May 2, 2024 · This work introduces a scalable approach to embed robust symbolic computation through recurrent dynamics into neuromorphic hardware.

May 2, 2024 · robust computation with recurrent dynamics into neuromorphic hardware, without requiring parameter fine-tuning or significant platform ...

Jul 16, 2024 · The paper proposes a hardware-aware training approach for neural field reconstruction models running on neuromorphic hardware with resistive ...

HDC was proposed as a framework for computation on neuromorphic hardware. → Provides an abstraction layer or “instruction set” of a few operations that allows ...

Co-authors ; Distributed Representations Enable Robust Multi-Timescale Symbolic Computation in Neuromorphic Hardware. M Cotteret, H Greatorex, A Renner, J Chen, ...

Distributed Representations Enable Robust Multi-Timescale Computation in Neuromorphic Hardware · Scaling Limits of Memristor-Based Routers for Asynchronous ...

Distributed Representations Enable Robust Multi-Timescale Computation in Neuromorphic Hardware, Madison Cotteret et.al. 2405.01305v1, null. 2024-04-25 ...

... neuromorphic systems. J Chen, H Wu, B Gao, J Tang, XS Hu ... Distributed Representations Enable Robust Multi-Timescale Computation in Neuromorphic Hardware.

Distributed Representations Enable Robust Multi-Timescale Computation in Neuromorphic Hardware ... neural networks (RSNNs) to robustly perform multi-timescale

Distributed Representations Enable Robust Multi-Timescale Computation in Neuromorphic Hardware ... robust multi-timescale dynamics into attractor-based RSNNs.