Dec 1, 2023 · We present desire backpropagation, a method to derive the desired spike activity of all neurons, including the hidden ones, from the output ...
Nov 10, 2022 · That makes desire backpropagation a spike-based supervised learning rule. We trained three-layer networks to classify MNIST and Fashion-MNIST ...
That makes desire backpropagation a spike-based supervised learning rule. We trained three-layer networks to classify MNIST and Fashion-MNIST images and reached ...
Dec 1, 2023 · We present desire backpropagation, a method to derive the desired spike activity of all neurons, including the hidden ones, from the output ...
Dec 1, 2023 · Desire backpropagation: A lightweight training algorithm for multi-layer spiking neural networks based on spike-timing-dependent plasticity.
We present desire backpropagation, a method to derive the desired spike activity of neurons from the output error. The loss function can then be evaluated ...
Nov 10, 2022 · Desire Backpropagation: A Lightweight Training Algorithm for Multi-Layer Spiking Neural Networks based on Spike-Timing-Dependent Plasticity.
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What is back propagation algorithm for multilayer neural network?
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What is spike in spiking neural network?
Training of multi-layer spiking neural networks using a combination of spike-timing-dependent plasiticiy (STDP) and backpropgatation of local errors. Usage.
Nov 10, 2022 · Desire Backpropagation: A Lightweight Training Algorithm for Multi-Layer Spiking Neural Networks based on Spike-Timing-Dependent Plasticity
This work proposes an approximate derivative method that accounts for the leaky behavior of LIF neurons that enables training deep convolutional SNNs ...