Molecular Spiking Neuron System Based on Spike Timing-Dependent Plasticity

Spike-Timing-Dependent Plasticity (STDP) is a biologically inspired learning mechanism that allows neurons to dynamically adjust synaptic weights based on the temporal relationship of input signals, thereby capturing correlations and patterns during learning. This paper introduces a novel molecular spiking neuron system designed to emulate synaptic weight adjustments in biological neural networks. Built upon the STDP, the system models information transfer between spiking neurons and the processing of input signals. Preliminary results indicate that the system exhibits intrinsic learning capabilities. First, a Chemical Reaction Network (CRN) is developed, comprising five functional modules: input, activation, inhibition, learning, and forgetting. The system successfully implements Long-Term Potentiation (LTP) and Long-Term Depression (LTD), providing greater flexibility in learning rules. Furthermore, by encoding spike input signals in terms of frequency or temporal sequence, the system adaptively adjusts synaptic weights, showcasing its potential for autonomous learning tasks such as temporal correlation detection and image classification.