The Asynchronous State in Cortical Circuits

TLDR

Correlated spiking is common in cortical circuits, yet its functional role is debated because correlations may arise from shared inputs and could constrain decoding. The study demonstrates that recurrent neural networks can produce an asynchronous state with near‑zero mean spiking correlations even when shared input is strong. This asynchronous state emerges when excitatory and inhibitory population fluctuations track each other, generating negative synaptic current correlations that cancel the effect of shared input. Experimental recordings from rodent neocortex confirmed near‑zero mean correlations, prompting a reevaluation of the origins and functional impact of observed correlations.

Abstract

Correlated spiking is often observed in cortical circuits, but its functional role is controversial. It is believed that correlations are a consequence of shared inputs between nearby neurons and could severely constrain information decoding. Here we show theoretically that recurrent neural networks can generate an asynchronous state characterized by arbitrarily low mean spiking correlations despite substantial amounts of shared input. In this state, spontaneous fluctuations in the activity of excitatory and inhibitory populations accurately track each other, generating negative correlations in synaptic currents which cancel the effect of shared input. Near-zero mean correlations were seen experimentally in recordings from rodent neocortex in vivo. Our results suggest a reexamination of the sources underlying observed correlations and their functional consequences for information processing.