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Found 6 papers, 4 papers with code
Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration
We demonstrate that our method can handle the SNN conversion with batch normalization layers and effectively preserve the high accuracy even in 32 time steps.
Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting
Then we introduce the temporal efficient training (TET) approach to compensate for the loss of momentum in the gradient descent with SG so that the training process can converge into flatter minima with better generalizability.
Control Theory Illustrates the Energy Efficiency in the Dynamic Reconfiguration of Functional Connectivity
The brain's functional connectivity fluctuates over time instead of remaining steady in a stationary mode even during the resting state.
Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks
Based on the introduced finite difference gradient, we propose a new family of Differentiable Spike (Dspike) functions that can adaptively evolve during training to find the optimal shape and smoothness for gradient estimation.
Ranked #4 on
Event data classification
on CIFAR10-DVS
A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration
Moreover, our calibration algorithm can produce SNN with state-of-the-art architecture on the large-scale ImageNet dataset, including MobileNet and RegNet.
Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks
As an alternative, many efforts have been devoted to converting conventional ANNs into SNNs by copying the weights from ANNs and adjusting the spiking threshold potential of neurons in SNNs.
