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Found 4 papers, 1 papers with code
Continuous Learning in a Single-Incremental-Task Scenario with Spike Features
Deep Neural Networks (DNNs) have two key deficiencies, their dependence on high precision computing and their inability to perform sequential learning, that is, when a DNN is trained on a first task and the same DNN is trained on the next task it forgets the first task.
A Deep Unsupervised Feature Learning Spiking Neural Network with Binarized Classification Layers for EMNIST Classification using SpykeFlow
The effect of stochastic gradient descent (SGD) approximations on learning capabilities of our network are also explored.
Regression with Deep Learning for Sensor Performance Optimization
Neural networks with at least two hidden layers are called deep networks.
Deep Convolutional Spiking Neural Networks for Image Classification
Spiking neural networks are biologically plausible counterparts of the artificial neural networks, artificial neural networks are usually trained with stochastic gradient descent and spiking neural networks are trained with spike timing dependant plasticity.
