Spiking Neural Networks for Nonlinear Regression

Abstract

Spiking Neural Networks for Nonlinear RegressionAlexander Henkes a, Jason Eshraghian b, Henning Wessels aa TU Braunschweig, Germanyb UC Santa Cruz, 1156 High Street, Santa Cruz, 95064, United StatesProceedings of Neuromorphic Materials, Devices, Circuits and Systems (NeuMatDeCaS)València, Spain, 2023 January 23rd - 25thOrganizers: Rohit Abraham John, Irem Boybat, Jason Eshraghian and Simone FabianoContributed talk, Alexander Henkes, presentation 045DOI: https://doi.org/10.29363/nanoge.neumatdecas.2023.045Publication date: 9th January 2023Spiking neural networks (SNNs) have the potential to greatly reduce memory and energy consumption compared to traditional neural networks. Inspired by the efficiency of the human brain, SNNs incorporate temporal and neuronal sparsity, which can be exploited by next-generation neuromorphic hardware. To explore the potential of SNNs in engineering applications, where regression tasks are common, we introduce a framework for regression using SNNs in the context of continuum mechanics. However, the nature of SNNs presents a challenge for regression tasks, which are frequently encountered in engineering sciences. To address this challenge, we propose a network topology that uses the membrane potential of spiking neurons to decode binary spike trains into real numbers. We apply our framework to history-dependent material models, fitting SNNs to data that describes the behavior of materials over time. This type of model can be used to model materials that are subjected to complex loading histories, such as materials that are stressed beyond their reversibility point. We use nonlinear regression to fit the model to data that describes the time-dependent behavior of these materials, allowing for accurate predictions about the material's response to different loading conditions. We derive several different spiking neural architectures, ranging from simple spiking feed-forward networks to complex spiking long short-term memory networks. We conduct numerical experiments to evaluate our framework's ability to perform regression on linear and nonlinear, history-dependent material models. SNNs are a natural fit for modeling history-dependent materials, and we demonstrate their ability to accurately model materials that are stressed beyond reversibility. A comparison with traditional neural networks shows that our proposed framework is more efficient while maintaining precision and generalizability. All code has been made publicly available to support reproducibility and encourage further development in this field. © FUNDACIO DE LA COMUNITAT VALENCIANA SCITOnanoGe is a prestigious brand of successful science conferences that are developed along the year in different areas of the world since 2009. 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