cordic-enhanced digital neuromorphic synaptic plasticity

In this groundbreaking research endeavor, we embark on an innovative application of the cordic algorithm to propel the development of highly efficient digital neuromorphic implementations for pair-based and triplet-based spike timing dependent plasticity (stdp) rules. stdp, the cornerstone of synaptic plasticity, underpins the neural processes critical for learning within the human brain. our pursuit of computational efficiency unveils a series of cutting-edge approximations that augment the performance of these implementations. through rigorous validation in simulated environments and a compelling fpga 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 computational neurobiology, we unlock new possibilities for pushing the boundaries of artificial intelligence, unveiling a world where machines mimic the remarkable adaptability of the human brain.