  Spiking neural networks (SNNs) take inspiration from the brain to enable
energy-efficient computations. Since the advent of Transformers, SNNs have
struggled to compete with artificial networks on modern sequential tasks, as
they inherit limitations from recurrent neural networks (RNNs), with the added
challenge of training with non-differentiable binary spiking activations.
However, a recent renewed interest in efficient alternatives to Transformers
has given rise to state-of-the-art recurrent architectures named state space
models (SSMs). This work systematically investigates, for the first time, the
intersection of state-of-the-art SSMs with SNNs for long-range sequence
modelling. Results suggest that SSM-based SNNs can outperform the Transformer
on all tasks of a well-established long-range sequence modelling benchmark. It
is also shown that SSM-based SNNs can outperform current state-of-the-art SNNs
with fewer parameters on sequential image classification. Finally, a novel
feature mixing layer is introduced, improving SNN accuracy while challenging
assumptions about the role of binary activations in SNNs. This work paves the
way for deploying powerful SSM-based architectures, such as large language
models, to neuromorphic hardware for energy-efficient long-range sequence
modelling.
