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Quiescent state activity as a signature of efficient coding
Veronika Koren¹ and ² Sophie Denève³ and David Barrett ⁴
¹Neural Information Processing Group, Technische Universität Berlin, Berlin, Germany ²Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany ³Group for Neural Theory, Departement des Etudes Cognitives, Ecole Normale Supérieure, Paris, France ⁴Computational and Biological Learning, Information Engineering Division, University of Cambridge, Cambridge, UK

A growing body of experimental evidence suggests that there is a complex interaction between stimulus-driven and spontaneous activity in the cortex [1]. In primary sensory cortices of awake behaving animals, multi-electrode recordings show neural responses during periods when the neural population is driven by a sensory stimulus, as well as during periods of quite-wakefulness, when no sensory stimulus is provided to the animal. During quite-wakefulness, the population activity can take the form of characteristic synchronized bursts of spiking activity, or up-states, interspersed by periods of silence, or down-states. Why does such activity occur and what could be its functional role? Previous mechanistic explanations have proposed that spontaneous activity is due to a dynamic interplay of local excitation, adaptation and inhibition [2]. Another mechanistic explanation is that "up" states could be triggered by modulations from distal areas, either top-down or subcortical, like a "gate" opening and closing [1]. However, the computational role of spontaneous activity remains a mystery.

We propose that up-states appear in E/I networks that are optimised for error correction. In recent work, we showed that E/I balance can be understood as a form of optimal predictive coding: by maintaining a tight balance, a population of neurons monitors its own coding error [4]. We find that when such networks are inactive, noise can induce neural spiking. The error correcting mechanisms of the network then respond to these noise-induced spikes by producing additional spikes in an attempt to correct the error. This error-correcting response appears as up-state of spiking activity. The network remains in an up-state, propagating the error signal throughout the network, until it manages to resolve the noise-induced error with an appropriate spike-train. Once the error has been resolved, the network activity returns to a down-state. According to this computational view, up-states are an attempt to correct errors induced by noise. Finally, we investigate the properties of these up-states, such as the distribution of the duration of up-states and the distribution of time epochs between up-states (inter-burst intervals) and we explore the E/I network parameters that determine the shape of this distribution.

[1] A. Luczak, P. Bartho , K.D. Harris, J.Neurosci., 33(4), (2013).
[2] A. Destexhe, J. Comput Neurosci. 27(3):493-506, (2009).
[3] C. van Vreeswijk, H. Sompolinsky, Science. 274(5293):1724-1726, (1996).
[4] M. Boerlin, C.K. Machens, S. Denève, PLoS Comput Biol. 9(11), (2013).