Sparse Distributed Memory using Spiking Neural Networks on Nengo

TL;DR

This paper demonstrates that spiking neural networks implemented on Nengo can effectively perform sparse distributed memory tasks, achieving comparable performance to traditional models, and applies this to MNIST image-label association.

Contribution

It introduces a novel implementation of SDM and CMM using SNNs on Nengo, showing their viability for associative memory tasks.

Findings

SNN-based SDMs perform similarly to conventional SDMs.

Different SNN neuron models yield comparable results.

Successful application to MNIST image-label association.

Abstract

We present a Spiking Neural Network (SNN) based Sparse Distributed Memory (SDM) implemented on the Nengo framework. We have based our work on previous work by Furber et al, 2004, implementing SDM using N-of-M codes. As an integral part of the SDM design, we have implemented Correlation Matrix Memory (CMM) using SNN on Nengo. Our SNN implementation uses Leaky Integrate and Fire (LIF) spiking neuron models on Nengo. Our objective is to understand how well SNN-based SDMs perform in comparison to conventional SDMs. Towards this, we have simulated both conventional and SNN-based SDM and CMM on Nengo. We observe that SNN-based models perform similarly as the conventional ones. In order to evaluate the performance of different SNNs, we repeated the experiment using Adaptive-LIF, Spiking Rectified Linear Unit, and Izhikevich models and obtained similar results. We conclude that it is indeed feasible to develop some types of associative memories using spiking neurons whose memory capacity and other features are similar to the performance without SNNs. Finally we have implemented an application where MNIST images, encoded with N-of-M codes, are associated with their labels and stored in the SNN-based SDM.