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Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays
We present MEMprop, the adoption of gradient-based learning to train fully memristive spiking neural networks (MSNNs).
SPICEprop: Backpropagating Errors Through Memristive Spiking Neural Networks
The natural spiking dynamics of the MIF neuron model are fully differentiable, eliminating the need for gradient approximations that are prevalent in the spiking neural network literature.
A Fully Memristive Spiking Neural Network with Unsupervised Learning
We present a fully memristive spiking neural network (MSNN) consisting of physically-realizable memristive neurons and memristive synapses to implement an unsupervised Spiking Time Dependent Plasticity (STDP) learning rule.
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