File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/TNNLS.2023.3250655
- Scopus: eid_2-s2.0-85149828262
- WOS: WOS:000953404100001
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Memristive Circuit Implementation of Caenorhabditis Elegans Mechanism for Neuromorphic Computing
Title | Memristive Circuit Implementation of Caenorhabditis Elegans Mechanism for Neuromorphic Computing |
---|---|
Authors | |
Keywords | Caenorhabditis elegans circuit design memristor neuromorphic computing robot |
Issue Date | 10-Mar-2023 |
Publisher | Institute of Electrical and Electronics Engineers |
Citation | IEEE Transactions on Neural Networks and Learning Systems, 2023, p. 1-12 How to Cite? |
Abstract | To overcome the energy efficiency bottleneck of the von Neumann architecture and scaling limit of silicon transistors, an emerging but promising solution is neuromorphic computing, a new computing paradigm inspired by how biological neural networks handle the massive amount of information in a parallel and efficient way. Recently, there is a surge of interest in the nematode worm Caenorhabditis elegans (C. elegans), an ideal model organism to probe the mechanisms of biological neural networks. In this article, we propose a neuron model for C. elegans with leaky integrate-and-fire (LIF) dynamics and adjustable integration time. We utilize these neurons to build the C. elegans neural network according to their neural physiology, which comprises: 1) sensory modules; 2) interneuron modules; and 3) motoneuron modules. Leveraging these block designs, we develop a serpentine robot system, which mimics the locomotion behavior of C. elegans upon external stimulus. Moreover, experimental results of C. elegans neurons presented in this article reveals the robustness (1% error w.r.t. 10% random noise) and flexibility of our design in term of parameter setting. The work paves the way for future intelligent systems by mimicking the C. elegans neural system. |
Persistent Identifier | http://hdl.handle.net/10722/340342 |
ISSN | 2023 Impact Factor: 10.2 2023 SCImago Journal Rankings: 4.170 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chen, Hegan | - |
dc.contributor.author | Hong, Qinghui | - |
dc.contributor.author | Wang, Zhongrui | - |
dc.contributor.author | Wang, Chunhua | - |
dc.contributor.author | Zeng, Xiangxiang | - |
dc.contributor.author | Zhang, Jiliang | - |
dc.date.accessioned | 2024-03-11T10:43:27Z | - |
dc.date.available | 2024-03-11T10:43:27Z | - |
dc.date.issued | 2023-03-10 | - |
dc.identifier.citation | IEEE Transactions on Neural Networks and Learning Systems, 2023, p. 1-12 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.uri | http://hdl.handle.net/10722/340342 | - |
dc.description.abstract | <p>To overcome the energy efficiency bottleneck of the von Neumann architecture and scaling limit of silicon transistors, an emerging but promising solution is neuromorphic computing, a new computing paradigm inspired by how biological neural networks handle the massive amount of information in a parallel and efficient way. Recently, there is a surge of interest in the nematode worm Caenorhabditis elegans (C. elegans), an ideal model organism to probe the mechanisms of biological neural networks. In this article, we propose a neuron model for C. elegans with leaky integrate-and-fire (LIF) dynamics and adjustable integration time. We utilize these neurons to build the C. elegans neural network according to their neural physiology, which comprises: 1) sensory modules; 2) interneuron modules; and 3) motoneuron modules. Leveraging these block designs, we develop a serpentine robot system, which mimics the locomotion behavior of C. elegans upon external stimulus. Moreover, experimental results of C. elegans neurons presented in this article reveals the robustness (1% error w.r.t. 10% random noise) and flexibility of our design in term of parameter setting. The work paves the way for future intelligent systems by mimicking the C. elegans neural system.<br></p> | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.relation.ispartof | IEEE Transactions on Neural Networks and Learning Systems | - |
dc.subject | Caenorhabditis elegans | - |
dc.subject | circuit design | - |
dc.subject | memristor | - |
dc.subject | neuromorphic computing | - |
dc.subject | robot | - |
dc.title | Memristive Circuit Implementation of Caenorhabditis Elegans Mechanism for Neuromorphic Computing | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TNNLS.2023.3250655 | - |
dc.identifier.scopus | eid_2-s2.0-85149828262 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 12 | - |
dc.identifier.eissn | 2162-2388 | - |
dc.identifier.isi | WOS:000953404100001 | - |
dc.identifier.issnl | 2162-237X | - |