A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks

Recurrent spiking neural networks (RSNNs) can be implemented very efficiently in neuromorphic systems. Nevertheless, training of these models with powerful gradient-based learning algorithms is mostly performed on standard digital hardware using Backpropagation through time (BPTT). However, BPTT has substantial limitations. It does not permit online training and its memory consumption scales linearly with the number of computation steps. In contrast, learning methods using forward propagation of gradients operate in an online manner with a memory consumption independent of the number of time steps. These methods enable SNNs to learn from continuous, infinite-length input sequences. Yet, slow execution speed on conventional hardware as well as inferior performance has hindered their widespread application. In this work, we introduce HYbrid PRopagation (HYPR) that combines the efficiency of parallelization with approximate online forward learning. Our algorithm yields high-throughput online learning through parallelization, paired with constant, i.e., sequence length independent, memory demands. HYPR enables parallelization of parameter update computation over the sub sequences for RSNNs consisting of almost arbitrary non-linear spiking neuron models. We apply HYPR to networks of spiking neurons with oscillatory subthreshold dynamics. We find that this type of neuron model is particularly well trainable by HYPR, resulting in an unprecedentedly low task performance gap between approximate forward gradient learning and BPTT.

Advancing Spatio-Temporal Processing in Spiking Neural Networks through Adaptation

Efficient implementations of spiking neural networks on neuromorphic hardware promise orders of magnitude less power consumption than their non-spiking counterparts. The standard neuron model for spike-based computation on such neuromorphic systems has long been the leaky integrate-and-fire (LIF) neuron. As a promising advancement, a computationally light augmentation of the LIF neuron model with an adaptation mechanism experienced a recent upswing in popularity, caused by demonstrations of its superior performance on spatio-temporal processing tasks. The root of the superiority of these so-called adaptive LIF neurons however, is not well understood. In this article, we thoroughly analyze the dynamical, computational, and learning properties of adaptive LIF neurons and networks thereof. We find that the frequently observed stability problems during training of such networks can be overcome by applying an alternative discretization method that results in provably better stability properties than the commonly used Euler-Forward method. With this discretization, we achieved a new state-of-the-art performance on common event-based benchmark datasets. We also show that the superiority of networks of adaptive LIF neurons extends to the prediction and generation of complex time series. Our further analysis of the computational properties of networks of adaptive LIF neurons shows that they are particularly well suited to exploit the spatio-temporal structure of input sequences. Furthermore, these networks are surprisingly robust to shifts of the mean input strength and input spike rate, even when these shifts were not observed during training. As a consequence, high-performance networks can be obtained without any normalization techniques such as batch normalization or batch-normalization through time.