Regulation of spike propagation in feedforward neural networks through short-term synaptic plasticity

Abstract

Both factors, multilayer Feedforward Neural Networks (FFNs) and short-term synaptic plasticity (STP), are considered crucial in the transmission and processing of neural signals. In this study, a 10-layer FFN was constructed to study the impact of STP on neuronal activity propagation. Neurons within the same layer do not have direct connections; instead, neurons between adjacent layers are randomly connected with a specific probability. The findings indicate that synaptic plasticity can regulate the network’s firing rate, synchronization, and output firing rate gain. Notably, short-term facilitation (STF) allows the network to exhibit high-pass filtering, while short-term depression (STD) achieves low-pass filtering. Combining STF and STD synapses in the FFN broadens the range of input firing rates effectively transmitted by the network. Increasing the proportion of STD-dominated synapses enhances the gain of low firing rate signals but reduces the gain of high firing rate signals. Adjusting the mix of synapses enables the network to implement bandpass filtering and control firing rate gain. These results underscore the effectiveness of modulating short-term synaptic plasticity in regulating neural activity propagation in FFNs.