Digital Multiplierless Implementation of Biological Adaptive-Exponential Neuron Model

Abstract

High-accuracy implementation of biological neural networks is a computationally expensive task, specially, for large-scale simulations of neuromorphic algorithms. This paper proposes a set of models for biological spiking neurons, which are efficiently implementable on digital platforms. Proposed models can reproduce different biological behaviors with a high precision. The proposed models are investigated, in terms of digital implementation feasibility and costs, targeting low-cost hardware implementation. Hardware synthesis and physical implementations on a field-programmable gate array show that the proposed models can produce biological behavior of different types of neurons with higher performance and considerably lower implementation costs compared with the original model.