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

Science.gov (United States)

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.

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.

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.

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.

The Shift from a Response Strategy to Object-in-Place Strategy during Learning Is Accompanied by a Matching Shift in Neural Firing Correlates in the Hippocampus

Science.gov (United States)

Lee, Inah; Kim, Jangjin

2010-01-01

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…

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)

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.

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

Science.gov (United States)

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.

A hydroclimatic model of global fire patterns

Science.gov (United States)

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

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.

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...

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.

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

Dynamics, patterns and causes of fires in Northwestern Amazonia.

Science.gov (United States)

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.

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.

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.

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

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.

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...

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).

[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.

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.

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.

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.

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)

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.

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.

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

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

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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.

Patterns recognition of electric brain activity using artificial neural networks

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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.

Adrenergic receptor-mediated modulation of striatal firing patterns.

Science.gov (United States)

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.

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

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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.

Assessing the suitability of soft computing approaches for forest fires prediction

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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

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

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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.

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.

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...

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

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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.

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

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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.

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

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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.

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

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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.

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...

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

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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.

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

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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...

Neural Global Pattern Similarity Underlies True and False Memories.

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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.

Neural mechanisms of sequence generation in songbirds

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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.

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

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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.

Neural coding in graphs of bidirectional associative memories.

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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.

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

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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.

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)

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

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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.

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.

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

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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...

Recurrent Neural Network For Forecasting Time Series With Long Memory Pattern

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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.

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

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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.

Transformed Neural Pattern Reinstatement during Episodic Memory Retrieval.

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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

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

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...

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...

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.

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

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.

Spatiotemporal distribution patterns of forest fires in northern Mexico

Science.gov (United States)

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...

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.

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...

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.

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

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

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)

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.

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.

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.

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

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.

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.

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.

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...

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...

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.

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.

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.

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

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.

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

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.

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.

Cross-Coupled Eye Movement Supports Neural Origin of Pattern Strabismus

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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

Evaluating fire danger in Brazilian biomes: present and future patterns

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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

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

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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.

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

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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

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)

Spike Neural Models Part II: Abstract Neural Models

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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.

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

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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...

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

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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.

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

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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.

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

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)

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

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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.

Sequence memory based on coherent spin-interaction neural networks.

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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.

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

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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.

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)

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

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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.

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

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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

Bursts generate a non-reducible spike-pattern code

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Hugo G Eyherabide

2009-05-01

Full Text Available On the single-neuron level, precisely timed spikes can either constitute firing-rate codes or spike-pattern codes that utilize the relative timing between consecutive spikes. There has been little experimental support for the hypothesis that such temporal patterns contribute substantially to information transmission. Using grasshopper auditory receptors as a model system, we show that correlations between spikes can be used to represent behaviorally relevant stimuli. The correlations reflect the inner structure of the spike train: a succession of burst-like patterns. We demonstrate that bursts with different spike counts encode different stimulus features, such that about 20% of the transmitted information corresponds to discriminating between different features, and the remaining 80% is used to allocate these features in time. In this spike-pattern code, the "what" and the "when" of the stimuli are encoded in the duration of each burst and the time of burst onset, respectively. Given the ubiquity of burst firing, we expect similar findings also for other neural systems.

A new perspective on behavioral inconsistency and neural noise in aging: Compensatory speeding of neural communication

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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.

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

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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.

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

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

2018-03-01

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

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

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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

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

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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.

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

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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.

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

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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.

Recognition of neural brain activity patterns correlated with complex motor activity

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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.

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

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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.

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.

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

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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.

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

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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-...

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).

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

Science.gov (United States)

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

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)

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...

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

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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.

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

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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.

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)

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

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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.

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

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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...

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’

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

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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

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

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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.

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.

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

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

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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.

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.

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.

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

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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.

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

Science.gov (United States)

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

2016-01-01

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

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.

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

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...

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.

Implementing Signature Neural Networks with Spiking Neurons.

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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

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

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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

Evidence of fuels management and fire weather influencing fire severity in an extreme fire event.

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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.

A Review of Fire Interactions and Mass Fires

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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.

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

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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 and brain injury, yet previous attempts to restore or rescue memory function in humans typically involved only nonspecific, modulation of brain areas and neural systems related to memory retrieval. Approach. We have constructed a model of processes by which the hippocampus encodes memory items via spatiotemporal firing of neural ensembles that underlie the successful encoding of short-term memory. A nonlinear multi-input, multi-output (MIMO) model of hippocampal CA3 and CA1 neural firing is computed that predicts activation patterns of CA1 neurons during the encoding (sample) phase of a delayed match-to-sample (DMS) human short-term memory task. Main results. MIMO model-derived electrical stimulation delivered to the same CA1 locations during the sample phase of DMS trials facilitated short-term/working memory by 37% during the task. Longer term memory retention was also tested in the same human subjects with a delayed recognition (DR) task that utilized images from the DMS task, along with images that were not from the task. Across the subjects, the stimulated trials exhibited significant improvement (35%) in both short-term and long-term retention of visual information. Significance. These results demonstrate the facilitation of memory encoding which is an important feature for the construction of an implantable neural prosthetic to improve human memory.

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

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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.

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.

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

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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.

Fire history and pattern in a Cascade Range landscape.

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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...

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

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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.

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%.

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

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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%.

Combining satellite-based fire observations and ground-based lightning detections to identify lightning fires across the conterminous USA

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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.

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.

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...

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

Establishing a Statistical Link between Network Oscillations and Neural Synchrony.

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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.

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

Area PEc Neurons Use a Multiphasic Pattern of Activity to Signal the Spatial Properties of Optic Flow

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Milena Raffi

2017-01-01

Full Text Available The cortical representation of visual perception requires the integration of several-signal processing distributed across many cortical areas, but the neural substrates of such perception are largely unknown. The type of firing pattern exhibited by single neurons is an important indicator of dynamic circuitry within or across cortical areas. Neurons in area PEc are involved in the spatial mapping of the visual field; thus, we sought to analyze the firing pattern of activity of PEc optic flow neurons to shed some light on the cortical processing of visual signals. We quantified the firing activity of 152 optic flow neurons using a spline interpolation function, which allowed determining onset, end, and latency of each neuronal response. We found that many PEc neurons showed multiphasic activity, which is strictly related to the position of the eye and to the position of the focus of expansion (FOE of the flow field. PEc neurons showed a multiphasic activity comprised of excitatory phases interspersed with inhibitory pauses. This phasic pattern seems to be a very efficient way to signal the spatial location of visual stimuli, given that the same neuron sends different firing patterns according to a specific combination of FOE/eye position.

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.

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

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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…

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.

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

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.

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)

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.

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.

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

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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

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

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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

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)

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...

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

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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.

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

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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.

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

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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.

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

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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.

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

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)

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

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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.

Study of GABAergic extra-synaptic tonic inhibition in single neurons and neural populations by traversing neural scales: application to propofol-induced anaesthesia.

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Hutt, Axel; Buhry, Laure

2014-12-01

Anaesthetic agents are known to affect extra-synaptic GABAergic receptors, which induce tonic inhibitory currents. Since these receptors are very sensitive to small concentrations of agents, they are supposed to play an important role in the underlying neural mechanism of general anaesthesia. Moreover anaesthetic agents modulate the encephalographic activity (EEG) of subjects and hence show an effect on neural populations. To understand better the tonic inhibition effect in single neurons on neural populations and hence how it affects the EEG, the work considers single neurons and neural populations in a steady-state and studies numerically and analytically the modulation of their firing rate and nonlinear gain with respect to different levels of tonic inhibition. We consider populations of both type-I (Leaky Integrate-and-Fire model) and type-II (Morris-Lecar model) neurons. To bridge the single neuron description to the population description analytically, a recently proposed statistical approach is employed which allows to derive new analytical expressions for the population firing rate for type-I neurons. In addition, the work shows the derivation of a novel transfer function for type-I neurons as considered in neural mass models and studies briefly the interaction of synaptic and extra-synaptic inhibition. We reveal a strong subtractive and divisive effect of tonic inhibition in type-I neurons, i.e. a shift of the firing rate to higher excitation levels accompanied by a change of the nonlinear gain. Tonic inhibition shortens the excitation window of type-II neurons and their populations while maintaining the nonlinear gain. The gained results are interpreted in the context of recent experimental findings under propofol-induced anaesthesia.

Death and rebirth of neural activity in sparse inhibitory networks

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Angulo-Garcia, David; Luccioli, Stefano; Olmi, Simona; Torcini, Alessandro

2017-05-01

Inhibition is a key aspect of neural dynamics playing a fundamental role for the emergence of neural rhythms and the implementation of various information coding strategies. Inhibitory populations are present in several brain structures, and the comprehension of their dynamics is strategical for the understanding of neural processing. In this paper, we clarify the mechanisms underlying a general phenomenon present in pulse-coupled heterogeneous inhibitory networks: inhibition can induce not only suppression of neural activity, as expected, but can also promote neural re-activation. In particular, for globally coupled systems, the number of firing neurons monotonically reduces upon increasing the strength of inhibition (neuronal death). However, the random pruning of connections is able to reverse the action of inhibition, i.e. in a random sparse network a sufficiently strong synaptic strength can surprisingly promote, rather than depress, the activity of neurons (neuronal rebirth). Thus, the number of firing neurons reaches a minimum value at some intermediate synaptic strength. We show that this minimum signals a transition from a regime dominated by neurons with a higher firing activity to a phase where all neurons are effectively sub-threshold and their irregular firing is driven by current fluctuations. We explain the origin of the transition by deriving a mean field formulation of the problem able to provide the fraction of active neurons as well as the first two moments of their firing statistics. The introduction of a synaptic time scale does not modify the main aspects of the reported phenomenon. However, for sufficiently slow synapses the transition becomes dramatic, and the system passes from a perfectly regular evolution to irregular bursting dynamics. In this latter regime the model provides predictions consistent with experimental findings for a specific class of neurons, namely the medium spiny neurons in the striatum.

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

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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

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

Fire Scenarios in Spain: A Territorial Approach to Proactive Fire Management in the Context of Global Change

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Cristina Montiel Molina

2016-11-01

Full Text Available Humans and fire form a coupled and co-evolving natural-human system in Mediterranean-climate ecosystems. In this context, recent trends in landscape change, such as urban sprawl or the abandoning of agricultural and forest land management in line with new models of economic development and lifestyles, are leading to new fire scenarios. A fire scenario refers to the contextual factors of a fire regime, i.e., the environmental, socio-economic and policy drivers of wildfire initiation and propagation on different spatial and temporal scales. This is basically a landscape concept linking territorial dynamics (related to ecosystem evolution and settlement patterns with a fire regime (ignition causes; spread patterns; fire frequency, severity, extent and seasonality. The aim of this article is to identify and characterize these land-based fire scenarios in Spain on a national and regional scale, using a GIS-based methodology to perform a spatial analysis of the area attributes of homogenous fire spread patterns. To do this, the main variables considered are: land use/land cover, fuel load and recent fire history. The final objective is to reduce territorial vulnerability to forest wildfires and facilitate the adaptation of fire policies and land management systems to current challenges of preparedness and uncertainty management.

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...

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.

Models of neural networks temporal aspects of coding and information processing in biological systems

CERN Document Server

Hemmen, J; Schulten, Klaus

1994-01-01

Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has focused on two paradigms: information coding through coherent firing of the neurons and functional feedback. Information coding through coherent neuronal firing exploits time as a cardinal degree of freedom. This capacity of a neural network rests on the fact that the neuronal action potential is a short, say 1 ms, spike, localized in space and time. Spatial as well as temporal correlations of activity may represent different states of a network. In particular, temporal correlations of activity may express that neurons process the same "object" of, for example, a visual scene by spiking at the very same time. The traditional description of a neural network through a firing rate, the famous S-shaped curve, presupposes a wide time window of, say, at least 100 ms. It thus fails to exploit the capacity to "bind" sets of coherently firing neurons for the purpose of both scene segmentation and figure-ground segregatio...

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

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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

Neural dynamics in reconfigurable silicon.

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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).

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

Science.gov (United States)

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.

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

Fuzzy logic and neural networks in artificial intelligence and pattern recognition

Science.gov (United States)

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.

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.

