Enhancement of Information Transmission with Stochastic Resonance: Influence of Stimulating Position in Hippocampal CA1 Neuron Models

Abstract

Stochastic resonance (SR) has been shown to enhance the signal to noise ratio or detection of signals in neurons. It is not yet clear how this effect of SR on the signal to noise ratio affects signal processing in neural networks. In this paper, we test the hypothesis that SR can improve information transmission in which sub-threshold stimuli are driven to distal positions on the dendritic trees of hippocampal CA1 neuron models. From spike firing times recorded at the soma, the inter spike intervals were generated and then "total" and "noise" entropies were estimated to obtain the mutual information and information rate of the spike trains. The simulation results show that the information rate reached a maximum value at a specific amplitude of the background noise in which sub-threshold stimuli were driven to distal positions on dendritic trees, while the information rate decreased as the noise intensity increased in which supra-threshold stimuli were driven to a proximal position. It is implied that SR can play a key role in improving the information transmission in the case of the sub-threshold input located at distal positions on the dendritic trees