Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding

TL;DR

This paper introduces and evaluates two supervised learning rules for spiking neural networks that encode information through precise spike timing, emphasizing the biological relevance and efficiency of the FILT rule in temporal encoding tasks.

Contribution

The study provides a theoretical analysis and empirical evaluation of two spike-based learning rules, highlighting the superior performance and biological plausibility of the FILT rule for temporal coding.

Findings

FILT outperforms INST in encoding precision and pattern memorization.

FILT achieves high efficiency with sub-millisecond spike timing.

FILT's error-filtering mechanism ensures smooth convergence.

Abstract

Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of FILT in most cases, underpinned by the rule's error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find FILT to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of FILT to be consistent with that of the highly efficient E-learning Chronotron, but with the distinct advantage that FILT is also implementable as an online method for increased biological realism.