Response nonlinearities in networks of spiking neurons.

Journal Article

Combining information from multiple sources is a fundamental operation performed by networks of neurons in the brain, whose general principles are still largely unknown. Experimental evidence suggests that combination of inputs in cortex relies on nonlinear summation. Such nonlinearities are thought to be fundamental to perform complex computations. However, these non-linearities are inconsistent with the balanced-state model, one of the most popular models of cortical dynamics, which predicts networks have a linear response. This linearity is obtained in the limit of very large recurrent coupling strength. We investigate the stationary response of networks of spiking neurons as a function of coupling strength. We show that, while a linear transfer function emerges at strong coupling, nonlinearities are prominent at finite coupling, both at response onset and close to saturation. We derive a general framework to classify nonlinear responses in these networks and discuss which of them can be captured by rate models. This framework could help to understand the diversity of non-linearities observed in cortical networks.

Full Text

Duke Authors

Cited Authors

  • Sanzeni, A; Histed, MH; Brunel, N

Published Date

  • September 2020

Published In

Volume / Issue

  • 16 / 9

Start / End Page

  • e1008165 -

PubMed ID

  • 32941457

Pubmed Central ID

  • 32941457

Electronic International Standard Serial Number (EISSN)

  • 1553-7358

Digital Object Identifier (DOI)

  • 10.1371/journal.pcbi.1008165

Language

  • eng

Conference Location

  • United States