Surrogate Gradient Learning in Spiking Neural Networks
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A growing number of neuromorphic spiking neural network processors that emulate biological neural networks create an imminent need for methods and tools to enable them to solve real-world signal processing problems. Like conventional neural networks, spiking neural networks are particularly efficient when trained on real, domain specific data. However, their training requires overcoming a number of challenges linked to their binary and dynamical nature. This tutorial elucidates step-by-step the problems typically encountered when training spiking neural networks, and guides the reader through the key concepts of synaptic plasticity and data-driven learning in the spiking setting. To that end, it gives an overview of existing approaches and provides an introduction to surrogate gradient methods, specifically, as a particularly flexible and efficient method to overcome the aforementioned challenges.
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