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.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.NE 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.