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

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…

Battery-Free Love-Wave-Based Neural Probe and Its Wireless Characterizations

Science.gov (United States)

Jung, In Ki; Fu, Chen; Lee, Keekeun

2013-06-01

A wireless Love-wave-based neural probe that utilizes a one-port reflective delay line was developed for both reading and stimulating neurons in the brain. Poly(methyl methacrylate) (PMMA) as a waveguide layer and gold (Au) electrodes were structured on the top of a 41° YX LiNbO3 piezoelectric substrate, following the parameters extracted from coupling-of-mode (COM) modeling. For a one-port reflective delay line, single-phase unidirectional transducers (SPUDTs) and three shorted grating reflectors were employed, which made possible the implementation of a wireless and battery-free neural probe. The fabricated Love-wave-based neural probes were wirelessly measured using two antennas with a 440 MHz central frequency and a network analyzer. Sharp reflection peaks with a high signal-to-noise ratio were observed from the reflection peaks. The probe was immersed in 0.9% saline solution while applying input DC voltages. Good linearity, high sensitivity, and reproducibility were observed depending on DC applied voltage, in the range from 0 to 500 mV. The sensitivity obtained from the DC firings (artificial neural firings) was ˜0.04 µs/VDC, indicating that this prototype probe is very promising for the wireless reading and stimulation of neural firings in in vivo animal testing.

A Neural Network-Based Interval Pattern Matcher

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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.

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…

A hidden Markov model approach to neuron firing patterns.

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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.

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

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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.

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.

An 800-year fire history

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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...

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

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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.

Creative-Dynamics Approach To Neural Intelligence

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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.

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.

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.

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 ...

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

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

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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.

How fast can we learn maximum entropy models of neural populations?

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Ganmor, Elad; Schneidman, Elad [Department of Neuroscience, Weizmann Institute of Science, Rehovot 76100 (Israel); Segev, Ronen, E-mail: elad.ganmor@weizmann.ac.i, E-mail: elad.schneidman@weizmann.ac.i [Department of Life Sciences and Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva 84105 (Israel)

2009-12-01

Most of our knowledge about how the brain encodes information comes from recordings of single neurons. However, computations in the brain are carried out by large groups of neurons. Modelling the joint activity of many interacting elements is computationally hard because of the large number of possible activity patterns and limited experimental data. Recently it was shown in several different neural systems that maximum entropy pairwise models, which rely only on firing rates and pairwise correlations of neurons, are excellent models for the distribution of activity patterns of neural populations, and in particular, their responses to natural stimuli. Using simultaneous recordings of large groups of neurons in the vertebrate retina responding to naturalistic stimuli, we show here that the relevant statistics required for finding the pairwise model can be accurately estimated within seconds. Furthermore, while higher order statistics may, in theory, improve model accuracy, they are, in practice, harmful for times of up to 20 minutes due to sampling noise. Finally, we demonstrate that trading accuracy for entropy may actually improve model performance when data is limited, and suggest an optimization method that automatically adjusts model constraints in order to achieve good performance.

How fast can we learn maximum entropy models of neural populations?

International Nuclear Information System (INIS)

Ganmor, Elad; Schneidman, Elad; Segev, Ronen

2009-01-01

Most of our knowledge about how the brain encodes information comes from recordings of single neurons. However, computations in the brain are carried out by large groups of neurons. Modelling the joint activity of many interacting elements is computationally hard because of the large number of possible activity patterns and limited experimental data. Recently it was shown in several different neural systems that maximum entropy pairwise models, which rely only on firing rates and pairwise correlations of neurons, are excellent models for the distribution of activity patterns of neural populations, and in particular, their responses to natural stimuli. Using simultaneous recordings of large groups of neurons in the vertebrate retina responding to naturalistic stimuli, we show here that the relevant statistics required for finding the pairwise model can be accurately estimated within seconds. Furthermore, while higher order statistics may, in theory, improve model accuracy, they are, in practice, harmful for times of up to 20 minutes due to sampling noise. Finally, we demonstrate that trading accuracy for entropy may actually improve model performance when data is limited, and suggest an optimization method that automatically adjusts model constraints in order to achieve good performance.

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.

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.

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...

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.

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.

Nonlinear transfer function encodes synchronization in a neural network from the mammalian brain.

Science.gov (United States)

Menendez de la Prida, L; Sanchez-Andres, J V

1999-09-01

Synchronization is one of the mechanisms by which the brain encodes information. The observed synchronization of neuronal activity has, however, several levels of fluctuations, which presumably regulate local features of specific areas. This means that biological neural networks should have an intrinsic mechanism able to synchronize the neuronal activity but also to preserve the firing capability of individual cells. Here, we investigate the input-output relationship of a biological neural network from developing mammalian brain, i.e., the hippocampus. We show that the probability of occurrence of synchronous output activity (which consists in stereotyped population bursts recorded throughout the hippocampus) is encoded by a sigmoidal transfer function of the input frequency. Under this scope, low-frequency inputs will not produce any coherent output while high-frequency inputs will determine a synchronous pattern of output activity (population bursts). We analyze the effect of the network size (N) on the parameters of the transfer function (threshold and slope). We found that sigmoidal functions realistically simulate the synchronous output activity of hippocampal neural networks. This outcome is particularly important in the application of results from neural network models to neurobiology.

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-...

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.

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

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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.

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

Science.gov (United States)

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.

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.

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

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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

Fire Resistance Tests of Various Fire Protective Coatings

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Mindaugas GRIGONIS

2011-03-01

Full Text Available Tests were carried out on more than 14 different samples of fire protective coatings in order to investigate a relation between the thickness of the intumescent fire protection coating and the time of exposure to heat. A number of coatings of different chemical composition enabled to determine the fire resistance behaviour patterns. During test the one-side and volumetric methods were employed in observance of the standard temperature-time curves. For one-side method, the coating was applied on one side and all edges of the specimen, whereas for volumetric test the specimens were completely covered with fire protective coating. It is shown that a layer of coating protects the specimen's surface from heat exposure for a certain period of time until full oxidation of the coating occurs. The efficiency of fire protective coatings also depends on thickness of the charred layer of the side exposed to heat.http://dx.doi.org/10.5755/j01.ms.17.1.257

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

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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.

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....

Antenna analysis using neural networks

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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

Enteric neural crest cells regulate vertebrate stomach patterning and differentiation.

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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.

Bioprinting for Neural Tissue Engineering.

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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.

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

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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.

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

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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.

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...

Fire history and fire management implications in the Yukon Flats National Wildlife Refuge, interior Alaska

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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...

Multifractal analysis of forest fires in complex regions

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Vega Orozco, C. D.; Kanevski, M.; Golay, J.; Tonini, M.; Conedera, M.

2012-04-01

Forest fires can be studied as point processes where the ignition points represent the set of locations of the observed events in a defined study region. Their spatial and temporal patterns can be characterized by their fractal properties; which quantify the global aspect of the geometry of the support data. However, a monofractal dimension can not completely describe the pattern structure and related scaling properties. Enhancements in fractal theory had developed the multifractal concept which describes the measures from which interlinked fractal sets can be retrieved and characterized by their fractal dimension and singularity strength [1, 2]. The spatial variability of forest fires is conditioned by an intermixture of human, topographic, meteorological and vegetation factors. This heterogeneity makes fire patterns complex scale-invariant processes difficult to be depicted by a single scale. Therefore, this study proposes an exploratory data analysis through a multifractal formalism to characterize and quantify the multiscaling behaviour of the spatial distribution pattern of this phenomenon in a complex region like the Swiss Alps. The studied dataset is represented by 2,401 georeferenced forest fire ignition points in canton Ticino, Switzerland, in a 40-years period from 1969 to 2008. Three multifractal analyses are performed: one assesses the multiscaling behaviour of fire occurrence probability of the support data (raw data) and four random patterns simulated within three different support domains; second analysis studies the multifractal behavior of patterns from anthropogenic and natural ignited fires (arson-, accident- and lightning-caused fires); and third analysis aims at detecting scale-dependency of the size of burned area. To calculate the generalized dimensions, Dq, a generalization of the box counting methods is carried out based on the generalization of Rényi information of the qth order moment of the probability distribution. For q > 0, Dq

A Markovian event-based framework for stochastic spiking neural networks.

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Touboul, Jonathan D; Faugeras, Olivier D

2011-11-01

In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.

Differential theory of learning for efficient neural network pattern recognition

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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.

Recurrent coupling improves discrimination of temporal spike patterns

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Chun-Wei eYuan

2012-05-01

Full Text Available Despite the ubiquitous presence of recurrent synaptic connections insensory neuronal systems, their general functional purpose is not wellunderstood. A recent conceptual advance has been achieved by theoriesof reservoir computing in which recurrent networks have been proposedto generate short-term memory as well as to improve neuronalrepresentation of the sensory input for subsequent computations.Here, we present a numerical study on the distinct effects ofinhibitory and excitatory recurrence in a canonical linearclassification task. It is found that both types of coupling improvethe ability to discriminate temporal spike patterns as compared to apurely feed-forward system, although in different ways. For a largeclass of inhibitory networks, the network's performance is optimal aslong as a fraction of roughly 50% of neurons per stimulus is activein the resulting population code. Thereby the contribution of inactiveneurons to the neural code is found to be even more informative thanthat of the active neurons, generating an inherent robustness ofclassification performance against temporal jitter of the inputspikes. Excitatory couplings are found to not only produce ashort-term memory buffer but also to improve linear separability ofthe population patterns by evoking more irregular firing as comparedto the purely inhibitory case. As the excitatory connectivity becomesmore sparse, firing becomes more variable and pattern separabilityimproves. We argue that the proposed paradigm is particularlywell-suited as a conceptual framework for processing of sensoryinformation in the auditory pathway.

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

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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.

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.

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

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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...

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.

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

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Scott L. Stephens

2008-12-01

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

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

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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

Application of unfolding transformation in the random matrix theory to analyze in vivo neuronal spike firing during awake and anesthetized conditions

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Risako Kato

2018-03-01

Full Text Available General anesthetics decrease the frequency and density of spike firing. This effect makes it difficult to detect spike regularity. To overcome this problem, we developed a method utilizing the unfolding transformation which analyzes the energy level statistics in the random matrix theory. We regarded the energy axis as time axis of neuron spike and analyzed the time series of cortical neural firing in vivo. Unfolding transformation detected regularities of neural firing while changes in firing densities were associated with pentobarbital. We found that unfolding transformation enables us to compare firing regularity between awake and anesthetic conditions on a universal scale. Keywords: Unfolding transformation, Spike-timing, Regularity

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...

The Challenges of Neural Mind-reading Paradigms

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Oscar eVilarroya

2013-06-01

Full Text Available Neural mind-reading studies, based on multivariate pattern analysis (MVPA methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: a the BOLD signal is a marker of neural activity; b the BOLD pattern identified by a MVPA is a neurally sound pattern; c the MVPA’s feature space is a good mapping of the neural representation of a stimulus, and d the pattern identified by a MVPA corresponds to a representation. I examine here the challenges that still have to be met before fully accepting such assumptions.

The challenges of neural mind-reading paradigms.

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Vilarroya, Oscar

2013-01-01

Neural mind-reading studies, based on multivariate pattern analysis (MVPA) methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: (a) the BOLD signal is a marker of neural activity; (b) the BOLD pattern identified by a MVPA is a neurally sound pattern; (c) the MVPA's feature space is a good mapping of the neural representation of a stimulus, and (d) the pattern identified by a MVPA corresponds to a representation. I examine here the challenges that still have to be met before fully accepting such assumptions.

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

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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

Systems thinking and wildland fire management

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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...

Spike timing analysis in neural networks with unsupervised synaptic plasticity

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Mizusaki, B. E. P.; Agnes, E. J.; Brunnet, L. G.; Erichsen, R., Jr.

2013-01-01

The synaptic plasticity rules that sculpt a neural network architecture are key elements to understand cortical processing, as they may explain the emergence of stable, functional activity, while avoiding runaway excitation. For an associative memory framework, they should be built in a way as to enable the network to reproduce a robust spatio-temporal trajectory in response to an external stimulus. Still, how these rules may be implemented in recurrent networks and the way they relate to their capacity of pattern recognition remains unclear. We studied the effects of three phenomenological unsupervised rules in sparsely connected recurrent networks for associative memory: spike-timing-dependent-plasticity, short-term-plasticity and an homeostatic scaling. The system stability is monitored during the learning process of the network, as the mean firing rate converges to a value determined by the homeostatic scaling. Afterwards, it is possible to measure the recovery efficiency of the activity following each initial stimulus. This is evaluated by a measure of the correlation between spike fire timings, and we analysed the full memory separation capacity and limitations of this system.

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

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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.

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.

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.

