Distributed Representations Enable Robust Multi-Timescale Symbolic Computation in Neuromorphic Hardware
<|reference_start|>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 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.<|reference_end|>
@article{cotteret2024distributed,
    title={Distributed Representations Enable Robust Multi-Timescale Symbolic
  Computation in Neuromorphic Hardware},
    author={Madison Cotteret, Hugh Greatorex, Alpha Renner, Junren Chen, Emre
  Neftci, Huaqiang Wu, Giacomo Indiveri, Martin Ziegler, Elisabetta Chicca},
    journal={arXiv preprint arXiv:2405.01305},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01305},
    primaryClass={cs.NE cs.AI}
}