Stochastic Resonance Can Enhance Information Transmission in Neural Networks

Abstract

Stochastic resonance (SR) is a noise-induced phenomenon whereby signal detection can be improved by the addition of background noise in nonlinear systems. SR can also improve the transmission of information within single neurons. Since information processing in the brain is carried out by neural networks and noise is present throughout the brain, the hypothesis that noise and coupling play an important role in the control of information processing within a population of neurons to control was tested. Using computer simulations, we investigate the effect of noise on the transmission of information in an array of neurons, known as array-enhanced SR (AESR) in an interconnected population of hippocampal neurons. A subthreshold synaptic current (signal) modeled by a filtered homogeneous Poisson process was applied to a distal position in each of the apical dendrites, while background synaptic signals (uncorrelated noise) were presented to the midpoint in the basal dendrite. The transmembrane potentials were recorded in each cell of an array of CA1 neuron models, in order to determine spike firing times and to estimate the total and noise entropies from the spike firing times. The results show that the mutual information is maximized for a specific amplitude of uncorrelated noise, implying the presence of AESR. The results also show that the maximum mutual information increases with increased numbers of neurons and the strength of connections. Moreover, the relative levels of excitation and inhibition modulate the mutual information transfer. It is concluded that uncorrelated noise can enhance information transmission of subthreshold synaptic input currents in a population of hippocampal CA1 neuron models. Therefore, endogenous neural noise could play an important role in neural tissue by modulating the transfer of information across the network.