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.

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...

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)

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)

Inherently stochastic spiking neurons for probabilistic neural computation

KAUST Repository

Al-Shedivat, Maruan

2015-04-01

Neuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms. © 2015 IEEE.

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

The challenges of neural mind-reading paradigms

OpenAIRE

Vilarroya, Oscar

2013-01-01

Neural mind-reading studies, based on multivariate pattern analysis (MVPA) methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: (a) the BOLD signal is a marker of neural activity; (b) the BOLD pattern identified by a MVPA is a neurally sound pattern; (c) the MVPA's feature space is a good mapping of the neura...

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.

Deterministic integer multiple firing depending on initial state in Wang model

Energy Technology Data Exchange (ETDEWEB)

Xie Yong [Institute of Nonlinear Dynamics, MSSV, Department of Engineering Mechanics, Xi' an Jiaotong University, Xi' an 710049 (China)]. E-mail: yxie@mail.xjtu.edu.cn; Xu Jianxue [Institute of Nonlinear Dynamics, MSSV, Department of Engineering Mechanics, Xi' an Jiaotong University, Xi' an 710049 (China); Jiang Jun [Institute of Nonlinear Dynamics, MSSV, Department of Engineering Mechanics, Xi' an Jiaotong University, Xi' an 710049 (China)

2006-12-15

We investigate numerically dynamical behaviour of the Wang model, which describes the rhythmic activities of thalamic relay neurons. The model neuron exhibits Type I excitability from a global view, but Type II excitability from a local view. There exists a narrow range of bistability, in which a subthreshold oscillation and a suprathreshold firing behaviour coexist. A special firing pattern, integer multiple firing can be found in the certain part of the bistable range. The characteristic feature of such firing pattern is that the histogram of interspike intervals has a multipeaked structure, and the peaks are located at about integer multiples of a basic interspike interval. Since the Wang model is noise-free, the integer multiple firing is a deterministic firing pattern. The existence of bistability leads to the deterministic integer multiple firing depending on the initial state of the model neuron, i.e., the initial values of the state variables.

Deterministic integer multiple firing depending on initial state in Wang model

International Nuclear Information System (INIS)

Xie Yong; Xu Jianxue; Jiang Jun

2006-01-01

We investigate numerically dynamical behaviour of the Wang model, which describes the rhythmic activities of thalamic relay neurons. The model neuron exhibits Type I excitability from a global view, but Type II excitability from a local view. There exists a narrow range of bistability, in which a subthreshold oscillation and a suprathreshold firing behaviour coexist. A special firing pattern, integer multiple firing can be found in the certain part of the bistable range. The characteristic feature of such firing pattern is that the histogram of interspike intervals has a multipeaked structure, and the peaks are located at about integer multiples of a basic interspike interval. Since the Wang model is noise-free, the integer multiple firing is a deterministic firing pattern. The existence of bistability leads to the deterministic integer multiple firing depending on the initial state of the model neuron, i.e., the initial values of the state variables

Reduced frequency and severity of residential fires following delivery of fire prevention education by on-duty fire fighters: cluster randomized controlled study.

Science.gov (United States)

Clare, Joseph; Garis, Len; Plecas, Darryl; Jennings, Charles

2012-04-01

In 2008, Surrey Fire Services, British Columbia, commenced a firefighter-delivered, door-to-door fire-prevention education and smoke alarm examination/installation initiative with the intention of reducing the frequency and severity of residential structure fires in the City of Surrey. High-risk zones within the city were identified and 18,473 home visits were undertaken across seven temporal delivery cohorts (13.8% of non-apartment dwellings in the city). The frequency and severity of fires pre- and post- the home visit intervention was examined in comparison to randomized high-risk cluster controls. Overall, the frequency of fires was found to have reduced in the city overall, however, the reduction in the intervention cohorts was significantly larger than for controls. Furthermore, when fires did occur within the intervention cohorts, smoke detectors were activated more frequently and the fires were confined to the object of origin more often post-home visits. No equivalent pattern was observed for the cluster control. On-duty fire fighters can reduce the frequency and severity of residential fires through targeted, door-to-door distribution of fire prevention education in high-risk areas. Copyright © 2012 Elsevier Ltd. All rights reserved.

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.

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...

Distributed Recurrent Neural Forward Models with Neural Control for Complex Locomotion in Walking Robots

DEFF Research Database (Denmark)

Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin

2015-01-01

here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated......Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental...... conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain...

A model of stimulus-specific neural assemblies in the insect antennal lobe.

Directory of Open Access Journals (Sweden)

Dominique Martinez

2008-08-01

Full Text Available It has been proposed that synchronized neural assemblies in the antennal lobe of insects encode the identity of olfactory stimuli. In response to an odor, some projection neurons exhibit synchronous firing, phase-locked to the oscillations of the field potential, whereas others do not. Experimental data indicate that neural synchronization and field oscillations are induced by fast GABA(A-type inhibition, but it remains unclear how desynchronization occurs. We hypothesize that slow inhibition plays a key role in desynchronizing projection neurons. Because synaptic noise is believed to be the dominant factor that limits neuronal reliability, we consider a computational model of the antennal lobe in which a population of oscillatory neurons interact through unreliable GABA(A and GABA(B inhibitory synapses. From theoretical analysis and extensive computer simulations, we show that transmission failures at slow GABA(B synapses make the neural response unpredictable. Depending on the balance between GABA(A and GABA(B inputs, particular neurons may either synchronize or desynchronize. These findings suggest a wiring scheme that triggers stimulus-specific synchronized assemblies. Inhibitory connections are set by Hebbian learning and selectively activated by stimulus patterns to form a spiking associative memory whose storage capacity is comparable to that of classical binary-coded models. We conclude that fast inhibition acts in concert with slow inhibition to reformat the glomerular input into odor-specific synchronized neural assemblies.

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…

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...

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.

Fire-driven alien invasion in a fire-adapted ecosystem

Science.gov (United States)

Keeley, Jon E.; Brennan, Teresa J.

2012-01-01

Disturbance plays a key role in many alien plant invasions. However, often the main driver of invasion is not disturbance per se but alterations in the disturbance regime. In some fire-adapted shrublands, the community is highly resilient to infrequent, high-intensity fires, but changes in the fire regime that result in shorter fire intervals may make these communities more susceptible to alien plant invasions. This study examines several wildfire events that resulted in short fire intervals in California chaparral shrublands. In one study, we compared postfire recovery patterns in sites with different prefire stand ages (3 and 24 years), and in another study we compared sites that had burned once in four years with sites that had burned twice in this period. The population size of the dominant native shrub Adenostoma fasciculatum was drastically reduced following fire in the 3-year sites relative to the 24-year sites. The 3-year sites had much greater alien plant cover and significantly lower plant diversity than the 24-year sites. In a separate study, repeat fires four years apart on the same sites showed that annual species increased significantly after the second fire, and alien annuals far outnumbered native annuals. Aliens included both annual grasses and annual forbs and were negatively correlated with woody plant cover. Native woody species regenerated well after the first fire but declined after the second fire, and one obligate seeding shrub was extirpated from two sites by the repeat fires. It is concluded that some fire-adapted shrublands are vulnerable to changes in fire regime, and this can lead to a loss of native diversity and put the community on a trajectory towards type conversion from a woody to an herbaceous system. Such changes result in alterations in the proportion of natives to non-natives, changes in functional types from deeply rooted shrubs to shallow rooted grasses and forbs, increased fire frequency due to the increase in fine fuels

Fire-driven alien invasion in a fire-adapted ecosystem.

Science.gov (United States)

Keeley, Jon E; Brennan, Teresa J

2012-08-01

Disturbance plays a key role in many alien plant invasions. However, often the main driver of invasion is not disturbance per se but alterations in the disturbance regime. In some fire-adapted shrublands, the community is highly resilient to infrequent, high-intensity fires, but changes in the fire regime that result in shorter fire intervals may make these communities more susceptible to alien plant invasions. This study examines several wildfire events that resulted in short fire intervals in California chaparral shrublands. In one study, we compared postfire recovery patterns in sites with different prefire stand ages (3 and 24 years), and in another study we compared sites that had burned once in four years with sites that had burned twice in this period. The population size of the dominant native shrub Adenostoma fasciculatum was drastically reduced following fire in the 3-year sites relative to the 24-year sites. The 3-year sites had much greater alien plant cover and significantly lower plant diversity than the 24-year sites. In a separate study, repeat fires four years apart on the same sites showed that annual species increased significantly after the second fire, and alien annuals far outnumbered native annuals. Aliens included both annual grasses and annual forbs and were negatively correlated with woody plant cover. Native woody species regenerated well after the first fire but declined after the second fire, and one obligate seeding shrub was extirpated from two sites by the repeat fires. It is concluded that some fire-adapted shrublands are vulnerable to changes in fire regime, and this can lead to a loss of native diversity and put the community on a trajectory towards type conversion from a woody to an herbaceous system. Such changes result in alterations in the proportion of natives to non-natives, changes in functional types from deeply rooted shrubs to shallow rooted grasses and forbs, increased fire frequency due to the increase in fine fuels

Differential Activation of Fast-Spiking and Regular-Firing Neuron Populations During Movement and Reward in the Dorsal Medial Frontal Cortex

Science.gov (United States)

Insel, Nathan; Barnes, Carol A.

2015-01-01

The medial prefrontal cortex is thought to be important for guiding behavior according to an animal's expectations. Efforts to decode the region have focused not only on the question of what information it computes, but also how distinct circuit components become engaged during behavior. We find that the activity of regular-firing, putative projection neurons contains rich information about behavioral context and firing fields cluster around reward sites, while activity among putative inhibitory and fast-spiking neurons is most associated with movement and accompanying sensory stimulation. These dissociations were observed even between adjacent neurons with apparently reciprocal, inhibitory–excitatory connections. A smaller population of projection neurons with burst-firing patterns did not show clustered firing fields around rewards; these neurons, although heterogeneous, were generally less selective for behavioral context than regular-firing cells. The data suggest a network that tracks an animal's behavioral situation while, at the same time, regulating excitation levels to emphasize high valued positions. In this scenario, the function of fast-spiking inhibitory neurons is to constrain network output relative to incoming sensory flow. This scheme could serve as a bridge between abstract sensorimotor information and single-dimensional codes for value, providing a neural framework to generate expectations from behavioral state. PMID:24700585

Avian response to fire in pine–oak forests of Great Smoky Mountains National Park following decades of fire suppression

Science.gov (United States)

Rose, Eli T.; Simons, Theodore R.

2016-01-01

Fire suppression in southern Appalachian pine–oak forests during the past century dramatically altered the bird community. Fire return intervals decreased, resulting in local extirpation or population declines of many bird species adapted to post-fire plant communities. Within Great Smoky Mountains National Park, declines have been strongest for birds inhabiting xeric pine–oak forests that depend on frequent fire. The buildup of fuels after decades of fire suppression led to changes in the 1996 Great Smoky Mountains Fire Management Plan. Although fire return intervals remain well below historic levels, management changes have helped increase the amount of fire within the park over the past 20 years, providing an opportunity to study patterns of fire severity, time since burn, and bird occurrence. We combined avian point counts in burned and unburned areas with remote sensing indices of fire severity to infer temporal changes in bird occurrence for up to 28 years following fire. Using hierarchical linear models that account for the possibility of a species presence at a site when no individuals are detected, we developed occurrence models for 24 species: 13 occurred more frequently in burned areas, 2 occurred less frequently, and 9 showed no significant difference between burned and unburned areas. Within burned areas, the top models for each species included fire severity, time since burn, or both, suggesting that fire influenced patterns of species occurrence for all 24 species. Our findings suggest that no single fire management strategy will suit all species. To capture peak occupancy for the entire bird community within xeric pine–oak forests, at least 3 fire regimes may be necessary; one applying frequent low severity fire, another using infrequent low severity fire, and a third using infrequently applied high severity fire.

A solution for two-dimensional mazes with use of chaotic dynamics in a recurrent neural network model.

Science.gov (United States)

Suemitsu, Yoshikazu; Nara, Shigetoshi

2004-09-01

Chaotic dynamics introduced into a neural network model is applied to solving two-dimensional mazes, which are ill-posed problems. A moving object moves from the position at t to t + 1 by simply defined motion function calculated from firing patterns of the neural network model at each time step t. We have embedded several prototype attractors that correspond to the simple motion of the object orienting toward several directions in two-dimensional space in our neural network model. Introducing chaotic dynamics into the network gives outputs sampled from intermediate state points between embedded attractors in a state space, and these dynamics enable the object to move in various directions. System parameter switching between a chaotic and an attractor regime in the state space of the neural network enables the object to move to a set target in a two-dimensional maze. Results of computer simulations show that the success rate for this method over 300 trials is higher than that of random walk. To investigate why the proposed method gives better performance, we calculate and discuss statistical data with respect to dynamical structure.

