Neuromorphic implementation of adaptive exponential Integrate and Fire neuron

Abstract

Today's intelligent systems are less efficient by a factor of a million to a billion in complex environments, when compared to biological system. For intelligent system to be useful, they must compete with biological systems. Recent research on Neuromorphic systems, introduces very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures. A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change. In this paper, a neuromorphic implementation of silicon neuron circuit that mimics the behavior of biological neuron using analog VLSI was presented. The simulated results are compared with the biological neuron and their performances are tabulated.