Haessig Germain, Milde Moritz B, Aceituno Pau Vilimelis, Oubari Omar, Knight James C, van Schaik André, Benosman Ryad B, Indiveri Giacomo
Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
International Centre for Neuromorphic Systems, MARCS Institute, Western Sydney University, Penrith, NSW, Australia.
Front Neurosci. 2020 May 19;14:420. doi: 10.3389/fnins.2020.00420. eCollection 2020.
Precise spike timing and temporal coding are used extensively within 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 their rate-based representation of signals. 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 propose a neuromorphic model inspired by the sand scorpion to explore the benefits of temporal coding, and validate it in an event-based sensory-processing task. 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 we demonstrate how the task can be solved both analytically and through numerical simulation of multiple SNN models. We argue that the models presented are optimal for spatio-temporal pattern classification using precise spike timing in a task that could be used as a standard benchmark for evaluating event-based sensory processing models based on temporal coding.
精确的脉冲时间和时间编码在昆虫的神经系统以及高等动物的感觉外周中被广泛使用。然而，传统的人工神经网络（ANN）和机器学习算法由于其基于速率的信号表示方式，无法利用这种编码策略。即使在人工脉冲神经网络（SNN）的情况下，确定时间编码优于ANN速率编码策略的应用仍然是一个开放的挑战。神经形态传感处理系统为探索时间编码的潜在优势提供了理想的环境，因为它们能够从相对脉冲时间中有效地提取对时空活动模式进行聚类或分类所需的信息。在这里，我们提出了一种受沙漠蝎子启发的神经形态模型，以探索时间编码的优势，并在基于事件的传感处理任务中对其进行验证。该任务包括仅使用八个空间分离的振动传感器的相对脉冲时间来定位目标。我们提出了两种不同的方法，其中SNN以无监督的方式学习对时空模式进行聚类，并且我们展示了如何通过解析和对多个SNN模型进行数值模拟来解决该任务。我们认为，所提出的模型对于在一项任务中使用精确脉冲时间进行时空模式分类是最优的，该任务可作为评估基于时间编码的基于事件的传感处理模型的标准基准。
