Modulated Spike-Time Dependent Plasticity (Stdp)-Based Learning for Spiking Neural Network (Snn): A Review

Computational models called Spiking Neural Networks (SNNs) are modelled after the intricate information processing discovered in the brain. A key learning principle, spike-time dependent plasticity (STDP), controls how the temporal connection between pre- and post-synaptic spikes affects synaptic weight changes. STDP is a Hebbian learning rule used in training algorithms for SNNs. SNNs encode information in the precise timing of spikes. Modified variants of STDP have recently evolved to improve the learning and adaptation of SNNs. These versions incorporate extra elements like neuromodulators and dendritic processing. This review focuses on the underlying principles, experimental results, and computational models to provide an in-depth overview of the developments in modulated STDP-based learning for SNNs. It also deals with the difficulties of modified STDP, such as computational complexity, parameter optimisation, scalability, and the quest for biological plausibility. This review is invaluable for researchers and practitioners interested in creating practical and biologically plausible learning algorithms for modulated STDP.