A crossing activation function combined with virtual noise fields allows one neural network to learn multiple functions assigned to different noise locations, with capacity rising when noise arrangement matches function proximity.
Training deep spiking neural networks using backpropagation
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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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Spatial Partial Functionalization of Neural Networks based on Noise Fields
A crossing activation function combined with virtual noise fields allows one neural network to learn multiple functions assigned to different noise locations, with capacity rising when noise arrangement matches function proximity.
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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.