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A Spiking Neural Network with a Global Self-Controller for Unsupervised Learning Based on Spike-Timing-Dependent Plasticity Using Flash Memory Synaptic Devices.

, , , , , , and . IJCNN, page 1-7. IEEE, (2019)

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Unsupervised Online Learning With Multiple Postsynaptic Neurons Based on Spike-Timing-Dependent Plasticity Using a TFT-Type NOR Flash Memory Array., , , , and . CoRR, (2018)Review of candidate devices for neuromorphic applications., , , , , , , , , and 3 other author(s). ESSDERC, page 22-27. IEEE, (2019)A Spiking Neural Network with a Global Self-Controller for Unsupervised Learning Based on Spike-Timing-Dependent Plasticity Using Flash Memory Synaptic Devices., , , , , , and . IJCNN, page 1-7. IEEE, (2019)Neuron Circuits for Low-Power Spiking Neural Networks Using Time-To-First-Spike Encoding., , , , , , , and . IEEE Access, (2022)Hardware-based spiking neural network architecture using simplified backpropagation algorithm and homeostasis functionality., , , , , , , , , and . Neurocomputing, (2021)Hardware-Based Spiking Neural Network Using a TFT-Type AND Flash Memory Array Architecture Based on Direct Feedback Alignment., , , , , , , and . IEEE Access, (2021)On-chip trainable hardware-based deep Q-networks approximating a backpropagation algorithm., , , , , , , , , and . Neural Comput. Appl., 33 (15): 9391-9402 (2021)Spiking Neural Networks With Time-to-First-Spike Coding Using TFT-Type Synaptic Device Model., , , , , , , , and . IEEE Access, (2021)Hardware Implementation of Spiking Neural Networks Using Time-To-First-Spike Encoding., , , , , , , , and . CoRR, (2020)