Supervised learning based on temporal coding in spiking neural networks
This provides a new approach for controlling SNNs with realistic temporal dynamics, potentially enabling processing of complex temporal spike patterns, but it is incremental as it adapts existing ANN methods to a specific coding scheme.
The paper tackled the problem of training spiking neural networks (SNNs) with gradient descent by using a temporal coding scheme where information is encoded in spike times, showing that the network input-output relation is differentiable and piece-wise linear, enabling direct application of ANN training methods on the MNIST task.
Gradient descent training techniques are remarkably successful in training analog-valued artificial neural networks (ANNs). Such training techniques, however, do not transfer easily to spiking networks due to the spike generation hard non-linearity and the discrete nature of spike communication. We show that in a feedforward spiking network that uses a temporal coding scheme where information is encoded in spike times instead of spike rates, the network input-output relation is differentiable almost everywhere. Moreover, this relation is piece-wise linear after a transformation of variables. Methods for training ANNs thus carry directly to the training of such spiking networks as we show when training on the permutation invariant MNIST task. In contrast to rate-based spiking networks that are often used to approximate the behavior of ANNs, the networks we present spike much more sparsely and their behavior can not be directly approximated by conventional ANNs. Our results highlight a new approach for controlling the behavior of spiking networks with realistic temporal dynamics, opening up the potential for using these networks to process spike patterns with complex temporal information.