Balanced input allows optimal encoding in a stochastic binary neural network model: an analytical study

Abstract

Recent neurophysiological experiments have demonstrated a remarkable effect of attention on the underlying neural/nactivity that suggests for the first time that information encoding is indeed actively influenced by attention. Single cell/nrecordings show that attention reduces both the neural variability and correlations in the attended condition with respect/nto the non-attended one. This reduction of variability and redundancy enhances the information associated with the/ndetection and further processing of the attended stimulus. Beyond the attentional paradigm, the local activity in a neural/ncircuit can be modulated in a number of ways, leading to the general question of understanding how the activity of such/ncircuits is sensitive to these relatively small modulations. Here, using an analytically tractable neural network model, we/ndemonstrate how this enhancement of information emerges when excitatory and inhibitory synaptic currents are balanced./nIn particular, we show that the network encoding sensitivity -as measured by the Fisher information- is maximized at the/nexact balance. Furthermore, we find a similar result for a more realistic spiking neural network model. As the regime of/nbalanced inputs has been experimentally observed, these results suggest that this regime is functionally important from an/ninformation encoding standpoint.