in western Oregon are extremely variable with long and (or) multiple establishment periods common, and that these patterns reflect variation in regional-scale climate...

  7. Fire Models and Design Fires

    DEFF Research Database (Denmark)

    Poulsen, Annemarie

    The aim of this project is to perform an experimental study on the influence of the thermal feedback on the burning behavior of well ventilated pre-flashover fires. For the purpose an experimental method has been developed. Here the same identical objects are tested under free burn conditions...... carried out by Carleton University and NRC-IRC performed on seven different types of fire loads representing commercial premises, comprise the tests used for the study. The results show that for some of the room test the heat release rate increased due to thermal feedback compared to free burn for a pre......-flashover fire. Two phenomena were observed, that relate well to theory was found. In an incipient phase the heat release rate rose with the temperature of the smoke layer/enclosure boundaries. This increase was also found to depend on the flammability properties of the burning object. The results also...

  8. Rich spectrum of neural field dynamics in the presence of short-term synaptic depression

    Science.gov (United States)

    Wang, He; Lam, Kin; Fung, C. C. Alan; Wong, K. Y. Michael; Wu, Si

    2015-09-01

    In continuous attractor neural networks (CANNs), spatially continuous information such as orientation, head direction, and spatial location is represented by Gaussian-like tuning curves that can be displaced continuously in the space of the preferred stimuli of the neurons. We investigate how short-term synaptic depression (STD) can reshape the intrinsic dynamics of the CANN model and its responses to a single static input. In particular, CANNs with STD can support various complex firing patterns and chaotic behaviors. These chaotic behaviors have the potential to encode various stimuli in the neuronal system.

  9. 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.

  10. 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)

  11. 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...

  12. 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...

  13. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

    The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

  14. Mercury and other trace elements in Ohio River fish collected near coal-fired power plants: Interspecific patterns and consideration of consumption risks.

    Science.gov (United States)

    Reash, Robin J; Brown, Lauren; Merritt, Karen

    2015-07-01

    Many coal-fired electric generating facilities in the United States are discharging higher loads of Hg, Se, and other chemicals to receiving streams due to the installation of flue gas desulfurization (FGD) air pollution control units. There are regulatory concerns about the potential increased uptake of these bioaccumulative trace elements into food webs. We evaluated the concentrations of As, total Hg (THg), methylmercury (MeHg), and Se in Ohio River fish collected proximal to coal-fired power plants, of which 75% operate FGD systems. Fillet samples (n = 50) from 6 fish species representing 3 trophic levels were analyzed. Geometric mean fillet concentrations of THg (wet wt), MeHg (wet wt), and Se (dry wt) in 3 species were 0.136, 0.1181, and 3.19 mg/kg (sauger); 0.123, 0.1013, and 1.56 mg/kg (channel catfish); and 0.127, 0.0914, and 3.30 mg/kg (hybrid striped bass). For all species analyzed, only 3 fillet samples (6% of total) had MeHg concentrations that exceeded the US Environmental Protection Agency (USEPA) human health criterion (0.3 mg/kg wet wt); all of these were freshwater drum aged ≥ 19 y. None of the samples analyzed exceeded the USEPA proposed muscle and whole body Se thresholds for protection against reproductive effects in freshwater fish. All but 8 fillet samples had a total As concentration less than 1.0 mg/kg dry wt. Mean Se health benefit values (HBVSe ) for all species were ≥ 4, indicating that potential Hg-related health risks associated with consumption of Ohio River fish are likely to be offset by adequate Se concentrations. Overall, we observed no measurable evidence of enhanced trace element bioaccumulation associated with proximity to power plant FGD facilities, however, some enhanced bioaccumulation could have occurred in the wastewater mixing zones. Furthermore, available evidence indicates that, due to hydraulic and physical factors, the main stem Ohio River appears to have low net Hg methylation potential. © 2015 SETAC.

