FiTS spiking neurons improve auditory task performance over LIF baselines by factorizing computation into frequency selectivity and group-delay-based temporal shaping, yielding interpretable per-neuron parameters.
Advancing spatiotemporal processing in spiking neural networks through adaptation
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SiLIF models apply SSM dynamics and parametrization to spiking neurons for stable training, reaching new SOTA on event-based and raw-audio speech datasets while using half the compute of SSMs via synaptic delays.
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FiTS: Interpretable Spiking Neurons via Frequency Selectivity and Temporal Shaping
FiTS spiking neurons improve auditory task performance over LIF baselines by factorizing computation into frequency selectivity and group-delay-based temporal shaping, yielding interpretable per-neuron parameters.
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SiLIF: Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks
SiLIF models apply SSM dynamics and parametrization to spiking neurons for stable training, reaching new SOTA on event-based and raw-audio speech datasets while using half the compute of SSMs via synaptic delays.