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- Publisher Website: 10.1109/CSTIC49141.2020.9282494
- Scopus: eid_2-s2.0-85099237867
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Conference Paper: Neural Spike Detection Based on 1T1R Memristor
| Title | Neural Spike Detection Based on 1T1R Memristor |
|---|---|
| Authors | |
| Issue Date | 2020 |
| Citation | China Semiconductor Technology International Conference 2020, CSTIC 2020, 2020, article no. 9282494 How to Cite? |
| Abstract | Neural spike detection is an important step to study the working principles of the brain and construct useful neuroprosthetics for patients with neurological diseases. However, the high power consumption of conventional electronic hardware hinders the way towards highly efficient neuroprosthetics. In this paper, an energy-efficient neural spike detector based on 1T1R memristor is presented. 69.12% TPR and 19.35% FPR can be achieved when benchmarked with a conventional method. Compared with traditional neural spike detectors, our memristor-based spike detector has shown significant advantages in both power consumption (84.5 nW compared to 815 nW in conventional hardware) and data transmission bandwidth (200× compression of raw data). |
| Persistent Identifier | http://hdl.handle.net/10722/334722 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Liu, Zhengwu | - |
| dc.contributor.author | Tang, Jianshi | - |
| dc.contributor.author | Gao, Bin | - |
| dc.contributor.author | Qian, He | - |
| dc.contributor.author | Wu, Huaqiang | - |
| dc.date.accessioned | 2023-10-20T06:50:11Z | - |
| dc.date.available | 2023-10-20T06:50:11Z | - |
| dc.date.issued | 2020 | - |
| dc.identifier.citation | China Semiconductor Technology International Conference 2020, CSTIC 2020, 2020, article no. 9282494 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/334722 | - |
| dc.description.abstract | Neural spike detection is an important step to study the working principles of the brain and construct useful neuroprosthetics for patients with neurological diseases. However, the high power consumption of conventional electronic hardware hinders the way towards highly efficient neuroprosthetics. In this paper, an energy-efficient neural spike detector based on 1T1R memristor is presented. 69.12% TPR and 19.35% FPR can be achieved when benchmarked with a conventional method. Compared with traditional neural spike detectors, our memristor-based spike detector has shown significant advantages in both power consumption (84.5 nW compared to 815 nW in conventional hardware) and data transmission bandwidth (200× compression of raw data). | - |
| dc.language | eng | - |
| dc.relation.ispartof | China Semiconductor Technology International Conference 2020, CSTIC 2020 | - |
| dc.title | Neural Spike Detection Based on 1T1R Memristor | - |
| dc.type | Conference_Paper | - |
| dc.description.nature | link_to_subscribed_fulltext | - |
| dc.identifier.doi | 10.1109/CSTIC49141.2020.9282494 | - |
| dc.identifier.scopus | eid_2-s2.0-85099237867 | - |
| dc.identifier.spage | article no. 9282494 | - |
| dc.identifier.epage | article no. 9282494 | - |