Spike-based building blocks for performing logic operations using Spiking Neural Networks on SpiNNaker

TL;DR

This paper introduces a new toolkit of spike-based logic gate building blocks implemented on SpiNNaker hardware, enabling neuromorphic systems to perform digital logic operations more efficiently.

Contribution

It presents novel spike-based models for logic gates and demonstrates their implementation and validation on SpiNNaker hardware platform.

Findings

Logic gate behaviors are successfully emulated using spike-based models.

Experimental results confirm the correct functionality of the proposed logic blocks.

The approach is feasible for integrating digital logic in neuromorphic systems.

Abstract

One of the most interesting and still growing scientific fields is neuromorphic engineering, which is focused on studying and designing hardware and software with the purpose of mimicking the basic principles of biological nervous systems. Currently, there are many research groups developing practical applications based on neuroscientific knowledge. This work provides researchers with a novel toolkit of building blocks based on Spiking Neural Networks that emulate the behavior of different logic gates. These could be very useful in many spike-based applications, since logic gates are the basis of digital circuits. The designs and models proposed are presented and implemented on a SpiNNaker hardware platform. Different experiments were performed in order to validate the expected behavior, and the obtained results are discussed. The functionality of traditional logic gates and the proposed blocks is studied, and the feasibility of the presented approach is discussed.