Neural network mechanisms underlying stimulus driven variability reduction

Abstract

It is well established that the variability of the neural activity across trials, as measured by the Fano factor, is elevated. This/nfact poses limits on information encoding by the neural activity. However, a series of recent neurophysiological experiments/nhave changed this traditional view. Single cell recordings across a variety of species, brain areas, brain states and stimulus/nconditions demonstrate a remarkable reduction of the neural variability when an external stimulation is applied and when/nattention is allocated towards a stimulus within a neuron’s receptive field, suggesting an enhancement of information/nencoding. Using an heterogeneously connected neural network model whose dynamics exhibits multiple attractors, we/ndemonstrate here how this variability reduction can arise from a network effect. In the spontaneous state, we show that the/nhigh degree of neural variability is mainly due to fluctuation-driven excursions from attractor to attractor. This occurs when,/nin the parameter space, the network working point is around the bifurcation allowing multistable attractors. The application/nof an external excitatory drive by stimulation or attention stabilizes one specific attractor, eliminating in this way the/ntransitions between the different attractors and resulting in a net decrease in neural variability over trials. Importantly, nonresponsive/nneurons also exhibit a reduction of variability. Finally, this reduced variability is found to arise from an increased/nregularity of the neural spike trains. In conclusion, these results suggest that the variability reduction under stimulation and/nattention is a property of neural circuits.