Flexible Logic from Neuronal Dynamics

TL;DR

This paper introduces two innovative methods for implementing logic operations using neuronal dynamics, leveraging the time dimension for programming and data representation, with explicit examples demonstrating their effectiveness.

Contribution

The paper proposes two novel time-based neuronal logic methods: one using sampling at different moments and another employing delayed neural responses, advancing neural computation techniques.

Findings

Both methods successfully perform logic operations.

Explicit examples validate the proposed approaches.

Time-based neuronal logic offers new computational possibilities.

Abstract

We present two novel methods for performing logic operations. Our methods are based on using the time dimension for programming and data representation. The first method is based on varying the sampling moment in time of a neuronal action potential, and the second method is based on a neural delay system, where the generation of the action potential is delayed by specific time lengths, to be sampled at a fixed moment in time. Both methods are supported by explicit examples.