CoSy seminar: "Bridging Spiking Neural Networks and Deep State-Space Models"

Everyone is welcome and the first 40 people to register will be treated to a free lunch sandwich. If you do not want lunch, you are still welcome to join.

Abstract: Spiking neural networks (SNNs) are biologically inspired, event-driven models that are suitable for processing temporal data and offer low-latency, energy-efficient computation when implemented on neuromorphic hardware. Similar to SNNs, state-space models (SSMs) utilize the concepts of state and recurrence. Recently, deep SSMs have emerged as a promising model family for obtaining competitive performance for a wide range of tasks on long temporal sequences. In this talk, we highlight the bridge between SNNs and SSMs; and use this relationship to investigate several temporal modeling and classification problems. In particular, we leverage the SSM perspective to develop zero-shot adaptation methods for SNNs under temporal-resolution mismatch between training data and test data. This corresponds to the scenarios where the temporal resolution of target data during deployment is not the same with that of the pre-deployment source data used for training. Furthermore, building upon core SNN concepts, we introduce spiking and state reset mechanisms into deep SSMs, and demonstrate the potential performance gains provided by these biologically inspired non-linearities. Overall, our results on various tasks, including an audio keyword spotting task, an event-based vision task and a sequential pattern recognition task, demonstrate that promising performance gains for temporal modeling emerge from careful integration of concepts from SNN and SSM frameworks.

This is a lecture in the seminar series held by CIM (Centre for Interdisciplinary Mathematics).