Distributed representations enable robust multi-timescale symbolic computation in neuromorphic hardware

Distributed representations enable robust multi-timescale symbolic computation in neuromorphic hardware

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...

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Bibliographic Details
Main Authors: Madison Cotteret, Hugh Greatorex, Alpha Renner, Junren Chen, Emre Neftci, Huaqiang Wu, Giacomo Indiveri, Martin Ziegler, Elisabetta Chicca
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Neuromorphic Computing and Engineering
Subjects:
attractor networks
distributed computation
vector symbolic architectures
neuromorphic hardware abstractions
hyperdimensional computing
hardware interoperability
Online Access:https://doi.org/10.1088/2634-4386/ada851
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https://doi.org/10.1088/2634-4386/ada851

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