Supervised Learning in Multilayer Spiking Neural Networks
This work addresses a domain-specific problem for researchers in neuromorphic computing and spiking neural networks, but it appears incremental as it builds on existing learning algorithms with added flexibility.
The authors tackled the problem of supervised learning in multilayer spiking neural networks by introducing an algorithm that overcomes limitations like handling multiple spikes and applying to linearisable neuron models, achieving successful results on benchmarks such as the XOR problem and Iris dataset.
The current article introduces a supervised learning algorithm for multilayer spiking neural networks. The algorithm presented here overcomes some limitations of existing learning algorithms as it can be applied to neurons firing multiple spikes and it can in principle be applied to any linearisable neuron model. The algorithm is applied successfully to various benchmarks, such as the XOR problem and the Iris data set, as well as complex classifications problems. The simulations also show the flexibility of this supervised learning algorithm which permits different encodings of the spike timing patterns, including precise spike trains encoding.