Distributed Representations Enable Robust Multi-Timescale Symbolic Computation in Neuromorphic Hardware
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2025-01-13
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Working Paper
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Abstract
Programming recurrent spiking neural networks (RSNNs) to robustly perform multi-timescale computation remains a difficult challenge. To address this, we describe a single-shot weight learning scheme to embed robust multi-timescale dynamics into attractor-based RSNNs, by exploiting the properties of high-dimensional distributed representations. We embed finite state machines into the RSNN dynamics by superimposing a symmetric autoassociative weight matrix and asymmetric transition terms, which are each formed by the vector binding of an input and heteroassociative outer-products between states. Our approach is validated through simulations with highly nonideal weights; an experimental closed-loop memristive hardware setup; and on Loihi 2, where it scales seamlessly to large state machines. This work introduces a scalable approach to embed robust symbolic computation through recurrent dynamics into neuromorphic hardware, without requiring parameter fine-tuning or significant platform-specific optimisation. Moreover, it demonstrates that distributed symbolic representations serve as a highly capable representation-invariant language for cognitive algorithms in neuromorphic hardware.
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published
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Cornell University
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v3
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Subject
Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); FOS: Computer and information sciences
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09699 - Indiveri, Giacomo / Indiveri, Giacomo
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Is previous version of: https://doi.org/10.3929/ethz-b-000726168