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Chaos-Based Mixed Signal Implementation of Spiking Neurons.

, , , and . Int. J. Neural Syst., 19 (6): 465-471 (2009)

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Hardware Implementation of Stochastic Spiking Neural Networks., , , and . Int. J. Neural Syst., (2012)An unconventional computing technique for ultra-fast and ultra-low power data mining., , , , and . PATMOS, page 40-46. IEEE, (2015)Spiking Neural Network Self-configuration for Temporal Pattern Recognition Analysis., , , and . ICANN (1), volume 5768 of Lecture Notes in Computer Science, page 421-428. Springer, (2009)Practical Hardware Implementation of Self-configuring Neural Networks., , , and . ISNN (3), volume 5553 of Lecture Notes in Computer Science, page 1154-1159. Springer, (2009)Stochastic-based pattern-recognition analysis., , and . Pattern Recognit. Lett., 31 (15): 2353-2356 (2010)Noise tolerant probabilistic logic for statistical pattern recognition applications., , , , , , , , , and . Integr. Comput. Aided Eng., 24 (4): 351-365 (2017)A New Stochastic Computing Methodology for Efficient Neural Network Implementation., , , , and . IEEE Trans. Neural Networks Learn. Syst., 27 (3): 551-564 (2016)Hardware implementation of stochastic-based Neural Networks., , and . IJCNN, page 1-4. IEEE, (2010)Chaos-Based Mixed Signal Implementation of Spiking Neurons., , , and . Int. J. Neural Syst., 19 (6): 465-471 (2009)High-Density Liquid-State Machine Circuitry for Time-Series Forecasting., , , , and . Int. J. Neural Syst., 26 (5): 1550036:1-1550036:12 (2016)