Advancing spatio-temporal processing through adaptation in spiking neural networks

This is the authors' abstract. We don't add key points for this paper.

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 systems has long been the leaky integrate-and-fire neuron. A computationally light augmentation of this neuron model with an adaptation mechanism has recently been shown to exhibit superior performance on spatio-temporal processing tasks. The root of the superiority of these so-called adaptive leaky integrate-and-fire neurons however is not well understood. In this article, we thoroughly analyze the dynamical, computational, and learning properties of adaptive leaky integrate-and-fire neurons and networks thereof. Our investigation reveals significant challenges related to stability and parameterization when employing the conventional Euler-Forward discretization for this class of models. We report a rigorous theoretical and empirical demonstration that these challenges can be effectively addressed by adopting an alternative discretization approach - the Symplectic Euler method, allowing to improve over state-of-the-art performances on common event-based benchmark datasets. Our further analysis of the computational properties of these networks shows that they are particularly well suited to exploit the spatio-temporal structure of input sequences without any normalization techniques.