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.

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

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.

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

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

Directory of Open Access Journals (Sweden)

Jenia eBugaysen

2011-03-01

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

Control Of Motor Unit Firing During Step-Like Increases In Voluntary Force

Directory of Open Access Journals (Sweden)

Xiaogang eHu

2014-09-01

Full Text Available In most skeletal muscles, force is generated by a combination of motor unit (MU recruitment and increases in the firing rate of previously active MUs. Two contrasting patterns of firing rate organization have been reported. In the first pattern, the earliest recruited MUs reach the highest firing rates as force is increased, and later recruited MUs fire at lower rates. When firing rate of multiple MUs are superimposed, these rate trajectories form a concentric layered profile termed ‘onion skin’. In the second pattern, called ‘reverse onion skin’, later recruited MUs reach higher firing rates, and crossing of firing rate trajectories for recorded MUs is common (although such trajectories are assembled routinely from different trials. Our present study examined the firing rate organization of concurrently active MUs of the first dorsal interosseous muscle during serial, step-like increases in isometric abduction forces. We used a surface sensor array coupled with MU discrimination algorithms to characterize MU firing patterns. Our objective was to determine whether ‘onion skin’ profiles are contingent upon the force trajectory of the motor task, examined here using step-like increases of force output, and also whether they are manifested at different force levels.Our results revealed that the overall ‘onion skin’ firing rate profile was retained as the force level increased with each force step up to 15% MVC. However, the distribution of firing rates across MUs was compressed with increasing force, and overlapping firing rate of units were observed. This rate compression was largely due to rate saturation of the relatively high frequency discharging MUs.Our results reflect flexible firing patterns across MUs at different levels of excitation drive. It is also evident that many units did not follow all the step increases consistently. This failure to track firing rate increases at higher forces could be due to an intrinsically

The Sodium-Potassium Pump Controls the Intrinsic Firing of the Cerebellar Purkinje Neuron

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Forrest, Michael D.; Wall, Mark J.; Press, Daniel A.; Feng, Jianfeng

2012-01-01

In vitro, cerebellar Purkinje cells can intrinsically fire action potentials in a repeating trimodal or bimodal pattern. The trimodal pattern consists of tonic spiking, bursting, and quiescence. The bimodal pattern consists of tonic spiking and quiescence. It is unclear how these firing patterns are generated and what determines which firing pattern is selected. We have constructed a realistic biophysical Purkinje cell model that can replicate these patterns. In this model, Na+/K+ pump activity sets the Purkinje cell's operating mode. From rat cerebellar slices we present Purkinje whole cell recordings in the presence of ouabain, which irreversibly blocks the Na+/K+ pump. The model can replicate these recordings. We propose that Na+/K+ pump activity controls the intrinsic firing mode of cerbellar Purkinje cells. PMID:23284664

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.

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

Science.gov (United States)

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

Vastus Lateralis Motor Unit Firing Rate Is Higher in Women With Patellofemoral Pain.

Science.gov (United States)

Gallina, Alessio; Hunt, Michael A; Hodges, Paul W; Garland, S Jayne

2018-05-01

To compare neural drive, determined from motor unit firing rate, in the vastus medialis and lateralis in women with and without patellofemoral pain. Cross-sectional study. University research laboratory. Women (N=56) 19 to 35 years of age, including 36 with patellofemoral pain and 20 controls. Not applicable. Participants sustained an isometric knee extension contraction at 10% of their maximal voluntary effort for 70 seconds. Motor units (N=414) were identified using high-density surface electromyography. Average firing rate was calculated between 5 and 35 seconds after recruitment for each motor unit. Initial firing rate was the inverse of the first 3 motor unit interspike intervals. In control participants, vastus medialis motor units discharged at higher rates than vastus lateralis motor units (P=.001). This was not observed in women with patellofemoral pain (P=.78) because of a higher discharge rate of vastus lateralis compared with control participants (P=.002). No between-group differences were observed for vastus medialis (P=.93). Similar results were obtained for the initial motor unit firing rate. These findings suggest that women with patellofemoral pain have a higher neural drive to vastus lateralis but not vastus medialis, which may be a contributor of the altered patellar kinematics observed in some studies. The different neural drive may be an adaptation to patellofemoral pain, possibly to compensate for decreased quadriceps force production, or a precursor of patellofemoral pain. Copyright © 2018 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

A model of interval timing by neural integration.

Science.gov (United States)

Simen, Patrick; Balci, Fuat; de Souza, Laura; Cohen, Jonathan D; Holmes, Philip

2011-06-22

We show that simple assumptions about neural processing lead to a model of interval timing as a temporal integration process, in which a noisy firing-rate representation of time rises linearly on average toward a response threshold over the course of an interval. Our assumptions include: that neural spike trains are approximately independent Poisson processes, that correlations among them can be largely cancelled by balancing excitation and inhibition, that neural populations can act as integrators, and that the objective of timed behavior is maximal accuracy and minimal variance. The model accounts for a variety of physiological and behavioral findings in rodents, monkeys, and humans, including ramping firing rates between the onset of reward-predicting cues and the receipt of delayed rewards, and universally scale-invariant response time distributions in interval timing tasks. It furthermore makes specific, well-supported predictions about the skewness of these distributions, a feature of timing data that is usually ignored. The model also incorporates a rapid (potentially one-shot) duration-learning procedure. Human behavioral data support the learning rule's predictions regarding learning speed in sequences of timed responses. These results suggest that simple, integration-based models should play as prominent a role in interval timing theory as they do in theories of perceptual decision making, and that a common neural mechanism may underlie both types of behavior.

Introduction to spiking neural networks: Information processing, learning and applications.

Science.gov (United States)

Ponulak, Filip; Kasinski, Andrzej

2011-01-01

The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.

A neural theory of visual attention

DEFF Research Database (Denmark)

Bundesen, Claus; Habekost, Thomas; Kyllingsbæk, Søren

2005-01-01

A neural theory of visual attention (NTVA) is presented. NTVA is a neural interpretation of C. Bundesen's (1990) theory of visual attention (TVA). In NTVA, visual processing capacity is distributed across stimuli by dynamic remapping of receptive fields of cortical cells such that more processing...... resources (cells) are devoted to behaviorally important objects than to less important ones. By use of the same basic equations used in TVA, NTVA accounts for a wide range of known attentional effects in human performance (reaction times and error rates) and a wide range of effects observed in firing rates...

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

Hybrid discrete-time neural networks.

Science.gov (United States)

Cao, Hongjun; Ibarz, Borja

2010-11-13

Hybrid dynamical systems combine evolution equations with state transitions. When the evolution equations are discrete-time (also called map-based), the result is a hybrid discrete-time system. A class of biological neural network models that has recently received some attention falls within this category: map-based neuron models connected by means of fast threshold modulation (FTM). FTM is a connection scheme that aims to mimic the switching dynamics of a neuron subject to synaptic inputs. The dynamic equations of the neuron adopt different forms according to the state (either firing or not firing) and type (excitatory or inhibitory) of their presynaptic neighbours. Therefore, the mathematical model of one such network is a combination of discrete-time evolution equations with transitions between states, constituting a hybrid discrete-time (map-based) neural network. In this paper, we review previous work within the context of these models, exemplifying useful techniques to analyse them. Typical map-based neuron models are low-dimensional and amenable to phase-plane analysis. In bursting models, fast-slow decomposition can be used to reduce dimensionality further, so that the dynamics of a pair of connected neurons can be easily understood. We also discuss a model that includes electrical synapses in addition to chemical synapses with FTM. Furthermore, we describe how master stability functions can predict the stability of synchronized states in these networks. The main results are extended to larger map-based neural networks.

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

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.

Fire severity and ecosytem responses following crown fires in California shrublands.

Science.gov (United States)

Keeley, Jon E; Brennan, Teresa; Pfaff, Anne H

2008-09-01

Chaparral shrublands burn in large high-intensity crown fires. Managers interested in how these wildfires affect ecosystem processes generally rely on surrogate measures of fire intensity known as fire severity metrics. In shrublands burned in the autumn of 2003, a study of 250 sites investigated factors determining fire severity and ecosystem responses. Using structural equation modeling we show that stand age, prefire shrub density, and the shortest interval of the prior fire history had significant direct effects on fire severity, explaining > 50% of the variation in severity. Fire severity per se is of interest to resource managers primarily because it is presumed to be an indicator of important ecosystem processes such as vegetative regeneration, community recovery, and erosion. Fire severity contributed relatively little to explaining patterns of regeneration after fire. Two generalizations can be drawn: fire severity effects are mostly shortlived, i.e., by the second year they are greatly diminished, and fire severity may have opposite effects on different functional types. Species richness exhibited a negative relationship to fire severity in the first year, but fire severity impacts were substantially less in the second postfire year and varied by functional type. Much of this relationship was due to alien plants that are sensitive to high fire severity; at all scales from 1 to 1000 m2, the percentage of alien species in the postfire flora declined with increased fire severity. Other aspects of disturbance history are also important determinants of alien cover and richness as both increased with the number of times the site had burned and decreased with time since last fire. A substantial number of studies have shown that remote-sensing indices are correlated with field measurements of fire severity. Across our sites, absolute differenced normalized burn ratio (dNBR) was strongly correlated with field measures of fire severity and with fire history at a

Synchronization of motor unit firings: an epiphenomenon of firing rate characteristics not common inputs.

Science.gov (United States)

Kline, Joshua C; De Luca, Carlo J

2016-01-01

Synchronous motor unit firing instances have been attributed to anatomical inputs shared by motoneurons. Yet, there is a lack of empirical evidence confirming the notion that common inputs elicit synchronization under voluntary conditions. We tested this notion by measuring synchronization between motor unit action potential trains (MUAPTs) as their firing rates progressed within a contraction from a relatively low force level to a higher one. On average, the degree of synchronization decreased as the force increased. The common input notion provides no empirically supported explanation for the observed synchronization behavior. Therefore, we investigated a more probable explanation for synchronization. Our data set of 17,546 paired MUAPTs revealed that the degree of synchronization varies as a function of two characteristics of the motor unit firing rate: the similarity and the slope as a function of force. Both are measures of the excitation of the motoneurons. As the force generated by the muscle increases, the firing rate slope decreases, and the synchronization correspondingly decreases. Different muscles have motor units with different firing rate characteristics and display different amounts of synchronization. Although this association is not proof of causality, it consistently explains our observations and strongly suggests further investigation. So viewed, synchronization is likely an epiphenomenon, subject to countless unknown neural interactions. As such, synchronous firing instances may not be the product of a specific design and may not serve a specific physiological purpose. Our explanation for synchronization has the advantage of being supported by empirical evidence, whereas the common input does not. Copyright © 2016 the American Physiological Society.

Time fluctuation analysis of forest fire sequences

Science.gov (United States)

Vega Orozco, Carmen D.; Kanevski, Mikhaïl; Tonini, Marj; Golay, Jean; Pereira, Mário J. G.

2013-04-01

Forest fires are complex events involving both space and time fluctuations. Understanding of their dynamics and pattern distribution is of great importance in order to improve the resource allocation and support fire management actions at local and global levels. This study aims at characterizing the temporal fluctuations of forest fire sequences observed in Portugal, which is the country that holds the largest wildfire land dataset in Europe. This research applies several exploratory data analysis measures to 302,000 forest fires occurred from 1980 to 2007. The applied clustering measures are: Morisita clustering index, fractal and multifractal dimensions (box-counting), Ripley's K-function, Allan Factor, and variography. These algorithms enable a global time structural analysis describing the degree of clustering of a point pattern and defining whether the observed events occur randomly, in clusters or in a regular pattern. The considered methods are of general importance and can be used for other spatio-temporal events (i.e. crime, epidemiology, biodiversity, geomarketing, etc.). An important contribution of this research deals with the analysis and estimation of local measures of clustering that helps understanding their temporal structure. Each measure is described and executed for the raw data (forest fires geo-database) and results are compared to reference patterns generated under the null hypothesis of randomness (Poisson processes) embedded in the same time period of the raw data. This comparison enables estimating the degree of the deviation of the real data from a Poisson process. Generalizations to functional measures of these clustering methods, taking into account the phenomena, were also applied and adapted to detect time dependences in a measured variable (i.e. burned area). The time clustering of the raw data is compared several times with the Poisson processes at different thresholds of the measured function. Then, the clustering measure value

Synaptic conditions for auto-associative memory storage and pattern completion in Jensen et al.'s model of hippocampal area CA3.

