Memristor-based neural networks

Thomas A (2013)
Journal of Physics D Applied Physics 46(9): 093001.

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Zeitschriftenaufsatz | Veröffentlicht | Englisch
Abstract / Bemerkung
The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them.
Erscheinungsjahr
Zeitschriftentitel
Journal of Physics D Applied Physics
Band
46
Zeitschriftennummer
9
Seite
093001
ISSN
eISSN
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Thomas A. Memristor-based neural networks. Journal of Physics D Applied Physics. 2013;46(9):093001.
Thomas, A. (2013). Memristor-based neural networks. Journal of Physics D Applied Physics, 46(9), 093001. doi:10.1088/0022-3727/46/9/093001
Thomas, A. (2013). Memristor-based neural networks. Journal of Physics D Applied Physics 46, 093001.
Thomas, A., 2013. Memristor-based neural networks. Journal of Physics D Applied Physics, 46(9), p 093001.
A. Thomas, “Memristor-based neural networks”, Journal of Physics D Applied Physics, vol. 46, 2013, pp. 093001.
Thomas, A.: Memristor-based neural networks. Journal of Physics D Applied Physics. 46, 093001 (2013).
Thomas, Andy. “Memristor-based neural networks”. Journal of Physics D Applied Physics 46.9 (2013): 093001.