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Found 4 papers, 2 papers with code
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks
In this paper, we take inspiration from the observation that membrane-related parameters are different across brain regions, and propose a training algorithm that is capable of learning not only the synaptic weights but also the membrane time constants of SNNs.
Deep Learning in Spiking Neural Networks
In this approach, a deep (multilayer) artificial neural network (ANN) is trained in a supervised manner using backpropagation.
Representation Learning using Event-based STDP
Although representation learning methods developed within the framework of traditional neural networks are relatively mature, developing a spiking representation model remains a challenging problem.
Acquisition of Visual Features Through Probabilistic Spike-Timing-Dependent Plasticity
The original model showed that a spike-timing-dependent plasticity (STDP) learning algorithm embedded in an appropriately selected SCN could perform unsupervised feature discovery.
