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            Bio-Inspired Information Pathways

            From Neuroscience to Neurotronics

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            Contributor(s)
            Ziegler, Martin (editor)
            Mussenbrock, Thomas (editor)
            Kohlstedt, Hermann (editor)
            Language
            English
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            Abstract
            This open access book offers a timely and comprehensive review of the field of neurotronics. Gathering cutting-edge contributions from neuroscientists, biologists, psychologists, as well as physicists, microelectronics engineers and information scientists, it gives extensive information on fundamental information pathways in selected nervous systems. It also highlights their relevance as building blocks for novel computing architectures, such as bio-inspired electronic devices, neuromorphic architectures, memristive devices, adaptive sensors and emergent, pulsed-coupled oscillatory networks. All in all, this book offers a unique bridge between fundamental research in neuroscience, neural information processing, nonlinear dynamics, and self-organization, and advanced practical applications concerning the fabrication of hardware-oriented computing.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/176118
            Keywords
            Neuromorphic Computing; Self-assembled Networks; Oscillator Networks; Simulation of Memristive Devices; Hippocampal Learning Models; Artificial Synapse; Neuronal Synchrony; Memristive and Memsensor Devices; Bio-inspired Electronics; Bio-inspired Decision Making Systems; Smart Sensor Arrays; Neuromorphic Sensing; CRC 1461
            DOI
            10.1007/978-3-031-36705-2
            ISBN
            9783031367052, 9783031367045
            Publisher
            Springer Nature
            Publisher website
            http://www.springernature.com/oabooks
            Publication date and place
            Cham, 2024
            Imprint
            Springer International Publishing
            Series
            Springer Series on Bio- and Neurosystems,
            Pages
            433
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              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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