Enhancement of information transmission with stochastic resonance in hippocampal CA1 neuron network

Abstract

Stochastic resonance (SR) has been shown to improve the detection of subthreshold neural signals in uncorrelated noise. It is yet unclear if and how interactions within a population of neurons can improve information processing in neural networks. In this paper, we investigate the effect of the number of neurons on information transmission in an array of hippocampal CA1 neuron models, i.e., array-enhanced SR (AESR). In computer simulations, the sub-threshold synaptic current (signal) generated by a filtered homogeneous Poisson process was applied to a distal position in each of the apical dendrites, while the background synaptic currents (uncorrelated noise) were presented to a proximal or middle point in each of the dendrites. The transmembrane potentials were recorded at one of the somas in the array of CA1 neuron models, in order to find spike firings and likewise to estimate the total and noise entropies calculated from those spike firing times. The results show that the information rate estimated at the population of the CA1 neuron models is maximized at a specific amplitude of uncorrelated noise, implying AESR. The results further show that the maximum information rate is increased as the number of neurons is increased. It is concluded that AESR can be an important role in information processing is neural systems and that the AESR is modulated by the number of neurons within the network.