Event-Based Computation for Touch Localization Based on Precise Spike Timing - Haessig, Germain; Milde, Moritz B.; Aceituno, Pau Vilimelis; Oubari, Omar; Knight, James C.; van Schaik, André; Benosman, Ryad B.; Indiveri, Giacomo (Frontiers in Neuroscience, 2020)
Precise spike timing and temporal coding is used extensively by the nervous system of insects and in the sensory periphery of higher order animals. However conventional Artificial Neural Networks (ANNs) and machine learning algorithms cannot take advantage of this coding strategy, due to the rate-based nature of the signal representation used. Even in the case of artificial Spiking Neural Networks (SNNs), identifying applications where temporal coding outperforms the rate coding strategies of ANNs is still an open challenge. Neuromorphic sensory-processing systems provide an ideal context for exploring the potential advantages of temporal coding, as they are able to efficiently extract the information required to cluster or classify spatio-temporal activity patterns from relative spike timing. Here we explore this approach in an event-based sensory-processing task, and validate it using a model inspired by the sand scorpion. The task consists in localizing a target using only the relative spike timing of eight spatially-separated vibration sensors. We propose two different approaches in which the SNNs learns to cluster spatio-temporal patterns in an unsupervised manner, and demonstrate how the task can be solved both analytically and with the SNN model implementations. We argue that the models proposed are optimal for spatio-temporal pattern classification using precise spike timing and propose a task that can be used as a standard benchmark in evaluating event-based sensory processing models based on temporal coding.