A VLSI approach to the implementation of additive and shunting neural networks

Abstract

Biologically inspired VLSI circuits are proposed which can be particularized to approximate the real-time dynamics of either additive or shunting neural models. Analog inputs to these circuits are represented by short spikes and, both, their transient and steady-state behaviours depend only on process-independent local ratios. The paper includes simulation results and experimental measures of a CMOS prototype, which illustrate the utility and the feasibility of the proposed VLSI approach.