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Bio-inspired Neural Networks

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Memristor Networks

Abstract

We describe a biological network and the principal mechanisms that are responsible for learning and memory. We start with a description of the morphology of these networks and their components, such as neurons and synapses. Then, we will identify crucial components of the information processing, such as ion flux and the induced mechanisms, e.g., long-term potentiation and depression. Next, we will compare the behaviour of a memristive system with the mechanisms identified in biological systems and present corresponding experiments and a few simulations. Finally, we will present more abstract ways of using memristors to solve complex problems.

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Acknowledgements

The authors would like to thank Barbara Kaltschmidt and Elisabetta Chicca for many helpful discussions. Furthermore, we are grateful to Patryk Krzysteczko, Jana Münchenberger, Stefan Niehörster, Matthias Schürmann, Marius Schirmer, Olga Simon, Savio Fabretti, and Joachim Sterz. Additionally, we would like to thank Günter Reiss and Andreas Hütten for the continuous support of our work. The Ministry of Innovation, Science and Research (MIWF) of North Rhine-Westfalia funded this research.

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  1. Physics Department, Bielefeld University, Bielefeld, Germany

    Andy Thomas

  2. Physics Department, Osnabrück University, Osnabrück, Germany

    Andy Thomas

  3. Biology Department, Bielefeld University, Bielefeld, Germany

    Christian Kaltschmidt

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  1. Computer Science Department, University of the West of England, Bristol, United Kingdom

    Andrew Adamatzky

  2. Electrical Engineering and Computer Sciences Department, University of California, Berkeley, California, USA

    Leon Chua

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Thomas, A., Kaltschmidt, C. (2014). Bio-inspired Neural Networks. In: Adamatzky, A., Chua, L. (eds) Memristor Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-02630-5_9

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