Science.gov (United States)

Cheu, Eng Yeow; Yu, Jiali; Tan, Chin Hiong; Tang, Huajin

2012-12-01

Jensen et al. (Learn Memory 3(2-3):243-256, 1996b) proposed an auto-associative memory model using an integrated short-term memory (STM) and long-term memory (LTM) spiking neural network. Their model requires that distinct pyramidal cells encoding different STM patterns are fired in different high-frequency gamma subcycles within each low-frequency theta oscillation. Auto-associative LTM is formed by modifying the recurrent synaptic efficacy between pyramidal cells. In order to store auto-associative LTM correctly, the recurrent synaptic efficacy must be bounded. The synaptic efficacy must be upper bounded to prevent re-firing of pyramidal cells in subsequent gamma subcycles. If cells encoding one memory item were to re-fire synchronously with other cells encoding another item in subsequent gamma subcycle, LTM stored via modifiable recurrent synapses would be corrupted. The synaptic efficacy must also be lower bounded so that memory pattern completion can be performed correctly. This paper uses the original model by Jensen et al. as the basis to illustrate the following points. Firstly, the importance of coordinated long-term memory (LTM) synaptic modification. Secondly, the use of a generic mathematical formulation (spiking response model) that can theoretically extend the results to other spiking network utilizing threshold-fire spiking neuron model. Thirdly, the interaction of long-term and short-term memory networks that possibly explains the asymmetric distribution of spike density in theta cycle through the merger of STM patterns with interaction of LTM network.

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.

Integrating remote sensing and terrain data in forest fire modeling

Science.gov (United States)

Medler, Michael Johns

Forest fire policies are changing. Managers now face conflicting imperatives to re-establish pre-suppression fire regimes, while simultaneously preventing resource destruction. They must, therefore, understand the spatial patterns of fires. Geographers can facilitate this understanding by developing new techniques for mapping fire behavior. This dissertation develops such techniques for mapping recent fires and using these maps to calibrate models of potential fire hazards. In so doing, it features techniques that strive to address the inherent complexity of modeling the combinations of variables found in most ecological systems. Image processing techniques were used to stratify the elements of terrain, slope, elevation, and aspect. These stratification images were used to assure sample placement considered the role of terrain in fire behavior. Examination of multiple stratification images indicated samples were placed representatively across a controlled range of scales. The incorporation of terrain data also improved preliminary fire hazard classification accuracy by 40%, compared with remotely sensed data alone. A Kauth-Thomas transformation (KT) of pre-fire and post-fire Thematic Mapper (TM) remotely sensed data produced brightness, greenness, and wetness images. Image subtraction indicated fire induced change in brightness, greenness, and wetness. Field data guided a fuzzy classification of these change images. Because fuzzy classification can characterize a continuum of a phenomena where discrete classification may produce artificial borders, fuzzy classification was found to offer a range of fire severity information unavailable with discrete classification. These mapped fire patterns were used to calibrate a model of fire hazards for the entire mountain range. Pre-fire TM, and a digital elevation model produced a set of co-registered images. Training statistics were developed from 30 polygons associated with the previously mapped fire severity. Fuzzy

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.

Synaptic energy drives the information processing mechanisms in spiking neural networks.

Science.gov (United States)

El Laithy, Karim; Bogdan, Martin

2014-04-01

Flow of energy and free energy minimization underpins almost every aspect of naturally occurring physical mechanisms. Inspired by this fact this work establishes an energy-based framework that spans the multi-scale range of biological neural systems and integrates synaptic dynamic, synchronous spiking activity and neural states into one consistent working paradigm. Following a bottom-up approach, a hypothetical energy function is proposed for dynamic synaptic models based on the theoretical thermodynamic principles and the Hopfield networks. We show that a synapse exposes stable operating points in terms of its excitatory postsynaptic potential as a function of its synaptic strength. We postulate that synapses in a network operating at these stable points can drive this network to an internal state of synchronous firing. The presented analysis is related to the widely investigated temporal coherent activities (cell assemblies) over a certain range of time scales (binding-by-synchrony). This introduces a novel explanation of the observed (poly)synchronous activities within networks regarding the synaptic (coupling) functionality. On a network level the transitions from one firing scheme to the other express discrete sets of neural states. The neural states exist as long as the network sustains the internal synaptic energy.

Dynamic Information Encoding With Dynamic Synapses in Neural Adaptation

Science.gov (United States)

Li, Luozheng; Mi, Yuanyuan; Zhang, Wenhao; Wang, Da-Hui; Wu, Si

2018-01-01

Adaptation refers to the general phenomenon that the neural system dynamically adjusts its response property according to the statistics of external inputs. In response to an invariant stimulation, neuronal firing rates first increase dramatically and then decrease gradually to a low level close to the background activity. This prompts a question: during the adaptation, how does the neural system encode the repeated stimulation with attenuated firing rates? It has been suggested that the neural system may employ a dynamical encoding strategy during the adaptation, the information of stimulus is mainly encoded by the strong independent spiking of neurons at the early stage of the adaptation; while the weak but synchronized activity of neurons encodes the stimulus information at the later stage of the adaptation. The previous study demonstrated that short-term facilitation (STF) of electrical synapses, which increases the synchronization between neurons, can provide a mechanism to realize dynamical encoding. In the present study, we further explore whether short-term plasticity (STP) of chemical synapses, an interaction form more common than electrical synapse in the cortex, can support dynamical encoding. We build a large-size network with chemical synapses between neurons. Notably, facilitation of chemical synapses only enhances pair-wise correlations between neurons mildly, but its effect on increasing synchronization of the network can be significant, and hence it can serve as a mechanism to convey the stimulus information. To read-out the stimulus information, we consider that a downstream neuron receives balanced excitatory and inhibitory inputs from the network, so that the downstream neuron only responds to synchronized firings of the network. Therefore, the response of the downstream neuron indicates the presence of the repeated stimulation. Overall, our study demonstrates that STP of chemical synapse can serve as a mechanism to realize dynamical neural

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...

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...

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

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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

Optical-Correlator Neural Network Based On Neocognitron

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Chao, Tien-Hsin; Stoner, William W.

1994-01-01

Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.

Drought effects on large fire activity in Canadian and Alaskan forests

International Nuclear Information System (INIS)

Xiao Jingfeng; Zhuang Qianlai

2007-01-01

Fire is the dominant disturbance in forest ecosystems across Canada and Alaska, and has important implications for forest ecosystems, terrestrial carbon dioxide emissions and the forestry industry. Large fire activity had increased in Canadian and Alaskan forests during the last four decades of the 20th century. Here we combined the Palmer Drought Severity Index and historical large fire databases to demonstrate that Canada and Alaska forest regions experienced summer drying over this time period, and drought during the fire season significantly affected forest fire activity in these regions. Climatic warming, positive geopotential height anomalies and ocean circulation patterns were spatially and temporally convolved in causing drought conditions, which in turn enhanced fuel flammability and thereby indirectly affected fire activity. Future fire regimes will likely depend on drought patterns under global climate change scenarios

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

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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.

Soil shapes community structure through fire.

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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.

Fire severity filters regeneration traits to shape community assembly in Alaska's boreal forest.

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Teresa N Hollingsworth

Full Text Available Disturbance can both initiate and shape patterns of secondary succession by affecting processes of community assembly. Thus, understanding assembly rules is a key element of predicting ecological responses to changing disturbance regimes. We measured the composition and trait characteristics of plant communities early after widespread wildfires in Alaska to assess how variations in disturbance characteristics influenced the relative success of different plant regeneration strategies. We compared patterns of post-fire community composition and abundance of regeneration traits across a range of fire severities within a single pre-fire forest type- black spruce forests of Interior Alaska. Patterns of community composition, as captured by multivariate ordination with nonmetric multidimensional scaling, were primarily related to gradients in fire severity (biomass combustion and residual vegetation and secondarily to gradients in soil pH and regional climate. This pattern was apparent in both the full dataset (n = 87 sites and for a reduced subset of sites (n = 49 that minimized the correlation between site moisture and fire severity. Changes in community composition across the fire-severity gradient in Alaska were strongly correlated to variations in plant regeneration strategy and rooting depth. The tight coupling of fire severity with regeneration traits and vegetation composition after fire supports the hypothesis that disturbance characteristics influence patterns of community assembly by affecting the relative success of different regeneration strategies. This study further demonstrated that variations in disturbance characteristics can dominate over environmental constraints in determining early patterns of community assembly. By affecting the success of regeneration traits, changes in fire regime directly shape the outcomes of community assembly, and thus may override the effects of slower environmental change on boreal forest

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

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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

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...

Fire forbids fifty-fifty forest

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Staal, Arie; Hantson, Stijn; Holmgren, Milena; Pueyo, Salvador; Bernardi, Rafael E.; Flores, Bernardo M.; Xu, Chi; Scheffer, Marten

2018-01-01

Recent studies have interpreted patterns of remotely sensed tree cover as evidence that forest with intermediate tree cover might be unstable in the tropics, as it will tip into either a closed forest or a more open savanna state. Here we show that across all continents the frequency of wildfires rises sharply as tree cover falls below ~40%. Using a simple empirical model, we hypothesize that the steepness of this pattern causes intermediate tree cover (30‒60%) to be unstable for a broad range of assumptions on tree growth and fire-driven mortality. We show that across all continents, observed frequency distributions of tropical tree cover are consistent with this hypothesis. We argue that percolation of fire through an open landscape may explain the remarkably universal rise of fire frequency around a critical tree cover, but we show that simple percolation models cannot predict the actual threshold quantitatively. The fire-driven instability of intermediate states implies that tree cover will not change smoothly with climate or other stressors and shifts between closed forest and a state of low tree cover will likely tend to be relatively sharp and difficult to reverse. PMID:29351323

Fire forbids fifty-fifty forest.

Science.gov (United States)

van Nes, Egbert H; Staal, Arie; Hantson, Stijn; Holmgren, Milena; Pueyo, Salvador; Bernardi, Rafael E; Flores, Bernardo M; Xu, Chi; Scheffer, Marten

2018-01-01

Recent studies have interpreted patterns of remotely sensed tree cover as evidence that forest with intermediate tree cover might be unstable in the tropics, as it will tip into either a closed forest or a more open savanna state. Here we show that across all continents the frequency of wildfires rises sharply as tree cover falls below ~40%. Using a simple empirical model, we hypothesize that the steepness of this pattern causes intermediate tree cover (30‒60%) to be unstable for a broad range of assumptions on tree growth and fire-driven mortality. We show that across all continents, observed frequency distributions of tropical tree cover are consistent with this hypothesis. We argue that percolation of fire through an open landscape may explain the remarkably universal rise of fire frequency around a critical tree cover, but we show that simple percolation models cannot predict the actual threshold quantitatively. The fire-driven instability of intermediate states implies that tree cover will not change smoothly with climate or other stressors and shifts between closed forest and a state of low tree cover will likely tend to be relatively sharp and difficult to reverse.

Neural breathing pattern in newborn infants pre- and postextubation.

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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.

Faunal responses to fire in chaparral and sage scrub in California, USA

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van Mantgem, Elizabeth; Keeley, Jon E.; Witter, Marti

2015-01-01

Impact of fire on California shrublands has been well studied but nearly all of this work has focused on plant communities. Impact on and recovery of the chaparral fauna has received only scattered attention; this paper synthesizes what is known in this regard for the diversity of animal taxa associated with California shrublands and outlines the primary differences between plant and animal responses to fire. We evaluated the primary faunal modes of resisting fire effects in three categories: 1) endogenous survival in a diapause or diapause-like stage, 2) sheltering in place within unburned refugia, or 3) fleeing and recolonizing. Utilizing these patterns in chaparral and sagescrub, as well as some studies on animals in other mediterranean-climate ecosystems, we derived generalizations about how plants and animals differ in their responses to fire impacts and their post fire recovery. One consequence of these differences is that variation in fire behavior has a much greater potential to affect animals than plants. For example, plants recover from fire endogenously from soil-stored seeds and resprouts, so fire size plays a limited role in determining recovery patterns. However, animals that depend on recolonization of burned sites from metapopulations may be greatly affected by fire size. Animal recolonization may also be greatly affected by regional land use patterns that affect colonization corridors, whereas such regional factors play a minimal role in plant community recovery. Fire characteristics such as rate of spread and fire intensity do not appear to play an important role in determining patterns of chaparral and sage scrub plant recovery after fire. However, these fire behavior characteristics may have a profound role in determining survivorship of some animal populations as slow-moving, smoldering combustion may limit survivorship of animals in burrows, whereas fast-moving, high intensity fires may affect survivorship of animals in above ground refugia or

Post-fire vegetation behaviour in large burnt scars from 2005 fire season in Spain

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Bastos, A.; Gouveia, C. M.; DaCamara, C. C.; Trigo, R. M.

