<ul><li>Spiking neural networks (SNNs) have gained attention for their low energy consumption and temporal dynamics.</li><li>Researchers have developed spiking state space models (SpikingSSMs) for long sequence learning.</li><li>SpikingSSMs integrate neuronal dynamics with state space models and utilize sparse synaptic computation.</li><li>The proposed SpikingSSM shows competitive performance on benchmark tasks and has potential as a low computation cost architecture for language models.</li></ul>

SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models

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