Abstract
Memristors are memory resistors that promise the efficient implementation of synaptic weights in artificial neural networks [1]. This kind of technology has permitted the implementation of a large number of real world data in an evolutionary learning artificial system. Human brain is capable of processing such data with standard always equal signals that are the synapsis. Our goal is to present a circuit which responds with binary outputs to the signal exiting from the memristors implemented in an artificial neural system that functions through a high efficiency learning algorithm.
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References
Alibart, F., Zmanidoost, E., Strukov, D.B.: Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nature Communications (2013)
Chua, L.O.: Memristor: the missing circuit element. IEEE Transactions on Circuit Theory 18(5), 507–519 (1971)
Corinto, F., Ascoli, A.: A boundary condition-based approach to the modeling of memristor nanostructures. IEEE Trans. Circuits Syst. I 59, 2713–2726 (2012), doi:10.1109/TCSI.2012.2190563
Corinto, F., Ascoli, A.: Memristive diode bridge with LCR filter. Electronics Letters 48(14), 824–825 (2012)
Batas, D., Fielder, H.: A memristor PSpice implementation and a new approach for magnetic flux-controlled memristor modeling. IEEE Transactions on Nanotechnology (2011)
Baldassi, C., Braunstein, A., Brunel, N., Zecchina, R.: Efficient supervised leaning in networks with binary synapses. PNAS (2007)
Baldassi, C.: Generalization Learning in a Perceptron with Binary Synapses. Journal of Statistical Physics (2009)
Manem, H., Rajedran, J., Rose, G.S.: Stochastic gradient descent inspired training technique for a CMOS/Nano memristive trainable threshold gate array. IEEE Trans. Circuits Syst. I (2012)
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Secco, J., Vinassa, A., Pontrandolfo, V., Baldassi, C., Corinto, F. (2015). Binary Synapse Circuitry for High Efficiency Learning Algorithm Using Generalized Boundary Condition Memristor Models. In: Bassis, S., Esposito, A., Morabito, F. (eds) Advances in Neural Networks: Computational and Theoretical Issues. Smart Innovation, Systems and Technologies, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-18164-6_36
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DOI: https://doi.org/10.1007/978-3-319-18164-6_36
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18163-9
Online ISBN: 978-3-319-18164-6
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