Emergence of Irregular Activity in Networks of Strongly Coupled Conductance-Based Neurons
Abstract
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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https://hdl.handle.net/10161/25455Published Version (Please cite this version)
10.1103/PhysRevX.12.011044Publication Info
Sanzeni, A; Histed, MH; & Brunel, N (2022). Emergence of Irregular Activity in Networks of Strongly Coupled Conductance-Based
Neurons. Physical Review X, 12(1). 10.1103/PhysRevX.12.011044. Retrieved from https://hdl.handle.net/10161/25455.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Nicolas Brunel
Duke School of Medicine Distinguished Professor in Neuroscience
We use theoretical models of brain systems to investigate how they process and learn
information from their inputs. Our current work focuses on the mechanisms of learning
and memory, from the synapse to the network level, in collaboration with various experimental
groups. Using methods fromstatistical physics, we have shown recently that the synapticconnectivity
of a network that maximizes storage capacity reproducestwo key experimentally observed
features: low connection proba

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