Stochastic template in cellular nonlinear networks modeling memristor induced synaptic noise
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Noise is one of the most challenging aspects of cellular nonlinear networks adversely affecting their functionality. Existing techniques to addressing the issues posed by noise are based on well-understood noise removal methods that have reached technical maturity and further have the disadvantage of limited success rates. A deeper understanding and modeling of noise dynamics and its origins are required for the efficient identification and resolution of problems in different network applications. The Stochastic template concept in this article can be beneficial in understanding and modeling noise dynamics in cellular nonlinear networks, which is critical for addressing challenges in network applications. In this paper, memristors functioning as synapses introduce noise into networks, and we conduct an initial investigation of a noisy network performing edge detection.
Details
Original language | English |
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Title of host publication | Proceedings of the 18th ACM International Symposium on Nanoscale Architectures, NANOARCH 2023 |
Publisher | Association for Computing Machinery |
ISBN (electronic) | 9798400703256 |
Publication status | Published - 18 Dec 2023 |
Peer-reviewed | Yes |
Publication series
Series | ACM International Conference Proceeding Series |
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Conference
Title | 18th ACM International Symposium on Nanoscale Architectures |
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Abbreviated title | NANOARCH 2023 |
Conference number | 18 |
Duration | 18 - 20 December 2023 |
Website | |
Location | Technische Universität Dresden |
City | Dresden |
Country | Germany |
External IDs
ORCID | /0000-0002-1236-1300/work/154191445 |
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ORCID | /0000-0001-7436-0103/work/154191787 |
ORCID | /0000-0002-6200-4707/work/154192566 |
ORCID | /0000-0002-2367-5567/work/168720266 |
Keywords
ASJC Scopus subject areas
Keywords
- Cellular Nonlinear Networks, Memristive Synapses, Noise