2012-04-01

Wildfires have a wide diversity of impacts on landscape which, in turn, depend on the interaction of fire regimes (e.g. intensity, extent, frequency) and the response of vegetation to them in short and long-terms. The increase in erosion rates and the loss of nutrients by runoff in the first months following the fire are among the major impacts of wildfires. A minimum of 30% of vegetation cover is enough to protect soils against erosion but vegetation may require a long period to reach this threshold after severe fires. Since erosion risk is strongly linked to vegetation recovery rates, post-fire vegetation monitoring becomes crucial in land management. Fire regimes in the Mediterranean have been changing in the past decades due to modifications in both socio-economic and climate patterns. Although many vegetation species in Mediterranean ecosystems are adapted to wildfires, changes in fire regime characteristics affect the ability of ecosystems to recover to their previous state. In Spain, fire is an important driver of changes in landscape composition, leading to dominance of shrubland following fire and to a major decrease of pine woodlands (Viedma et al., 2006). Remote sensing is a powerful tool in land management, allowing vegetation monitoring on large spatial scales for relatively long periods of time. In order to assess vegetation dynamics, monthly NDVI data from 1998-2009 from SPOT/VEGETATION at 1km spatial resolution over the Iberian Peninsula were used. This work focuses on 2005 fire season in Spain, which registered the highest amount of burnt area since 1994, with more than 188000 ha burnt. Burnt scars in this fire season were identified by cluster analysis. Post-fire vegetation recovery was assessed based on the monoparametric model developed by Gouveia et al. (2010) that was applied to four large scars located in different geographical settings with different land cover characteristics. While the two northern regions presented fast recovery, in the

Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.

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Naveros, Francisco; Garrido, Jesus A; Carrillo, Richard R; Ros, Eduardo; Luque, Niceto R

2017-01-01

Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under

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

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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.

Biological and geophysical feedbacks with fire in the Earth system

Science.gov (United States)

Archibald, S.; Lehmann, C. E. R.; Belcher, C. M.; Bond, W. J.; Bradstock, R. A.; Daniau, A.-L.; Dexter, K. G.; Forrestel, E. J.; Greve, M.; He, T.; Higgins, S. I.; Hoffmann, W. A.; Lamont, B. B.; McGlinn, D. J.; Moncrieff, G. R.; Osborne, C. P.; Pausas, J. G.; Price, O.; Ripley, B. S.; Rogers, B. M.; Schwilk, D. W.; Simon, M. F.; Turetsky, M. R.; Van der Werf, G. R.; Zanne, A. E.

2018-03-01

Roughly 3% of the Earth’s land surface burns annually, representing a critical exchange of energy and matter between the land and atmosphere via combustion. Fires range from slow smouldering peat fires, to low-intensity surface fires, to intense crown fires, depending on vegetation structure, fuel moisture, prevailing climate, and weather conditions. While the links between biogeochemistry, climate and fire are widely studied within Earth system science, these relationships are also mediated by fuels—namely plants and their litter—that are the product of evolutionary and ecological processes. Fire is a powerful selective force and, over their evolutionary history, plants have evolved traits that both tolerate and promote fire numerous times and across diverse clades. Here we outline a conceptual framework of how plant traits determine the flammability of ecosystems and interact with climate and weather to influence fire regimes. We explore how these evolutionary and ecological processes scale to impact biogeochemical and Earth system processes. Finally, we outline several research challenges that, when resolved, will improve our understanding of the role of plant evolution in mediating the fire feedbacks driving Earth system processes. Understanding current patterns of fire and vegetation, as well as patterns of fire over geological time, requires research that incorporates evolutionary biology, ecology, biogeography, and the biogeosciences.

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...

A complex-valued firing-rate model that approximates the dynamics of spiking networks.

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Evan S Schaffer

2013-10-01

Full Text Available Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons.

A complex-valued firing-rate model that approximates the dynamics of spiking networks.

Science.gov (United States)

Schaffer, Evan S; Ostojic, Srdjan; Abbott, L F

2013-10-01

Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons.

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.

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

Science.gov (United States)

Han, Bing; Taha, Tarek M

2010-04-01

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

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

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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.

Noise adaptation in integrate-and fire neurons.

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Rudd, M E; Brown, L G

1997-07-01

The statistical spiking response of an ensemble of identically prepared stochastic integrate-and-fire neurons to a rectangular input current plus gaussian white noise is analyzed. It is shown that, on average, integrate-and-fire neurons adapt to the root-mean-square noise level of their input. This phenomenon is referred to as noise adaptation. Noise adaptation is characterized by a decrease in the average neural firing rate and an accompanying decrease in the average value of the generator potential, both of which can be attributed to noise-induced resets of the generator potential mediated by the integrate-and-fire mechanism. A quantitative theory of noise adaptation in stochastic integrate-and-fire neurons is developed. It is shown that integrate-and-fire neurons, on average, produce transient spiking activity whenever there is an increase in the level of their input noise. This transient noise response is either reduced or eliminated over time, depending on the parameters of the model neuron. Analytical methods are used to prove that nonleaky integrate-and-fire neurons totally adapt to any constant input noise level, in the sense that their asymptotic spiking rates are independent of the magnitude of their input noise. For leaky integrate-and-fire neurons, the long-run noise adaptation is not total, but the response to noise is partially eliminated. Expressions for the probability density function of the generator potential and the first two moments of the potential distribution are derived for the particular case of a nonleaky neuron driven by gaussian white noise of mean zero and constant variance. The functional significance of noise adaptation for the performance of networks comprising integrate-and-fire neurons is discussed.

Dynamical systems, attractors, and neural circuits.

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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.

Statistical analysis and decoding of neural activity in the rodent geniculate ganglion using a metric-based inference system.

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Wei Wu

Full Text Available We analyzed the spike discharge patterns of two types of neurons in the rodent peripheral gustatory system, Na specialists (NS and acid generalists (AG to lingual stimulation with NaCl, acetic acid, and mixtures of the two stimuli. Previous computational investigations found that both spike rate and spike timing contribute to taste quality coding. These studies used commonly accepted computational methods, but they do not provide a consistent statistical evaluation of spike trains. In this paper, we adopted a new computational framework that treated each spike train as an individual data point for computing summary statistics such as mean and variance in the spike train space. We found that these statistical summaries properly characterized the firing patterns (e. g. template and variability and quantified the differences between NS and AG neurons. The same framework was also used to assess the discrimination performance of NS and AG neurons and to remove spontaneous background activity or "noise" from the spike train responses. The results indicated that the new metric system provided the desired decoding performance and noise-removal improved stimulus classification accuracy, especially of neurons with high spontaneous rates. In summary, this new method naturally conducts statistical analysis and neural decoding under one consistent framework, and the results demonstrated that individual peripheral-gustatory neurons generate a unique and reliable firing pattern during sensory stimulation and that this pattern can be reliably decoded.

Fires in large scale ventilation systems

International Nuclear Information System (INIS)

Gregory, W.S.; Martin, R.A.; White, B.W.; Nichols, B.D.; Smith, P.R.; Leslie, I.H.; Fenton, D.L.; Gunaji, M.V.; Blythe, J.P.

1991-01-01

This paper summarizes the experience gained simulating fires in large scale ventilation systems patterned after ventilation systems found in nuclear fuel cycle facilities. The series of experiments discussed included: (1) combustion aerosol loading of 0.61x0.61 m HEPA filters with the combustion products of two organic fuels, polystyrene and polymethylemethacrylate; (2) gas dynamic and heat transport through a large scale ventilation system consisting of a 0.61x0.61 m duct 90 m in length, with dampers, HEPA filters, blowers, etc.; (3) gas dynamic and simultaneous transport of heat and solid particulate (consisting of glass beads with a mean aerodynamic diameter of 10μ) through the large scale ventilation system; and (4) the transport of heat and soot, generated by kerosene pool fires, through the large scale ventilation system. The FIRAC computer code, designed to predict fire-induced transients in nuclear fuel cycle facility ventilation systems, was used to predict the results of experiments (2) through (4). In general, the results of the predictions were satisfactory. The code predictions for the gas dynamics, heat transport, and particulate transport and deposition were within 10% of the experimentally measured values. However, the code was less successful in predicting the amount of soot generation from kerosene pool fires, probably due to the fire module of the code being a one-dimensional zone model. The experiments revealed a complicated three-dimensional combustion pattern within the fire room of the ventilation system. Further refinement of the fire module within FIRAC is needed. (orig.)

Neuron's eye view: Inferring features of complex stimuli from neural responses.

Directory of Open Access Journals (Sweden)

Xin Chen

2017-08-01

Full Text Available Experiments that study neural encoding of stimuli at the level of individual neurons typically choose a small set of features present in the world-contrast and luminance for vision, pitch and intensity for sound-and assemble a stimulus set that systematically varies along these dimensions. Subsequent analysis of neural responses to these stimuli typically focuses on regression models, with experimenter-controlled features as predictors and spike counts or firing rates as responses. Unfortunately, this approach requires knowledge in advance about the relevant features coded by a given population of neurons. For domains as complex as social interaction or natural movement, however, the relevant feature space is poorly understood, and an arbitrary a priori choice of features may give rise to confirmation bias. Here, we present a Bayesian model for exploratory data analysis that is capable of automatically identifying the features present in unstructured stimuli based solely on neuronal responses. Our approach is unique within the class of latent state space models of neural activity in that it assumes that firing rates of neurons are sensitive to multiple discrete time-varying features tied to the stimulus, each of which has Markov (or semi-Markov dynamics. That is, we are modeling neural activity as driven by multiple simultaneous stimulus features rather than intrinsic neural dynamics. We derive a fast variational Bayesian inference algorithm and show that it correctly recovers hidden features in synthetic data, as well as ground-truth stimulus features in a prototypical neural dataset. To demonstrate the utility of the algorithm, we also apply it to cluster neural responses and demonstrate successful recovery of features corresponding to monkeys and faces in the image set.

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.

Warning signals for eruptive events in spreading fires.

Science.gov (United States)

Fox, Jerome M; Whitesides, George M

2015-02-24

Spreading fires are noisy (and potentially chaotic) systems in which transitions in dynamics are notoriously difficult to predict. As flames move through spatially heterogeneous environments, sudden shifts in temperature, wind, or topography can generate combustion instabilities, or trigger self-stabilizing feedback loops, that dramatically amplify the intensities and rates with which fires propagate. Such transitions are rarely captured by predictive models of fire behavior and, thus, complicate efforts in fire suppression. This paper describes a simple, remarkably instructive physical model for examining the eruption of small flames into intense, rapidly moving flames stabilized by feedback between wind and fire (i.e., "wind-fire coupling"-a mechanism of feedback particularly relevant to forest fires), and it presents evidence that characteristic patterns in the dynamics of spreading flames indicate when such transitions are likely to occur. In this model system, flames propagate along strips of nitrocellulose with one of two possible modes of propagation: a slow, structured mode, and a fast, unstructured mode sustained by wind-fire coupling. Experimental examination of patterns in dynamics that emerge near bifurcation points suggests that symptoms of critical slowing down (i.e., the slowed recovery of the system from perturbations as it approaches tipping points) warn of impending transitions to the unstructured mode. Findings suggest that slowing responses of spreading flames to sudden changes in environment (e.g., wind, terrain, temperature) may anticipate the onset of intense, feedback-stabilized modes of propagation (e.g., "blowup fires" in forests).

