Multi-timescale conductance spiking networks deliver a gradient-trainable, sparse neuron model with diverse firing regimes that outperforms LIF and AdLIF baselines on Mackey-Glass regression.
Neuromorphic silicon neuron circuits
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Multi-Timescale Conductance Spiking Networks: A Sparse, Gradient-Trainable Framework with Rich Firing Dynamics for Enhanced Temporal Processing
Multi-timescale conductance spiking networks deliver a gradient-trainable, sparse neuron model with diverse firing regimes that outperforms LIF and AdLIF baselines on Mackey-Glass regression.