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Found 3 papers, 2 papers with code
Spiking neural networks trained via proxy
By assuming IF neuron with rate-coding as an approximation of ReLU, we backpropagate the error of the SNN in the proxy ANN to update the shared weights, simply by replacing the ANN final output with that of the SNN.
Spike time displacement based error backpropagation in convolutional spiking neural networks
To do so, we consider a convolutional SNN (CSNN) with two sets of weights: real-valued weights that are updated in the backward pass and their signs, binary weights, that are employed in the feedforward process.
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on Fashion-MNIST
BS4NN: Binarized Spiking Neural Networks with Temporal Coding and Learning
We recently proposed the S4NN algorithm, essentially an adaptation of backpropagation to multilayer spiking neural networks that use simple non-leaky integrate-and-fire neurons and a form of temporal coding known as time-to-first-spike coding.
