TY - GEN AB - <p>Neuronal avalanches are a form of spontaneous activity widely observed in cortical slices and other types of nervous tissue, both <em>in vivo</em> and <em>in vitro</em>. 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 (<span class="inline-formula"><img src="article/file?type=thumbnail&id=10.1371/journal.pcbi.1000846.e001" loading="lazy" class="inline-graphic" /></span> 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.</p> AD - University of Chicago AD - University of Chicago AD - University of Chicago AD - University of Chicago AU - Benayoun, Marc AU - Cowan, Jack D. AU - van Drongelen, Wim AU - Wallace, Edward DA - 2010-07-08 ID - 10224 JF - PLOS Computational Biology L1 - https://knowledge.uchicago.edu/record/10224/files/journal.pcbi.1000846.zip L1 - https://knowledge.uchicago.edu/record/10224/files/journal.pcbi.1000846.pdf L2 - https://knowledge.uchicago.edu/record/10224/files/journal.pcbi.1000846.zip L2 - https://knowledge.uchicago.edu/record/10224/files/journal.pcbi.1000846.pdf L4 - https://knowledge.uchicago.edu/record/10224/files/journal.pcbi.1000846.zip L4 - https://knowledge.uchicago.edu/record/10224/files/journal.pcbi.1000846.pdf LA - eng LK - https://knowledge.uchicago.edu/record/10224/files/journal.pcbi.1000846.zip LK - https://knowledge.uchicago.edu/record/10224/files/journal.pcbi.1000846.pdf N2 - <p>Neuronal avalanches are a form of spontaneous activity widely observed in cortical slices and other types of nervous tissue, both <em>in vivo</em> and <em>in vitro</em>. 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 (<span class="inline-formula"><img src="article/file?type=thumbnail&id=10.1371/journal.pcbi.1000846.e001" loading="lazy" class="inline-graphic" /></span> 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.</p> PY - 2010-07-08 T1 - Avalanches in a Stochastic Model of Spiking Neurons TI - Avalanches in a Stochastic Model of Spiking Neurons UR - https://knowledge.uchicago.edu/record/10224/files/journal.pcbi.1000846.zip UR - https://knowledge.uchicago.edu/record/10224/files/journal.pcbi.1000846.pdf Y1 - 2010-07-08 ER -