Computational Account of Spontaneous Activity as a Signature of Predictive Coding.

Veronika Koren,Sophie Denève,Kim T Blackwell

doi:10.1371/journal.pcbi.1005355

Abstract

Spontaneous activity is commonly observed in a variety of cortical states. Experimental evidence suggested that neural assemblies undergo slow oscillations with Up ad Down states even when the network is isolated from the rest of the brain. Here we show that these spontaneous events can be generated by the recurrent connections within the network and understood as signatures of neural circuits that are correcting their internal representation. A noiseless spiking neural network can represent its input signals most accurately when excitatory and inhibitory currents are as strong and as tightly balanced as possible. However, in the presence of realistic neural noise and synaptic delays, this may result in prohibitively large spike counts. An optimal working regime can be found by considering terms that control firing rates in the objective function from which the network is derived and then minimizing simultaneously the coding error and the cost of neural activity. In biological terms, this is equivalent to tuning neural thresholds and after-spike hyperpolarization. In suboptimal working regimes, we observe spontaneous activity even in the absence of feed-forward inputs. In an all-to-all randomly connected network, the entire population is involved in Up states. In spatially organized networks with local connectivity, Up states spread through local connections between neurons of similar selectivity and take the form of a traveling wave. Up states are observed for a wide range of parameters and have similar statistical properties in both active and quiescent state. In the optimal working regime, Up states are vanishing, leaving place to asynchronous activity, suggesting that this working regime is a signature of maximally efficient coding. Although they result in a massive increase in the firing activity, the read-out of spontaneous Up states is in fact orthogonal to the stimulus representation, therefore interfering minimally with the network function.

Highlights

A growing amount of experimental evidence suggests a complex interaction between stimulus-driven and spontaneous activity [1,2,3]
We refer to working regimes in the presence of stimuli as active states and to the working regimes in the absence of external drive as quiescent states (Table 1)
One of the fundamental concepts on how neurons in the brain might encode behaviorally relevant variables is brought by predictive coding

Summary

Introduction

A growing amount of experimental evidence suggests a complex interaction between stimulus-driven and spontaneous activity [1,2,3]. In sensory cortices of awake behaving animals, neural activity is present both during periods when the neural population is driven by sensory stimuli, as well as in absence of those. We refer to working regimes in the presence of stimuli as active states and to the working regimes in the absence of external drive as quiescent states (Table 1). In the absence of external drive, 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 [1]. Spontaneous Up states in a variety of cortical states share many of the statistical properties of stimulus-driven spiking responses [4]

Methods

Results

Conclusion