  15. A Computational Model of Pattern Separation Efficiency in the Dentate Gyrus with Implications in Schizophrenia

    Directory of Open Access Journals (Sweden)

    Faramarz eFaghihi

    2015-03-01

    Full Text Available Information processing in the hippocampus begins by transferring spiking activity of the Entorhinal Cortex (EC into the Dentate Gyrus (DG. Activity pattern in the EC is separated by the DG such that it plays an important role in hippocampal functions including memory. The structural and physiological parameters of these neural networks enable the hippocampus to be efficient in encoding a large number of inputs that animals receive and process in their life time. The neural encoding capacity of the DG depends on its single neurons encoding and pattern separation efficiency. In this study, encoding by the DG is modelled such that single neurons and pattern separation efficiency are measured using simulations of different parameter values. For this purpose, a probabilistic model of single neurons efficiency is presented to study the role of structural and physiological parameters. Known neurons number of the EC and the DG is used to construct a neural network by electrophysiological features of neuron in the DG. Separated inputs as activated neurons in the EC with different firing probabilities are presented into the DG. For different connectivity rates between the EC and DG, pattern separation efficiency of the DG is measured. The results show that in the absence of feedback inhibition on the DG neurons, the DG demonstrates low separation efficiency and high firing frequency. Feedback inhibition can increase separation efficiency while resulting in very low single neuron’s encoding efficiency in the DG and very low firing frequency of neurons in the DG (sparse spiking. This work presents a mechanistic explanation for experimental observations in the hippocampus, in combination with theoretical measures. Moreover, the model predicts a critical role for impaired inhibitory neurons in schizophrenia where deficiency in pattern separation of the DG has been observed.

  16. A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia

    Science.gov (United States)

    Faghihi, Faramarz; Moustafa, Ahmed A.

    2015-01-01

    Information processing in the hippocampus begins by transferring spiking activity of the entorhinal cortex (EC) into the dentate gyrus (DG). Activity pattern in the EC is separated by the DG such that it plays an important role in hippocampal functions including memory. The structural and physiological parameters of these neural networks enable the hippocampus to be efficient in encoding a large number of inputs that animals receive and process in their life time. The neural encoding capacity of the DG depends on its single neurons encoding and pattern separation efficiency. In this study, encoding by the DG is modeled such that single neurons and pattern separation efficiency are measured using simulations of different parameter values. For this purpose, a probabilistic model of single neurons efficiency is presented to study the role of structural and physiological parameters. Known neurons number of the EC and the DG is used to construct a neural network by electrophysiological features of granule cells of the DG. Separated inputs as activated neurons in the EC with different firing probabilities are presented into the DG. For different connectivity rates between the EC and DG, pattern separation efficiency of the DG is measured. The results show that in the absence of feedback inhibition on the DG neurons, the DG demonstrates low separation efficiency and high firing frequency. Feedback inhibition can increase separation efficiency while resulting in very low single neuron’s encoding efficiency in the DG and very low firing frequency of neurons in the DG (sparse spiking). This work presents a mechanistic explanation for experimental observations in the hippocampus, in combination with theoretical measures. Moreover, the model predicts a critical role for impaired inhibitory neurons in schizophrenia where deficiency in pattern separation of the DG has been observed. PMID:25859189

  17. Browns Ferry fire

    International Nuclear Information System (INIS)

    Harkleroad, J.R.

    1983-01-01

    A synopsis of the March 22, 1975 fire at Browns Ferry Nuclear Plant is discussed. Emphasis is placed on events prior to and during the fire. How the fire started, fire fighting activities, fire and smoke development, and restoration activities are discussed

  18. Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns.

    Directory of Open Access Journals (Sweden)

    Hiroyuki Akama

    Full Text Available In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF. This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk and co-occurrence adjustment (degree balance and distribution. We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

  19. Antenna analysis using neural networks

    Science.gov (United States)

    Smith, William T.

