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Sparse Distributed Memory using Spiking Neural Networks on Nengo
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Sparse Distributed Memory using Spiking Neural Networks on Nengo
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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.
Forward citations
Cited by 2 Pith papers
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Spiking Sequence Machines and Transformers
Spiking SDM and transformers implement identical functional operations for sequences via cosine similarity retrieval, unified by a phase-latency isomorphism between spike timing and sinusoidal positional encoding.
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Rank-Order N-of-M Codes for Sparse Distributed Memory: Disentangling Representation and Learning Effects in Noise Robustness Against Contemporary Neuromorphic Architectures
Rank-order N-of-M SDM's large noise-robustness gain comes mostly from MAX-Hebbian learning interacting with the encoder; the encoder alone yields only a small, often non-significant advantage.
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