Abstract
Due to their intrinsic properties, memristors can be viewed as resistors in which the internal conductance is modulated by an external signal with the possibility to remember the previous state. This behavior is closely related to the functionality of the synapses and, for this reason, memristors have recently gained increasing attention also for their use as synapse in artificial neural networks for neuromorphic processing. As the most difficult step in the implementation of artificial neural networks is the realization of synapses, which often require a large number of transistors, the recent demonstration of the memory effect in memristors suggested a possible realization of synapses at the nanoscale with low power consumption and small size. In this work we explore the idea of using memristors as a synapse in a complex network to take advantage of the dynamics introduced by them and, in particular, propose a coupling scheme consisting of two HP memristors connected in antiparallel to achieve adaptive synchronization in two coupled Rössler systems.
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Frasca, M., Gambuzza, L.V., Buscarino, A., Fortuna, L. (2015). Memristor Based Adaptive Coupling for Synchronization of Two Rössler Systems. 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_39
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DOI: https://doi.org/10.1007/978-3-319-18164-6_39
Publisher Name: Springer, Cham
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