Scientific Understanding of Consciousness
Consciousness as an Emergent Property of Thalamocortical Activity

Asynchronous State in Cortical Circuits

 

Science 29 January 2010: Vol. 327. no. 5965, pp. 587 - 590

The Asynchronous State in Cortical Circuits

Alfonso Renart,¹ Jaime de la Rocha,^1,2 Peter Bartho,^1,3 Liad Hollender,¹ Néstor Parga,⁴ Alex Reyes,² Kenneth D. Harris^1,5

¹ Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA.
² Center for Neural Science, New York University, New York, NY 10003, USA.
³ Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest 1083, Hungary.
⁴ Departamento de Física Teórica, Universidad Autónoma de Madrid, Madrid 28049, Spain.
⁵ Smilow Research Center, New York University Medical School, New York, NY 10016, USA.

(paraphrase)

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.

To investigate the relation 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 r_in to which the inputs are themselves correlated. The effect of shared input can be isolated by considering presynaptic neurons that fire independently (r_in = 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

Correlations between E and I inputs thus decorrelate the synaptic currents to postsynaptic neurons.

Correlations in the asynchronous state do not qualitatively constrain averaging of activity across neural populations.

Asynchronous activity persists in the presence of shared input because of a spontaneously generated tracking of fluctuations in the population-averaged instantaneous activities of the E and I neurons.

We demonstrate theoretically that recurrent network dynamics can lead to an active decorrelation of synaptic currents, resulting in a state of arbitrarily low mean correlation. We therefore conclude that shared input does not inevitably cause correlated activity. By preventing uncontrolled network-wide synchrony, this mechanism generates a background of weakly correlated spiking, as required for efficient information processing based on either firing rates or coordinated spike timing patterns.

Both simulations and in vivo recordings showed a wide distribution of correlations under stationary conditions. In nonstationary conditions, global activity modulations can result in positively biased correlations, but correlations around the mean activity imposed by these modulations can still be extremely small.

Whether "residual" correlations of this nature will have a strong impact on coding will depend on the extent to which downstream networks are able to disambiguate modulations in activity due to different sources. In either case, we suggest that cortical circuitry does not itself constitute an irreducible source of "noise."

(end of paraphrase)

 

 

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