Avalanches in a stochastic model of spiking neurons.
Neuronal avalanches are a form of spontaneous activity widely observed in cortical slices and other types of nervous tissue, both in vivo and in vitro. They are characterized by irregular, isolated population bursts when many neurons fire together, where the number of spikes per burst obeys a power...
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2010-01-01
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doaj-438708db2de04fcaba5e8a60cc069d622020-11-25T01:45:19ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582010-01-0167e100084610.1371/journal.pcbi.1000846Avalanches in a stochastic model of spiking neurons.Marc BenayounJack D CowanWim van DrongelenEdward WallaceNeuronal avalanches are a form of spontaneous activity widely observed in cortical slices and other types of nervous tissue, both in vivo and in vitro. They are characterized by irregular, isolated population bursts when many neurons fire together, where the number of spikes per burst obeys a power law distribution. We simulate, using the Gillespie algorithm, a model of neuronal avalanches based on stochastic single neurons. The network consists of excitatory and inhibitory neurons, first with all-to-all connectivity and later with random sparse connectivity. Analyzing our model using the system size expansion, we show that the model obeys the standard Wilson-Cowan equations for large network sizes ( neurons). When excitation and inhibition are closely balanced, networks of thousands of neurons exhibit irregular synchronous activity, including the characteristic power law distribution of avalanche size. We show that these avalanches are due to the balanced network having weakly stable functionally feedforward dynamics, which amplifies some small fluctuations into the large population bursts. Balanced networks are thought to underlie a variety of observed network behaviours and have useful computational properties, such as responding quickly to changes in input. Thus, the appearance of avalanches in such functionally feedforward networks indicates that avalanches may be a simple consequence of a widely present network structure, when neuron dynamics are noisy. An important implication is that a network need not be "critical" for the production of avalanches, so experimentally observed power laws in burst size may be a signature of noisy functionally feedforward structure rather than of, for example, self-organized criticality.http://europepmc.org/articles/PMC2900286?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Marc Benayoun Jack D Cowan Wim van Drongelen Edward Wallace |
spellingShingle |
Marc Benayoun Jack D Cowan Wim van Drongelen Edward Wallace Avalanches in a stochastic model of spiking neurons. PLoS Computational Biology |
author_facet |
Marc Benayoun Jack D Cowan Wim van Drongelen Edward Wallace |
author_sort |
Marc Benayoun |
title |
Avalanches in a stochastic model of spiking neurons. |
title_short |
Avalanches in a stochastic model of spiking neurons. |
title_full |
Avalanches in a stochastic model of spiking neurons. |
title_fullStr |
Avalanches in a stochastic model of spiking neurons. |
title_full_unstemmed |
Avalanches in a stochastic model of spiking neurons. |
title_sort |
avalanches in a stochastic model of spiking neurons. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS Computational Biology |
issn |
1553-734X 1553-7358 |
publishDate |
2010-01-01 |
description |
Neuronal avalanches are a form of spontaneous activity widely observed in cortical slices and other types of nervous tissue, both in vivo and in vitro. They are characterized by irregular, isolated population bursts when many neurons fire together, where the number of spikes per burst obeys a power law distribution. We simulate, using the Gillespie algorithm, a model of neuronal avalanches based on stochastic single neurons. The network consists of excitatory and inhibitory neurons, first with all-to-all connectivity and later with random sparse connectivity. Analyzing our model using the system size expansion, we show that the model obeys the standard Wilson-Cowan equations for large network sizes ( neurons). When excitation and inhibition are closely balanced, networks of thousands of neurons exhibit irregular synchronous activity, including the characteristic power law distribution of avalanche size. We show that these avalanches are due to the balanced network having weakly stable functionally feedforward dynamics, which amplifies some small fluctuations into the large population bursts. Balanced networks are thought to underlie a variety of observed network behaviours and have useful computational properties, such as responding quickly to changes in input. Thus, the appearance of avalanches in such functionally feedforward networks indicates that avalanches may be a simple consequence of a widely present network structure, when neuron dynamics are noisy. An important implication is that a network need not be "critical" for the production of avalanches, so experimentally observed power laws in burst size may be a signature of noisy functionally feedforward structure rather than of, for example, self-organized criticality. |
url |
http://europepmc.org/articles/PMC2900286?pdf=render |
work_keys_str_mv |
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