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Chaotic synchronization of neural networks in FPGA. (English) Zbl 1401.92012

Barone, Dante Augusto Couto (ed.) et al., Computational neuroscience. First Latin American workshop, LAWCN 2017, Porto Alegre, Brazil, November 22–24, 2017. Proceedings. Cham: Springer (ISBN 978-3-319-71010-5/pbk; 978-3-319-71011-2/ebook). Communications in Computer and Information Science 720, 17-30 (2017).
Summary: The objective of this work is to obtain a complete synchronization of Hopfield neural networks (HNN) with a delay using a field programmable gate array (FPGA) simulating in real-time a natural neural networks (NNN). This work is motivated by research in neurosciences involving the implantation of chips between the skull and the brain to prevent or ameliorate diseases such as Parkinson’s, epilepsy and depression. Our contribution is the introduction of new synchronization techniques based on the qualitative theory of differential equations, chaos theory and algebraic topology substituting calculations using the Lyapunov stability criterion (LSC). The presented technique does not depend on the neural networks to be synchronized but also presents a lower computational cost in comparison with previous works. The results show that FPGAs are good platforms for such experiments.
For the entire collection see [Zbl 1383.92004].

MSC:

92B20 Neural networks for/in biological studies, artificial life and related topics
92-08 Computational methods for problems pertaining to biology
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