Biomimetic oculomotor control with spiking neural networks

Abstract

Spiking neural networks (SNNs) are comprised of artificial neurons that, like their biological counterparts, communicate via electrical spikes. SNNs have been hailed as the next wave of deep learning as they promise low latency and low-power consumption when run on neuromorphic hardware. Current deep neural network models for computer vision often require power-hungry GPUs to train and run, making them great candidates to replace with SNNs. We develop and train a biomimetic, SNN-driven, neuromuscular oculomotor controller for a realistic biomechanical model of the human eye. Inspired by the ON and OFF bipolar cells of the retina, we use event-based data flow in the SNN to direct the necessary extraocular muscle-driven eye movements. We train our SNN models from scratch, using modified deep learning techniques. Classification tasks are straightforward to implement with SNNs and have received the most research attention, but visual tracking is a regression task. We use surrogate gradients and introduce a linear layer to convert membrane voltages from the final spiking layer into the desired outputs. Our SNN foveation network enhances the biomimetic properties of the virtual eye model and enables it to perform reliable visual tracking. Overall, with event-based data processed by an SNN, our oculomotor controller successfully tracks a visual target while activating 87.3% fewer neurons than a conventional neural network.