Recurrent Spiking Neural Networks (RSNNs) have emerged as a computationally
efficient and brain-inspired learning model. The design of sparse RSNNs with
fewer neurons and synapses helps reduce the computational complexity of RSNNs.
Traditionally, sparse SNNs are obtained by first training a dense and complex
SNN for a target task, and, then, pruning neurons with low activity
(activity-based pruning) while maintaining task performance. In contrast, this
paper presents a task-agnostic methodology for designing sparse RSNNs by
pruning a large randomly initialized model. We introduce a novel Lyapunov Noise
Pruning (LNP) algorithm that uses graph sparsification methods and utilizes
Lyapunov exponents to design a stable sparse RSNN from a randomly initialized
RSNN. We show that the LNP can leverage diversity in neuronal timescales to
design a sparse Heterogeneous RSNN (HRSNN). Further, we show that the same
sparse HRSNN model can be trained for different tasks, such as image
classification and temporal prediction. We experimentally show that, in spite
of being task-agnostic, LNP increases computational efficiency (fewer neurons
and synapses) and prediction performance of RSNNs compared to traditional
activity-based pruning of trained dense models.

通过引入新颖的 Lyapunov Noise Pruning (LNP) 算法和利用各类散度来设计一个稳定的、计算效率高的、适用于不同任务的稀疏的异质 RSNN 模型，本文展示了相对于传统的基于活性剪枝的方法，该任务无关的方法可以增加 RSNN 的计算效率和预测性能。