Linear Constraints Learning for Spiking Neurons
This addresses a key limitation in spiking neural networks for classification tasks, though it appears incremental as it builds on existing multi-spike learning approaches.
The paper tackles the problem of interference between interacting output spikes in multi-spike learning for spiking neural networks, resulting in significantly higher memory capacity and faster convergence compared to existing methods.
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.