The Asynchronous State in Cortical Circuits

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 re-examination of the sources underlying observed correlations and their functional consequences for information processing. The spiking activity of neurons is often correlated within local cortical populations (1-4). While correlations could be a signature of active information processing (5,6), they can also impair the estimation of information conveyed by the firing rates of neural populations (2,7,8) and might limit the efficiency of an organism for performing sensory discriminations (2,7). Under certain conditions correlated spiking is an inevitable consequence of shared pre-synaptic input (9,10). In general, however, the overall contribution of shared input to correlation magnitudes measured in vivo is unclear, as measured correlations could reflect mostly covariations in activity due to cognitive or external variables outside the control of the experimenter (11-13). To investigate the relationship between correlations and shared input, we studied theoretically the correlation structures characteristic of densely connected recurrent networks. We start by considering how the correlation between a single neuronal pair depends on the fraction p of shared inputs and the degree rin to which the inputs are themselves correlated. The effect of shared input can be isolated by considering presynaptic neurons that fire independently (rin = 0). Both excitatory (E) and inhibitory (I) shared inputs cause positive correlations of a moderate magnitude in the synaptic input and spiking activity of the postsynaptic pair (Fig. 1A-B) (9, 14). Spiking correlations rin between inputs, on the other hand, have a major impact on the output correlation rout of the post-synaptic pair. When all inputs are E, very weak input correlations give rise to strongly correlated synaptic currents and †To whom correspondence should be addressed; arenart@andromeda.rutgers. edu. 6New address: Departments of Bioengineering, Electrical and Electronic Engineering, Imperial College, London SW7 2AZ, UK *These authors contributed equally to this work. NIH Public Access Author Manuscript Science. Author manuscript; available in PMC 2010 April 29. Published in final edited form as: Science. 2010 January 29; 327(5965): 587–590. doi:10.1126/science.1179850. N IH PA Athor M anscript N IH PA Athor M anscript N IH PA Athor M anscript output spikes (Fig. 1C). This occurs because, when p and rin are small, the correlation c of the two input currents is approximately equal to