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.

翻译：编程递归脉冲神经网络以鲁棒地执行多时间尺度计算仍然是一个难题。为解决这一问题，我们展示了如何利用向量符号架构(VSA)提供的分布式方法（该方法使用高维随机向量作为表征的最小单元），将鲁棒的多时间尺度动态嵌入基于吸引子的RSNN中。通过叠加对称的自联想权重矩阵和非对称的转移项，我们将有限状态机嵌入RSNN动态中。转移项由输入与状态间的异联想外积的VSA绑定构成。我们的方法通过高度非理想权重的仿真、实验性的闭环忆阻硬件装置以及在Loihi 2上的验证得到证实——其中该方法可无缝扩展至大型状态机。这项工作证明了VSA表征在将具有递归动态的鲁棒计算嵌入神经形态硬件方面的有效性，无需参数微调或显著的平台特定优化。这推动了VSA作为神经形态硬件中认知算法的高层表征不变抽象语言的发展。