Computing with Neural Circuits: A Model

TLDR

The study focuses on neural circuits composed of nonlinear graded‑response model neurons arranged in networks with effectively symmetric synaptic connections, approximating biological neurons while retaining key computational properties. The authors propose a conceptual framework and minimization principle to understand computation in model neural circuits and aim to implement this model with electronic devices to create novel electronic circuits. They demonstrate that complex circuits solving biologically relevant problems can be analyzed and understood without tracking detailed dynamics.

Abstract

A new conceptual framework and a minimization principle together provide an understanding of computation in model neural circuits. The circuits consist of nonlinear graded-response model neurons organized into networks with effectively symmetric synaptic connections. The neurons represent an approximation to biological neurons in which a simplified set of important computational properties is retained. Complex circuits solving problems similar to those essential in biology can be analyzed and understood without the need to follow the circuit dynamics in detail. Implementation of the model with electronic devices will provide a class of electronic circuits of novel form and function.