Mutual Information for Neural Communication With Spike-Time Dependent Plasticity and Consolidation Effect

Abstract

Neural communication is a promising communication technique in future nano-scale applications. In neural communication systems, neurons are able to change their synaptic strength over time, which is known as synaptic plasticity. The plasticity may affect the synaptic strength, and hence have significant influence on information transmission efficiency in neural communications. In this paper, we focus on a neural communication system under synaptic plasticity including vesicle release and post-synaptic spike generation processes. Specifically, one typical learning rule of the synaptic plasticity, i.e., spike-time dependent plasticity (STDP), and the related consolidation effect are considered, where the synaptic weights can be enhanced or decreased depending on temporal correlations between presynaptic spike arrival and postsynaptic firing. The mutual information for the neural communication system is theoretically analyzed considering the influence of STDP, which is also evaluated through simulations under various factors. It is observed that STDP can increase the mutual information between synapses under realistic models of vesicle release probabilities and the synaptic noise. When the initial synaptic strength is relatively large, the consolidation effect helps improve the communication rates under both single-input single-output (SISO) and multi-input single-output (MISO) channels.