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Stochastic learning in deep neural networks based on nanoscale PCMO device characteristics., , , и . Neurocomputing, (2018)C. elegans chemotaxis inspired neuromorphic circuit for contour tracking and obstacle avoidance., и . IJCNN, стр. 1-8. IEEE, (2015)NormAD - Normalized Approximate Descent based supervised learning rule for spiking neurons., и . IJCNN, стр. 1-8. IEEE, (2015)Arithmetic computing via rate coding in neural circuits with spike-triggered adaptive synapses., и . IJCNN, стр. 1-8. IEEE, (2015)Mimicking the worm - An adaptive spiking neural circuit for contour tracking inspired by C. Elegans thermotaxis., , и . IJCNN, стр. 2079-2086. IEEE, (2014)Acceleration of Convolutional Networks Using Nanoscale Memristive Devices., , и . EANN, том 893 из Communications in Computer and Information Science, стр. 240-251. Springer, (2018)Training Probabilistic Spiking Neural Networks with First-to-spike Decoding., , и . CoRR, (2017)Nanoscale electronic synapses using phase change devices., , , , , , , , , и 5 other автор(ы). JETC, 9 (2): 12:1-12:20 (2013)Live Demonstration: Image Classification Using Bio-inspired Spiking Neural Networks., , и . ISCAS, стр. 1-. IEEE, (2018)Training Multilayer Spiking Neural Networks using NormAD based Spatio-Temporal Error Backpropagation., и . CoRR, (2018)