Embedding Robust Multi-Timescale Computation in Neuromorphic Hardware using Distributed Representations

Distributed representations using high-dimensional random vectors can be leveraged to embed robust multi-timescale dynamics into attractor-based recurrent spiking neural networks, enabling the implementation of arbitrary finite state machines in neuromorphic hardware.