Linear Constraints Learning for Spiking Neurons

TL;DR

This paper presents a supervised learning algorithm for spiking neural networks that effectively manages spike interference, leading to improved memory capacity, faster convergence, and competitive accuracy on standard datasets.

Contribution

A novel learning mechanism that balances weight adjustments to prevent spike interference, enhancing multi-spike classification performance.

Findings

Higher memory capacity than existing methods

Faster convergence in training

Competitive accuracy on Iris and MNIST datasets

Abstract

We introduce a new supervised learning algorithm based to train spiking neural networks for classification. The algorithm overcomes a limitation of existing multi-spike learning methods: it solves the problem of interference between interacting output spikes during a learning trial. This problem of learning interference causes learning performance in existing approaches to decrease as the number of output spikes increases, and represents an important limitation in existing multi-spike learning approaches. We address learning interference by introducing a novel mechanism to balance the magnitudes of weight adjustments during learning, which in theory allows every spike to simultaneously converge to their desired timings. Our results indicate that our method achieves significantly higher memory capacity and faster convergence compared to existing approaches for multi-spike classification. In the ubiquitous Iris and MNIST datasets, our algorithm achieves competitive predictive performance with state-of-the-art approaches.