Neural activity in the prelimbic and infralimbic cortices of freely moving rats during social interaction: Effect of isolation rearing

Science.gov (United States)

Minami, Chihiro; Shimizu, Tomoko

2017-01-01

Sociability promotes a sound daily life for individuals. Reduced sociability is a central symptom of various neuropsychiatric disorders, and yet the neural mechanisms underlying reduced sociability remain unclear. The prelimbic cortex (PL) and infralimbic cortex (IL) have been suggested to play an important role in the neural mechanisms underlying sociability because isolation rearing in rats results in impairment of social behavior and structural changes in the PL and IL. One possible mechanism underlying reduced sociability involves dysfunction of the PL and IL. We made a wireless telemetry system to record multiunit activity in the PL and IL of pairs of freely moving rats during social interaction and examined the influence of isolation rearing on this activity. In group-reared rats, PL neurons increased firing when the rat showed approaching behavior and also contact behavior, especially when the rat attacked the partner. Conversely, IL neurons increased firing when the rat exhibited leaving behavior, especially when the partner left on its own accord. In social interaction, the PL may be involved in active actions toward others, whereas the IL may be involved in passive relief from cautionary subjects. Isolation rearing altered social behavior and neural activity. Isolation-reared rats showed an increased frequency and decreased duration of contact behavior. The increased firing of PL neurons during approaching and contact behavior, observed in group-reared rats, was preserved in isolation-reared rats, whereas the increased firing of IL neurons during leaving behavior, observed in group-reared rats, was suppressed in isolation-reared rats. This result indicates that isolation rearing differentially alters neural activity in the PL and IL during social behavior. The differential influence of isolation rearing on neural activity in the PL and IL may be one of the neural bases of isolation rearing-induced behavior. PMID:28459875

Neural activity in the prelimbic and infralimbic cortices of freely moving rats during social interaction: Effect of isolation rearing.

Science.gov (United States)

Minami, Chihiro; Shimizu, Tomoko; Mitani, Akira

2017-01-01

Sociability promotes a sound daily life for individuals. Reduced sociability is a central symptom of various neuropsychiatric disorders, and yet the neural mechanisms underlying reduced sociability remain unclear. The prelimbic cortex (PL) and infralimbic cortex (IL) have been suggested to play an important role in the neural mechanisms underlying sociability because isolation rearing in rats results in impairment of social behavior and structural changes in the PL and IL. One possible mechanism underlying reduced sociability involves dysfunction of the PL and IL. We made a wireless telemetry system to record multiunit activity in the PL and IL of pairs of freely moving rats during social interaction and examined the influence of isolation rearing on this activity. In group-reared rats, PL neurons increased firing when the rat showed approaching behavior and also contact behavior, especially when the rat attacked the partner. Conversely, IL neurons increased firing when the rat exhibited leaving behavior, especially when the partner left on its own accord. In social interaction, the PL may be involved in active actions toward others, whereas the IL may be involved in passive relief from cautionary subjects. Isolation rearing altered social behavior and neural activity. Isolation-reared rats showed an increased frequency and decreased duration of contact behavior. The increased firing of PL neurons during approaching and contact behavior, observed in group-reared rats, was preserved in isolation-reared rats, whereas the increased firing of IL neurons during leaving behavior, observed in group-reared rats, was suppressed in isolation-reared rats. This result indicates that isolation rearing differentially alters neural activity in the PL and IL during social behavior. The differential influence of isolation rearing on neural activity in the PL and IL may be one of the neural bases of isolation rearing-induced behavior.

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

Weaving and neural complexity in symmetric quantum states

Science.gov (United States)

Susa, Cristian E.; Girolami, Davide

2018-04-01

We study the behaviour of two different measures of the complexity of multipartite correlation patterns, weaving and neural complexity, for symmetric quantum states. Weaving is the weighted sum of genuine multipartite correlations of any order, where the weights are proportional to the correlation order. The neural complexity, originally introduced to characterize correlation patterns in classical neural networks, is here extended to the quantum scenario. We derive closed formulas of the two quantities for GHZ states mixed with white noise.

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

Directory of Open Access Journals (Sweden)

Silvia Scarpetta

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

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.

Increasing elevation of fire in the Sierra Nevada and implications for forest change

Science.gov (United States)

Mark W. Schwartz; Nathalie Butt; Christopher R. Dolanc; Andrew Holguin; Max A. Moritz; Malcolm P. North; Hugh D. Safford; Nathan L. Stephenson; James H. Thorne; Phillip J. van Mantgem

2015-01-01

Fire in high-elevation forest ecosystems can have severe impacts on forest structure, function and biodiversity. Using a 105-year data set, we found increasing elevation extent of fires in the Sierra Nevada, and pose five hypotheses to explain this pattern. Beyond the recognized pattern of increasing fire frequency in the Sierra Nevada since the late 20th century, we...

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.

Using neutral models to identify constraints on low-severity fire regimes.

Science.gov (United States)

Donald McKenzie; Amy E. Hessl; Lara-Karena B. Kellogg

2006-01-01

Climate, topography, fuel loadings, and human activities all affect spatial and temporal patterns of fire occurrence. Because fire is modeled as a stochastic process, for which each fire history is only one realization, a simulation approach is necessary to understand baseline variability, thereby identifying constraints, or forcing functions, that affect fire regimes...

Neural network application to diesel generator diagnostics

International Nuclear Information System (INIS)

Logan, K.P.

1990-01-01

Diagnostic problems typically begin with the observation of some system behavior which is recognized as a deviation from the expected. The fundamental underlying process is one involving pattern matching cf observed symptoms to a set of compiled symptoms belonging to a fault-symptom mapping. Pattern recognition is often relied upon for initial fault detection and diagnosis. Parallel distributed processing (PDP) models employing neural network paradigms are known to be good pattern recognition devices. This paper describes the application of neural network processing techniques to the malfunction diagnosis of subsystems within a typical diesel generator configuration. Neural network models employing backpropagation learning were developed to correctly recognize fault conditions from the input diagnostic symptom patterns pertaining to various engine subsystems. The resulting network models proved to be excellent pattern recognizers for malfunction examples within the training set. The motivation for employing network models in lieu of a rule-based expert system, however, is related to the network's potential for generalizing malfunctions outside of the training set, as in the case of noisy or partial symptom patterns

Beneficial role of noise in artificial neural networks

International Nuclear Information System (INIS)

Monterola, Christopher; Saloma, Caesar; Zapotocky, Martin

2008-01-01

We demonstrate enhancement of neural networks efficacy to recognize frequency encoded signals and/or to categorize spatial patterns of neural activity as a result of noise addition. For temporal information recovery, noise directly added to the receiving neurons allow instantaneous improvement of signal-to-noise ratio [Monterola and Saloma, Phys. Rev. Lett. 2002]. For spatial patterns however, recurrence is necessary to extend and homogenize the operating range of a feed-forward neural network [Monterola and Zapotocky, Phys. Rev. E 2005]. Finally, using the size of the basin of attraction of the networks learned patterns (dynamical fixed points), a procedure for estimating the optimal noise is demonstrated

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...

Advanced analysis of forest fire clustering

Science.gov (United States)

Kanevski, Mikhail; Pereira, Mario; Golay, Jean

2017-04-01

Analysis of point pattern clustering is an important topic in spatial statistics and for many applications: biodiversity, epidemiology, natural hazards, geomarketing, etc. There are several fundamental approaches used to quantify spatial data clustering using topological, statistical and fractal measures. In the present research, the recently introduced multi-point Morisita index (mMI) is applied to study the spatial clustering of forest fires in Portugal. The data set consists of more than 30000 fire events covering the time period from 1975 to 2013. The distribution of forest fires is very complex and highly variable in space. mMI is a multi-point extension of the classical two-point Morisita index. In essence, mMI is estimated by covering the region under study by a grid and by computing how many times more likely it is that m points selected at random will be from the same grid cell than it would be in the case of a complete random Poisson process. By changing the number of grid cells (size of the grid cells), mMI characterizes the scaling properties of spatial clustering. From mMI, the data intrinsic dimension (fractal dimension) of the point distribution can be estimated as well. In this study, the mMI of forest fires is compared with the mMI of random patterns (RPs) generated within the validity domain defined as the forest area of Portugal. It turns out that the forest fires are highly clustered inside the validity domain in comparison with the RPs. Moreover, they demonstrate different scaling properties at different spatial scales. The results obtained from the mMI analysis are also compared with those of fractal measures of clustering - box counting and sand box counting approaches. REFERENCES Golay J., Kanevski M., Vega Orozco C., Leuenberger M., 2014: The multipoint Morisita index for the analysis of spatial patterns. Physica A, 406, 191-202. Golay J., Kanevski M. 2015: A new estimator of intrinsic dimension based on the multipoint Morisita index

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

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.

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

Neural networks prove effective at NO{sub x} reduction

Energy Technology Data Exchange (ETDEWEB)

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

2000-05-01

The virtues of the Pegasus NeuSIGHT combustion optimisation software are extolled. It has been installed at more than 25 power plants and operates mostly in closed loop control. The software uses the leading neural network technology from Computer associates. The system is said to reduce emissions of nitrogen oxides, increase plant efficiency and has the potential for saving millions of dollars in capital and operating costs. The value of the system is illustrated by case studies from three coal-fired power plants. The meaning of 'neural network' is explained.

Multi-objective Optimization of Coal-fired Boiler Combustion Based on NSGA-II

OpenAIRE

Tingfang Yu; Hongzhen Zhu; Chunhua Peng

2013-01-01

NOx emission characteristics and overall heat loss model for a 300MW coal-fired boiler were established by Back Propagation (BP) neural network, by which the the functional relationship between outputs (NOx emissions & overall heat loss of the boiler) and inputs (operational parameters of the boiler) of a coal-fired boiler can be predicted. A number of field test data from a full-scale operating 300MWe boiler were used to train and verify the BP model. The NOx emissions & heat loss pr...

Wildfire patterns and landscape changes in Mediterranean oak woodlands.

Science.gov (United States)

Guiomar, N; Godinho, S; Fernandes, P M; Machado, R; Neves, N; Fernandes, J P

2015-12-01

Fire is infrequent in the oak woodlands of southern Portugal (montado) but large and severe fires affected these agro-forestry systems in 2003-2005. We hypothesised transition from forest to shrubland as a fire-driven process and investigated the links between fire incidence and montado change to other land cover types, particularly those related with the presence of pioneer communities (generically designed in this context as "transitions to early-successional communities"). We present a landscape-scale framework for assessing the probability of transition from montado to pioneer communities, considering three sets of explanatory variables: montado patterns in 1990 and prior changes from montado to early-successional communities (occurred between 1960 and 1990), fire patterns, and spatial factors. These three sets of factors captured 78.2% of the observed variability in the transitions from montado to pioneer vegetation. The contributions of fire patterns and spatial factors were high, respectively 60.6% and 43.4%, the influence of montado patterns and former changes in montado being lower (34.4%). The highest amount of explained variation in the occurrence of transitions from montado to early-successional communities was related to the pure effect of fire patterns (19.9%). Low spatial connectedness in montado landscape can increase vulnerability to changes, namely to pioneer vegetation, but the observed changes were mostly explained by fire characteristics and spatial factors. Among all metrics used to characterize fire patterns and extent, effective mesh size provided the best modelling results. Transitions from montado to pioneer communities are more likely in the presence of high values of the effective mesh size of total burned area. This cross-boundary metric is an indicator of the influence of large fires in the distribution of the identified transitions and, therefore, we conclude that the occurrence of large fires in montado increases its probability of

Synchrony-induced modes of oscillation of a neural field model

Science.gov (United States)

Esnaola-Acebes, Jose M.; Roxin, Alex; Avitabile, Daniele; Montbrió, Ernest

2017-11-01

We investigate the modes of oscillation of heterogeneous ring networks of quadratic integrate-and-fire (QIF) neurons with nonlocal, space-dependent coupling. Perturbations of the equilibrium state with a particular wave number produce transient standing waves with a specific temporal frequency, analogously to those in a tense string. In the neuronal network, the equilibrium corresponds to a spatially homogeneous, asynchronous state. Perturbations of this state excite the network's oscillatory modes, which reflect the interplay of episodes of synchronous spiking with the excitatory-inhibitory spatial interactions. In the thermodynamic limit, an exact low-dimensional neural field model describing the macroscopic dynamics of the network is derived. This allows us to obtain formulas for the Turing eigenvalues of the spatially homogeneous state and hence to obtain its stability boundary. We find that the frequency of each Turing mode depends on the corresponding Fourier coefficient of the synaptic pattern of connectivity. The decay rate instead is identical for all oscillation modes as a consequence of the heterogeneity-induced desynchronization of the neurons. Finally, we numerically compute the spectrum of spatially inhomogeneous solutions branching from the Turing bifurcation, showing that similar oscillatory modes operate in neural bump states and are maintained away from onset.

