Distributed Representations Enable Robust Multi-Timescale Symbolic Computation in Neuromorphic Hardware

• URL: http://arxiv.org/abs/2405.01305v2
• Date: Tue, 16 Jul 2024 09:41:27 GMT
• Title: Distributed Representations Enable Robust Multi-Timescale Symbolic Computation in Neuromorphic Hardware
• Authors: Madison Cotteret, Hugh Greatorex, Alpha Renner, Junren Chen, Emre Neftci, Huaqiang Wu, Giacomo Indiveri, Martin Ziegler, Elisabetta Chicca,
• Abstract summary: We describe a single-shot weight learning scheme to embed robust multi-timescale dynamics into attractor-based RSNNs. We embed finite state machines into the RSNN dynamics by superimposing a symmetric autoassociative weight matrix. This work introduces a scalable approach to embed robust symbolic computation through recurrent dynamics into neuromorphic hardware.
• Score: 3.961418890143814
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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 non-ideal 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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