Brain-Inspired Learning on Neuromorphic Substrates

• URL: http://arxiv.org/abs/2010.11931v1
• Date: Thu, 22 Oct 2020 17:56:59 GMT
• Title: Brain-Inspired Learning on Neuromorphic Substrates
• Authors: Friedemann Zenke and Emre O. Neftci
• Abstract summary: This article provides a mathematical framework for the design of practical online learning algorithms for neuromorphic substrates. Specifically, we show a direct connection between Real-Time Recurrent Learning (RTRL) and biologically plausible learning rules for training Spiking Neural Networks (SNNs) We motivate a sparse approximation based on block-diagonal Jacobians, which reduces the algorithm's computational complexity.
• Score: 5.279475826661643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neuromorphic hardware strives to emulate brain-like neural networks and thus holds the promise for scalable, low-power information processing on temporal data streams. Yet, to solve real-world problems, these networks need to be trained. However, training on neuromorphic substrates creates significant challenges due to the offline character and the required non-local computations of gradient-based learning algorithms. This article provides a mathematical framework for the design of practical online learning algorithms for neuromorphic substrates. Specifically, we show a direct connection between Real-Time Recurrent Learning (RTRL), an online algorithm for computing gradients in conventional Recurrent Neural Networks (RNNs), and biologically plausible learning rules for training Spiking Neural Networks (SNNs). Further, we motivate a sparse approximation based on block-diagonal Jacobians, which reduces the algorithm's computational complexity, diminishes the non-local information requirements, and empirically leads to good learning performance, thereby improving its applicability to neuromorphic substrates. In summary, our framework bridges the gap between synaptic plasticity and gradient-based approaches from deep learning and lays the foundations for powerful information processing on future neuromorphic hardware systems.

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