CORDIC-Enhanced Digital NeuromorphicSynaptic Plasticity

In this groundbreaking research endeavor, we embark on an innovative application of the CORDIC Algorithm to propelthe development of highly efficient digital neuromorphic implementations for pair-based and triplet-based Spike TimingDependent Plasticity (STDP) rules. STDP, the cornerstone of synaptic plasticity, underpins the neural processes critical forlearning within the human brain. Our pursuit of computational efficiency unveils a series of cutting-edge approximations thataugment the performance of these implementations. Through rigorous validation in simulated environments and a compellingFPGA implementation, we demonstrate the remarkable prowess of our models in dynamically modulating synaptic weights,responsive to the intricate temporal patterns of neuronal spiking. As we delve into the intricate world of computationalneurobiology, we unlock new possibilities for pushing the boundaries of artificial intelligence, unveiling a world where machinesmimic the remarkable adaptability of the human brain.

کلیدواژه ها:

Algorithm ، spiking neural network (SNN) ، synaptic learning rule ، pair-based spike-timing-dependent synapticplasticity (PSTDP) ، Triplet-based spike-timing-dependent synaptic plasticity (TSTDP).