Emergence of Irregular Activity in Networks of Strongly Coupled Conductance-Based Neurons.
Cortical neurons are characterized by irregular firing and a broad distribution of rates. The balanced state model explains these observations with a cancellation of mean excitatory and inhibitory currents, which makes fluctuations drive firing. In networks of neurons with current-based synapses, the balanced state emerges dynamically if coupling is strong, i.e., if the mean number of synapses per neuron K is large and synaptic efficacy is of the order of 1 / K . When synapses are conductance-based, current fluctuations are suppressed when coupling is strong, questioning the applicability of the balanced state idea to biological neural networks. We analyze networks of strongly coupled conductance-based neurons and show that asynchronous irregular activity and broad distributions of rates emerge if synaptic efficacy is of the order of 1/ log(K). In such networks, unlike in the standard balanced state model, current fluctuations are small and firing is maintained by a drift-diffusion balance. This balance emerges dynamically, without fine-tuning, if inputs are smaller than a critical value, which depends on synaptic time constants and coupling strength, and is significantly more robust to connection heterogeneities than the classical balanced state model. Our analysis makes experimentally testable predictions of how the network response properties should evolve as input increases.
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- 51 Physical sciences
- 0206 Quantum Physics
- 0204 Condensed Matter Physics
- 0201 Astronomical and Space Sciences
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Location
Related Subject Headings
- 51 Physical sciences
- 0206 Quantum Physics
- 0204 Condensed Matter Physics
- 0201 Astronomical and Space Sciences