How pattern formation in ring networks of excitatory and inhibitoryspiking neurons depends on the input current regime

Directory of Open Access Journals (Sweden)

Birgit eKriener

2014-01-01

Full Text Available Pattern formation, i.e., the generation of an inhomogeneous spatial activity distribution in a dynamical system with translation invariant structure, is a well-studied phenomenon in neuronal network dynamics,specifically in neural field models. These are population models to describe the spatio-temporal dynamics of large groups of neurons in terms of macroscopic variables such as population firing rates. Though neural field models are often deduced from and equipped with biophysically meaningfulproperties, a direct mapping to simulations of individual spiking neuron populations is rarely considered. Neurons have a distinct identity defined by their action on their postsynaptic targets. In its simplest form they act either excitatorily or inhibitorily.When the distribution of neuron identities is assumed to be periodic, pattern formation can be observed, given the coupling strength is supercritical, i.e., larger than a critical weight. We find that this critical weight is strongly dependent on the characteristics of the neuronal input, i.e., depends on whether neurons are mean- orfluctuation driven, and different limits in linearizing the full non-linear system apply in order to assess stability.In particular, if neurons are mean-driven, the linearization has a very simple form and becomesindependent of both the fixed point firing rate and the variance of the input current, while in the very strongly fluctuation-driven regime the fixed point rate, as well as the input mean and variance areimportant parameters in the determination of the critical weight.We demonstrate that interestingly even in ``intermediate'' regimes, when the system is technically fluctuation-driven, the simple linearization neglecting the variance of the input can yield the better prediction of the critical couplingstrength. We moreover analyze the effects of structural randomness by rewiring individualsynapses or redistributing weights, as well as coarse-graining on pattern

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...

Analysis of accelerants and fire debris using aroma detection technology

Energy Technology Data Exchange (ETDEWEB)

Barshick, S.A.

1997-01-17

The purpose of this work was to investigate the utility of electronic aroma detection technologies for the detection and identification of accelerant residues in suspected arson debris. Through the analysis of known accelerant residues, a trained neural network was developed for classifying suspected arson samples. Three unknown fire debris samples were classified using this neural network. The item corresponding to diesel fuel was correctly identified every time. For the other two items, wide variations in sample concentration and excessive water content, producing high sample humidities, were shown to influence the sensor response. Sorbent sampling prior to aroma detection was demonstrated to reduce these problems and to allow proper neural network classification of the remaining items corresponding to kerosene and gasoline.

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

Action Potential Modulation of Neural Spin Networks Suggests Possible Role of Spin

CERN Document Server

Hu, H P

2004-01-01

In this paper we show that nuclear spin networks in neural membranes are modulated by action potentials through J-coupling, dipolar coupling and chemical shielding tensors and perturbed by microscopically strong and fluctuating internal magnetic fields produced largely by paramagnetic oxygen. We suggest that these spin networks could be involved in brain functions since said modulation inputs information carried by the neural spike trains into them, said perturbation activates various dynamics within them and the combination of the two likely produce stochastic resonance thus synchronizing said dynamics to the neural firings. Although quantum coherence is desirable and may indeed exist, it is not required for these spin networks to serve as the subatomic components for the conventional neural networks.

Development of nuclear power plant diagnosis technique using neural networks

International Nuclear Information System (INIS)

Horiguchi, Masahiro; Fukawa, Naohiro; Nishimura, Kazuo

1991-01-01

A nuclear power plant diagnosis technique has been developed, called transient phenomena analysis, which employs neural network. The neural networks identify malfunctioning equipment by recognizing the pattern of main plant parameters, making it possible to locate the cause of an abnormality when a plant is in a transient state. In a case where some piece of equipment shows abnormal behavior, many plant parameters either directly or indirectly related to that equipment change simultaneously. When an abrupt change in a plant parameter is detected, changes in the 49 main plant parameters are classified into three types and a characteristic change pattern consisting of 49 data is defined. The neural networks then judge the cause of the abnormality from this pattern. This neural-network-based technique can recognize 100 patterns that are characterized by the causes of plant abnormality. (author)

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.

Dlx proteins position the neural plate border and determine adjacent cell fates.

Science.gov (United States)

Woda, Juliana M; Pastagia, Julie; Mercola, Mark; Artinger, Kristin Bruk

2003-01-01

The lateral border of the neural plate is a major source of signals that induce primary neurons, neural crest cells and cranial placodes as well as provide patterning cues to mesodermal structures such as somites and heart. Whereas secreted BMP, FGF and Wnt proteins influence the differentiation of neural and non-neural ectoderm, we show here that members of the Dlx family of transcription factors position the border between neural and non-neural ectoderm and are required for the specification of adjacent cell fates. Inhibition of endogenous Dlx activity in Xenopus embryos with an EnR-Dlx homeodomain fusion protein expands the neural plate into non-neural ectoderm tissue whereas ectopic activation of Dlx target genes inhibits neural plate differentiation. Importantly, the stereotypic pattern of border cell fates in the adjacent ectoderm is re-established only under conditions where the expanded neural plate abuts Dlx-positive non-neural ectoderm. Experiments in which presumptive neural plate was grafted to ventral ectoderm reiterate induction of neural crest and placodal lineages and also demonstrate that Dlx activity is required in non-neural ectoderm for the production of signals needed for induction of these cells. We propose that Dlx proteins regulate intercellular signaling across the interface between neural and non-neural ectoderm that is critical for inducing and patterning adjacent cell fates.

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.

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.

Artificial neural networks in NDT

International Nuclear Information System (INIS)

Abdul Aziz Mohamed

2001-01-01

Artificial neural networks, simply known as neural networks, have attracted considerable interest in recent years largely because of a growing recognition of the potential of these computational paradigms as powerful alternative models to conventional pattern recognition or function approximation techniques. The neural networks approach is having a profound effect on almost all fields, and has been utilised in fields Where experimental inter-disciplinary work is being carried out. Being a multidisciplinary subject with a broad knowledge base, Nondestructive Testing (NDT) or Nondestructive Evaluation (NDE) is no exception. This paper explains typical applications of neural networks in NDT/NDE. Three promising types of neural networks are highlighted, namely, back-propagation, binary Hopfield and Kohonen's self-organising maps. (Author)

Neural network approach to radiologic lesion detection

International Nuclear Information System (INIS)

Newman, F.D.; Raff, U.; Stroud, D.

1989-01-01

An area of artificial intelligence that has gained recent attention is the neural network approach to pattern recognition. The authors explore the use of neural networks in radiologic lesion detection with what is known in the literature as the novelty filter. This filter uses a linear model; images of normal patterns become training vectors and are stored as columns of a matrix. An image of an abnormal pattern is introduced and the abnormality or novelty is extracted. A VAX 750 was used to encode the novelty filter, and two experiments have been examined

Application Of Artificial Neural Networks In Modeling Of Manufactured Front Metallization Contact Resistance For Silicon Solar Cells

Directory of Open Access Journals (Sweden)

Musztyfaga-Staszuk M.

2015-09-01

Full Text Available This paper presents the application of artificial neural networks for prediction contact resistance of front metallization for silicon solar cells. The influence of the obtained front electrode features on electrical properties of solar cells was estimated. The front electrode of photovoltaic cells was deposited using screen printing (SP method and next to manufactured by two methods: convectional (1. co-fired in an infrared belt furnace and unconventional (2. Selective Laser Sintering. Resistance of front electrodes solar cells was investigated using Transmission Line Model (TLM. Artificial neural networks were obtained with the use of Statistica Neural Network by Statsoft. Created artificial neural networks makes possible the easy modelling of contact resistance of manufactured front metallization and allows the better selection of production parameters. The following technological recommendations for the screen printing connected with co-firing and selective laser sintering technology such as optimal paste composition, morphology of the silicon substrate, co-firing temperature and the power and scanning speed of the laser beam to manufacture the front electrode of silicon solar cells were experimentally selected in order to obtain uniformly melted structure well adhered to substrate, of a small front electrode substrate joint resistance value. The prediction possibility of contact resistance of manufactured front metallization is valuable for manufacturers and constructors. It allows preserving the customers’ quality requirements and bringing also measurable financial advantages.

Modeling post-fire hydro-geomorphic recovery in the Waldo Canyon Fire

Science.gov (United States)

Kinoshita, Alicia; Nourbakhshbeidokhti, Samira; Chin, Anne

2016-04-01

Wildfire can have significant impacts on watershed hydrology and geomorphology by changing soil properties and removing vegetation, often increasing runoff and soil erosion and deposition, debris flows, and flooding. Watershed systems may take several years or longer to recover. During this time, post-fire channel changes have the potential to alter hydraulics that influence characteristics such as time of concentration and increase time to peak flow, flow capacity, and velocity. Using the case of the 2012 Waldo Canyon Fire in Colorado (USA), this research will leverage field-based surveys and terrestrial Light Detection and Ranging (LiDAR) data to parameterize KINEROS2 (KINematic runoff and EROSion), an event oriented, physically-based watershed runoff and erosion model. We will use the Automated Geospatial Watershed Assessment (AGWA) tool, which is a GIS-based hydrologic modeling tool that uses commonly available GIS data layers to parameterize, execute, and spatially visualize runoff and sediment yield for watersheds impacted by the Waldo Canyon Fire. Specifically, two models are developed, an unburned (Bear Creek) and burned (Williams) watershed. The models will simulate burn severity and treatment conditions. Field data will be used to validate the burned watersheds for pre- and post-fire changes in infiltration, runoff, peak flow, sediment yield, and sediment discharge. Spatial modeling will provide insight into post-fire patterns for varying treatment, burn severity, and climate scenarios. Results will also provide post-fire managers with improved hydro-geomorphic modeling and prediction tools for water resources management and mitigation efforts.

How pattern formation in ring networks of excitatory and inhibitory spiking neurons depends on the input current regime.

Science.gov (United States)

Kriener, Birgit; Helias, Moritz; Rotter, Stefan; Diesmann, Markus; Einevoll, Gaute T

2013-01-01

Pattern formation, i.e., the generation of an inhomogeneous spatial activity distribution in a dynamical system with translation invariant structure, is a well-studied phenomenon in neuronal network dynamics, specifically in neural field models. These are population models to describe the spatio-temporal dynamics of large groups of neurons in terms of macroscopic variables such as population firing rates. Though neural field models are often deduced from and equipped with biophysically meaningful properties, a direct mapping to simulations of individual spiking neuron populations is rarely considered. Neurons have a distinct identity defined by their action on their postsynaptic targets. In its simplest form they act either excitatorily or inhibitorily. When the distribution of neuron identities is assumed to be periodic, pattern formation can be observed, given the coupling strength is supracritical, i.e., larger than a critical weight. We find that this critical weight is strongly dependent on the characteristics of the neuronal input, i.e., depends on whether neurons are mean- or fluctuation driven, and different limits in linearizing the full non-linear system apply in order to assess stability. In particular, if neurons are mean-driven, the linearization has a very simple form and becomes independent of both the fixed point firing rate and the variance of the input current, while in the very strongly fluctuation-driven regime the fixed point rate, as well as the input mean and variance are important parameters in the determination of the critical weight. We demonstrate that interestingly even in "intermediate" regimes, when the system is technically fluctuation-driven, the simple linearization neglecting the variance of the input can yield the better prediction of the critical coupling strength. We moreover analyze the effects of structural randomness by rewiring individual synapses or redistributing weights, as well as coarse-graining on the formation of

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.

Neural processing of auditory signals and modular neural control for sound tropism of walking machines

DEFF Research Database (Denmark)

Manoonpong, Poramate; Pasemann, Frank; Fischer, Joern

2005-01-01

and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right....... The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it....

Improvement of the Hopfield Neural Network by MC-Adaptation Rule

Science.gov (United States)

Zhou, Zhen; Zhao, Hong

2006-06-01

We show that the performance of the Hopfield neural networks, especially the quality of the recall and the capacity of the effective storing, can be greatly improved by making use of a recently presented neural network designing method without altering the whole structure of the network. In the improved neural network, a memory pattern is recalled exactly from initial states having a given degree of similarity with the memory pattern, and thus one can avoids to apply the overlap criterion as carried out in the Hopfield neural networks.

Spiking Neurons for Analysis of Patterns

Science.gov (United States)

Huntsberger, Terrance

2008-01-01

Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological

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)

Broad-Scale Environmental Conditions Responsible for Post-Fire Vegetation Dynamics

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