Abstract
Learning from the robust mechanism of the biological nervous system is critical for creating reliable neuromorphic hardware. The homeostatic inhibition plasticity rule is a robust biological mechanism to balance Hebbian plasticity and resist external environmental disturbances and local damage. It plays an essential role in maintaining the homeostatic sparse firing patterns of the nervous system. This paper imitates this mechanism and provides a fast homeostatic inhibitory plasticity rule circuit with a memristive synapse. Firstly, the design method and principle of the circuit are demonstrated. Secondly, the function of the circuit was verified in PSpice© using a commercial Knowm memristor as a synapse. The PSpice© simulation results show that the circuit can achieve a weight update curve similar to the biological homeostatic inhibitory plasticity rule, and the time scale of the circuit is improved by a factor of 1000 compared to that of the biological nervous system. Furthermore, the circuit has wide applicability due to the tunable qualities of the homeostatic learning window, scaling factor, and homeostatic factor. This study provides new opportunities for building fast and reliable neuromorphic hardware.
Funder
The National Defense Basic Scientific Research Plan of China
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
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