Insights into the Communication Performance of Interneuron Synaptic Interactions

This paper presents interneuron synaptic communication models with relay synaptic neuron (RSN) positioned in the synaptic gap. Utilizing a time-slotted channel framework, each slot transmits a binary bit from the presynaptic neuron (PrSN) to the postsynaptic neuron (PoSN). Additionally, we analyze the end-to-end probability of error and the maximum achievable rate (MAR), accounting for inter-symbol interference (ISI) and noise effects.. RSNs improve transmission reliability and efficiency by mitigating signal degradation and enhancing the signal-to-noise ratio, underscoring their critical role in synaptic communication. All analytical results are validated through Monte Carlo simulations, providing strong empirical support for the effectiveness of RSNs in enhancing synaptic performance.