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10 September 2019 Biological plausibility in organic neuromorphic devices: from global phenomena to synchronization functions (Conference Presentation)
Paschalis Gkoupidenis
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Proceedings Volume 11096, Organic and Hybrid Sensors and Bioelectronics XII; 110960K (2019) https://doi.org/10.1117/12.2526392
Event: SPIE Organic Photonics + Electronics, 2019, San Diego, California, United States
Abstract
It is now well recognized that traditional computing systems based on von Neumann architecture are not efficient enough to manipulate and process the massive amount of data produced by the contemporary information technologies. A shifting paradigm from the traditional computing systems is the emulation of the brain computational efficiency at the hardware-based level, a field that is also known as neuromorphic computing. Although neuromorphic computing with inorganic materials has been advanced over the past years, nevertheless biological plausibility is questionable in many cases of solid-state technologies. In the brain, for instance, neural populations are immersed in a common electrolyte or cerebrospinal fluid and this fact equips the brain with more efficient features in processing when compared to electronic devices or circuits. Due to this topology in biological neural networks, higher order phenomena exist such as global regulation of neural activity and communication between different regions in the brain mediated by the presence of the global electrolyte. In this work, device concepts will be presented that lead to biological plausibility in organic neuromorphic devices, including global phenomena and synchronization functions. Introducing this level of biological plausibility, paves the way for new concepts of neuromorphic communication between different subunits in a circuit.
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Paschalis Gkoupidenis "Biological plausibility in organic neuromorphic devices: from global phenomena to synchronization functions (Conference Presentation)", Proc. SPIE 11096, Organic and Hybrid Sensors and Bioelectronics XII, 110960K (10 September 2019); https://doi.org/10.1117/12.2526392
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KEYWORDS
Brain
Computing systems
Biological neural networks
Computer architecture
Electronic circuits
Electronic components
Information technology
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