    1992-01-01

    Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern

  20. The Neural Border: Induction, Specification and Maturation of the territory that generates Neural Crest cells.

    Science.gov (United States)

    Pla, Patrick; Monsoro-Burq, Anne H

    2018-05-28

    The neural crest is induced at the edge between the neural plate and the nonneural ectoderm, in an area called the neural (plate) border, during gastrulation and neurulation. In recent years, many studies have explored how this domain is patterned, and how the neural crest is induced within this territory, that also participates to the prospective dorsal neural tube, the dorsalmost nonneural ectoderm, as well as placode derivatives in the anterior area. This review highlights the tissue interactions, the cell-cell signaling and the molecular mechanisms involved in this dynamic spatiotemporal patterning, resulting in the induction of the premigratory neural crest. Collectively, these studies allow building a complex neural border and early neural crest gene regulatory network, mostly composed by transcriptional regulations but also, more recently, including novel signaling interactions. Copyright © 2018. Published by Elsevier Inc.

  1. Grid-pattern formation of extracellular matrix on silicon by low-temperature atmospheric-pressure plasma jets for neural network biochip fabrication

    Energy Technology Data Exchange (ETDEWEB)

    Ando, Ayumi, E-mail: ando@ppl.eng.osaka-u.ac.jp [Center for Atomic and Molecular Technologies, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka, 565-0871 (Japan); Uno, Hidetaka; Urisu, Tsuneo [FIRST Research Center for Innovative Nanobiodevice, Nagoya University, Furo-cho, Chikusa, Nagoya, Aichi, 464-8603 (Japan); Hamaguchi, Satoshi [Center for Atomic and Molecular Technologies, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka, 565-0871 (Japan)

    2013-07-01

    Grid patterns of extracellular matrices (ECMs) have been formed on silicon (Si) substrates with the use of low-temperature atmospheric-pressure plasma (APP) jets with metal stencil masks and neuron model cells have been successfully cultured on the patterned ECMs. Arrangement of living neuron cells on a microelectronics chip in a desired pattern is one of the major challenges for the fabrication of neuron-cell biochips. The APP-based technique presented in this study offers a cost-effective solution to this problem by providing a simple patterning method of ECMs, which act as biological interfaces between living cells and non-biological materials such as Si.

  2. Fire Ant Bites

    Science.gov (United States)

    ... Favorite Name: Category: Share: Yes No, Keep Private Fire Ant Bites Share | Fire ants are aggressive, venomous insects that have pinching ... across the United States, even into Puerto Rico. Fire ant stings usually occur on the feet or ...

  3. Fire safety at home

    Science.gov (United States)

    ... over the smoke alarm as needed. Using a fire extinguisher can put out a small fire to keep it from getting out of control. Tips for use include: Keep fire extinguishers in handy locations, at least one on ...

  4. Crown Fire Potential

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — Crown fire potential was modeled using FlamMap, an interagency fire behavior mapping and analysis program that computes potential fire behavior characteristics. The...

  5. National Fire Protection Association

    Science.gov (United States)

    ... closed NFPA Journal® NFPA Journal® Update (newsletter) Fire Technology ... die from American home fires, and another 13,000 are injured each year. This is the story of fire that the statistics won't show ...

  6. Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall

    Science.gov (United States)

    Hampson, Robert E.; Song, Dong; Robinson, Brian S.; Fetterhoff, Dustin; Dakos, Alexander S.; Roeder, Brent M.; She, Xiwei; Wicks, Robert T.; Witcher, Mark R.; Couture, Daniel E.; Laxton, Adrian W.; Munger-Clary, Heidi; Popli, Gautam; Sollman, Myriam J.; Whitlow, Christopher T.; Marmarelis, Vasilis Z.; Berger, Theodore W.; Deadwyler, Sam A.

    2018-06-01

    Objective. We demonstrate here the first successful implementation in humans of a proof-of-concept system for restoring and improving memory function via facilitation of memory encoding using the patient’s own hippocampal spatiotemporal neural codes for memory. Memory in humans is subject to disruption by drugs, disease a