Spiking neural networks with Hebbian plasticity for unsupervised representation learning

• URL: http://arxiv.org/abs/2305.03866v2
• Date: Wed, 10 May 2023 18:50:55 GMT
• Title: Spiking neural networks with Hebbian plasticity for unsupervised representation learning
• Authors: Naresh Ravichandran, Anders Lansner, Pawel Herman
• Abstract summary: We introduce a novel spiking neural network model for learning distributed internal representations from data in an unsupervised procedure. We employ an online correlation-based Hebbian-Bayesian learning and rewiring mechanism, shown previously to perform representation learning, into a spiking neural network. We show performance close to the non-spiking BCPNN, and competitive with other Hebbian-based spiking networks when trained on MNIST and F-MNIST machine learning benchmarks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a novel spiking neural network model for learning distributed internal representations from data in an unsupervised procedure. We achieved this by transforming the non-spiking feedforward Bayesian Confidence Propagation Neural Network (BCPNN) model, employing an online correlation-based Hebbian-Bayesian learning and rewiring mechanism, shown previously to perform representation learning, into a spiking neural network with Poisson statistics and low firing rate comparable to in vivo cortical pyramidal neurons. We evaluated the representations learned by our spiking model using a linear classifier and show performance close to the non-spiking BCPNN, and competitive with other Hebbian-based spiking networks when trained on MNIST and F-MNIST machine learning benchmarks.

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