Memristive-synapse spiking neural networks based on single-electron transistors Article Swipe
Keliu Long
,
Xiaohong Zhang
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1007/s10825-019-01437-w
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1007/s10825-019-01437-w
In recent decades, with the rapid development of artificial intelligence technologies and bionic engineering, the spiking neural network (SNN), inspired by biological neural systems, has become one of the most promising research topics, enjoying numerous applications in various fields. Due to its complex structure, the simplification of SNN circuits requires serious consideration, along with their power consumption and space occupation. In this regard, the use of SSN circuits based on single-electron transistors (SETs) and modified memristor synapses is proposed herein. A prominent feature of SETs is Coulomb oscillation, which has characteristics similar to the pulses produced by spiking neurons. Here, a novel window function is used in the memristor model to improve the linearity of the memristor and solve the boundary and terminal lock problems. In addition, we modify the memristor synapse to achieve better weight control. Finally, to test the SNN constructed with SETs and memristor synapses, an associative memory learning process, including memory construction, loss, reconstruction, and change, is implemented in the circuit using the PSPICE simulator.
Related Topics
Concepts
Memristor
Computer science
Artificial neural network
Transistor
Memistor
Content-addressable memory
Physical neural network
Spiking neural network
Window function
Electronic circuit
Artificial intelligence
Electronic engineering
Resistive random-access memory
Voltage
Electrical engineering
Recurrent neural network
Types of artificial neural networks
Engineering
Telecommunications
Spectral density
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10825-019-01437-w
- https://link.springer.com/content/pdf/10.1007/s10825-019-01437-w.pdf
- OA Status
- hybrid
- Cited By
- 9
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2998659810
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2998659810Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s10825-019-01437-wDigital Object Identifier
- Title
-
Memristive-synapse spiking neural networks based on single-electron transistorsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-12-26Full publication date if available
- Authors
-
Keliu Long, Xiaohong ZhangList of authors in order
- Landing page
-
https://doi.org/10.1007/s10825-019-01437-wPublisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s10825-019-01437-w.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://link.springer.com/content/pdf/10.1007/s10825-019-01437-w.pdfDirect OA link when available
- Concepts
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Memristor, Computer science, Artificial neural network, Transistor, Memistor, Content-addressable memory, Physical neural network, Spiking neural network, Window function, Electronic circuit, Artificial intelligence, Electronic engineering, Resistive random-access memory, Voltage, Electrical engineering, Recurrent neural network, Types of artificial neural networks, Engineering, Telecommunications, Spectral densityTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
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2023: 3, 2022: 1, 2021: 3, 2020: 2Per-year citation counts (last 5 years)
- References (count)
-
51Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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