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

arXiv: https://arxiv.org/abs/2405.01305 · HTML: https://mlsanity.com/paper/2405.01305/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 108 references that are in the map.
- First posted on arXiv 2 May 2024.

## Builds on (most cited first, 30 of 30)

- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations](https://mlsanity.com/paper/2111.06077/) (2021)
- [Efficient Neuromorphic Signal Processing with Loihi 2](https://mlsanity.com/paper/2111.03746/) (2021)
- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges](https://mlsanity.com/paper/2112.15424/) (2021)
- [Stable Recurrent Models](https://mlsanity.com/paper/1805.10369/) (2018)
- [A Neuro-vector-symbolic Architecture for Solving Raven's Progressive Matrices](https://mlsanity.com/paper/2203.04571/) (2022)
- [Attractor and integrator networks in the brain](https://mlsanity.com/paper/2112.03978/) (2021)
- [In-memory hyperdimensional computing](https://mlsanity.com/paper/1906.01548/) (2019)
- [A Theoretical Perspective on Hyperdimensional Computing](https://mlsanity.com/paper/2010.07426/) (2020)
- [Computing on Functions Using Randomized Vector Representations](https://mlsanity.com/paper/2109.03429/) (2021)
- [Surrogate gradients for analog neuromorphic computing](https://mlsanity.com/paper/2006.07239/) (2020)
- [Vector Symbolic Architectures as a Computing Framework for Emerging Hardware](https://mlsanity.com/paper/2106.05268/) (2021)
- [Variable Binding for Sparse Distributed Representations: Theory and Applications](https://mlsanity.com/paper/2009.06734/) (2020)
- [Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing](https://mlsanity.com/paper/2311.14641/) (2023)
- [Robust computation with rhythmic spike patterns](https://mlsanity.com/paper/1901.07718/) (2019)
- [Neuromorphic electronic circuits for building autonomous cognitive systems](https://mlsanity.com/paper/1403.6428/) (2014)
- [Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System](https://mlsanity.com/paper/1703.01909/) (2017)
- [Neuromorphic Visual Scene Understanding with Resonator Networks](https://mlsanity.com/paper/2208.12880/) (2022)
- [DenRAM: Neuromorphic Dendritic Architecture with RRAM for Efficient Temporal Processing with Delays](https://mlsanity.com/paper/2312.08960/) (2023)
- [Neural Distributed Autoassociative Memories: A Survey](https://mlsanity.com/paper/1709.00848/) (2017)
- [Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors](https://mlsanity.com/paper/2102.06408/) (2021)
- [Composing Neural Algorithms with Fugu](https://mlsanity.com/paper/1905.12130/) (2019)
- [Online Training of Spiking Recurrent Neural Networks with Phase-Change Memory Synapses](https://mlsanity.com/paper/2108.01804/) (2021)
- [Vector Symbolic Finite State Machines in Attractor Neural Networks](https://mlsanity.com/paper/2212.01196/) (2022)
- [Gradient-descent hardware-aware training and deployment for mixed-signal Neuromorphic processors](https://mlsanity.com/paper/2303.12167/) (2023)
- [Linear Codes for Hyperdimensional Computing](https://mlsanity.com/paper/2403.03278/) (2024)
- [Capacity Analysis of Vector Symbolic Architectures](https://mlsanity.com/paper/2301.10352/) (2023)
- [Rhythmic inhibition allows neural networks to search for maximally consistent states](https://mlsanity.com/paper/1503.02777/) (2015)
- [Visual Pattern Recognition with on On-chip Learning: towards a Fully Neuromorphic Approach](https://mlsanity.com/paper/2008.03470/) (2020)
- [Scaling Limits of Memristor-Based Routers for Asynchronous Neuromorphic Systems](https://mlsanity.com/paper/2307.08116/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Grid Cell-Inspired Structured Vector Algebra for Cognitive Maps](https://mlsanity.com/paper/2503.08608/) (2025)
- [High-resolution spatial memory requires grid-cell-like neural codes](https://mlsanity.com/paper/2507.00598/) (2025)
- [A Neuromorphic Architecture for Scalable Event-Based Control](https://mlsanity.com/paper/2511.11924/) (2025)

## Abstract (arXiv)

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.