Nanoscale Electronic Processing of Artificial Network of Synapse for Neuromorphic Memristor Computing
The principle of brain signal communication is to execute actions, diverse to a novel neuroelectronics field. The action execution is based on neuron and synapse, and this phenomenon applies in electronics to build brain-inspired chips. In this paper, we propose a neuromorphic memristor based on genetic algorithm optimization in the artificial neural network. We consider brain neurons and its synapses to be electronic components. The neuron as an electronic system and memristor, considered as a fourth circuit element as synapse as an interconnect between the neurons. The models for neuron and synapse systems are with memristor neuromorphic model. The simulation results show the associate memory action for increased number of neurons. The complexity of associate memory is of 83% (10/12) decision outcomes. The cyclomatic complexity coverage is of 7 (inc. descendants).
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Document Type: Research Article
Affiliations: Department of Electronics and Communication, SRM University, Kattankulathur 603203, Tamil Nadu, India
Publication date: 2017-07-01
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- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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