Decorrelation by recurrent inhibition in heterogeneous neural circuits

Alberto Bernacchia, Xiao Jing Wang

Research output: Contribution to journalLetterpeer-review


The activity of neurons is correlated, and this correlation affects how the brain processes information. We study the neural circuit mechanisms of correlations by analyzing a network model characterized by strong and heterogeneous interactions: excitatory input drives the fluctuations of neural activity,which are counterbalanced by inhibitory feedback. In particular, excitatory input tends to correlate neurons, while inhibitory feedback reduces correlations.We demonstrate that heterogeneity of synaptic connections is necessary for this inhibition of correlations. We calculate statistical averages over the disordered synaptic interactions and apply our findings to both a simple linear model and a more realistic spiking network model.We find that correlations at zero time lag are positive and of magnitude K-1/2 , where K is the number of connections to a neuron. Correlations at longer timescales are of smaller magnitude, of order K-1, implying that inhibition of correlations occurs quickly, on a timescale of K-1/2 . The small magnitude of correlations agrees qualitativelywith physiological measurements in the cerebral cortex and basal ganglia. The model could be used to study correlations in brain regions dominated by recurrent inhibition, such as the striatum and globus pallidus.

Original languageEnglish (US)
Pages (from-to)1732-1767
Number of pages36
JournalNeural computation
Issue number7
StatePublished - 2013

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience


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