Extreme Sparsity in Hodgkin-Huxley Spiking Neural Networks

Spiking neural networks present a new generation of artificial neural networks utilizing binary data representation for large energy, memory, and latency savings due to widespread sparsity. At the same time, they are at the focal point for neuroscientific communities, enabling biologically plausible simulations of systems with a learning capacity. In this work, we present spiking neural networks based on an implementation of a biologically highly plausible neuron model, the Hodgkin-Huxley model. The networks are trained via backpropagation through time using surrogate gradients. They show competitive performances on various neuromorphic datasets while displaying a spiking activity lower by up to two orders of magnitude compared to similar spiking neural networks based on the conventional leaky integrate-and-fire neuron model. Additionally, this work paves the way toward higher biological plausibility in high-performance computational neuroscience by enabling in-depth looks at the underlying dynamics of Hodgkin-Huxley neurons as part of larger operating networks.