Exact solutions for rate and synchrony in recurrent networks of coincidence detectors

Shawn Mikula, Ernst Niebur

Research output: Contribution to journalArticlepeer-review

Abstract

We provide analytical solutions for mean firing rates and cross-correlations of coincidence detector neurons in recurrent networks with excitatory or inhibitory connectivity, with rate-modulated steady-state spiking inputs. We use discrete-time finite-state Markov chains to represent network state transition probabilities, which are subsequently used to derive exact analytical solutions for mean firing rates and cross-correlations. As illustrated in several examples, the method can be used for modeling cortical microcircuits and clarifying single-neuron and population coding mechanisms. We also demonstrate that increasing firing rates do not necessarily translate into increasing cross-correlations, though our results do support the contention that firing rates and cross-correlations are likely to be coupled. Our analytical solutions underscore the complexity of the relationship between firing rates and cross-correlations.

Original languageEnglish (US)
Pages (from-to)2637-2661
Number of pages25
JournalNeural Computation
Volume20
Issue number11
DOIs
StatePublished - Nov 2008

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

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