Programming recurrent spiking neural networks (RSNNs) to robustly perform
multi-timescale computation remains a difficult challenge. To address this, we
show how the distributed approach offered by vector symbolic architectures
(VSAs), which uses high-dimensional random vectors as the smallest units of
representation, can be leveraged to embed robust multi-timescale dynamics into
attractor-based RSNNs. We embed finite state machines into the RSNN dynamics by
superimposing a symmetric autoassociative weight matrix and asymmetric
transition terms. The transition terms are formed by the VSA 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 demonstrates the effectiveness of
VSA representations for embedding robust computation with recurrent dynamics
into neuromorphic hardware, without requiring parameter fine-tuning or
significant platform-specific optimisation. This advances VSAs as a high-level
representation-invariant abstract language for cognitive algorithms in
neuromorphic hardware.

使用高维随机向量作为最小表示单元的矢量符号结构（VSAs）嵌入鲁棒的多时间尺度动力学到基于吸引子的 RSNNs，通过叠加对称的自联想权重矩阵和非对称的状态转换项来嵌入有限状态机进入 RSNNs 动力学，通过模拟高度非理想的权重、实验性的闭环存储器硬件设置，和在 Loihi 2 上验证，表明了 VSA 表示在嵌入鲁棒计算与循环动力学到神经形态硬件中的有效性，无需参数微调或平台的特定优化，这将 VSA 推进为认知算法在神经形态硬件中的高级表示不变的抽象语言。