A Novel Generalized Hardware-Efficient Neuron Model Based on Asynchronous CA Dynamics and Its Biologically Plausible On-FPGA Learnings

This brief presents a novel generalized neuron model (i.e., soma-dendrite-spine model) the dynamics of which is described by an asynchronous cellular automaton. It is shown that the neuron model and its network models can realize three kinds of typical biologically plausible learnings. These models are implemented in a field programmable gate array (FPGA) and experiments validate operations of the learnings. It is then shown that the presented models consume fewer hardware resources (e.g., less than 37% FPGA slices) and lower power (e.g., less than 39% power) compared to conventional models.