A Novel Hardware-Efficient Ergodic Sequential Logic Neuron Model: Cellular Differentiation Method and Virtual Clinical Trial of Neural Prosthesis
A novel sequential logic neuron model and its cellular differentiation method are presented. It is shown that the differentiation method enables the neuron model to reproduce typical nonlinear responses of a given neuron. Then a virtual clinical trial of neural prosthesis is executed, i.e., a target...
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| Veröffentlicht in: | IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2024-09, Vol.71 (9), p.4311-4315 |
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| container_title | IEEE transactions on circuits and systems. II, Express briefs |
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| creator | Shiomi, Yuta Suzuki, Haruto Torikai, Hiroyuki |
| description | A novel sequential logic neuron model and its cellular differentiation method are presented. It is shown that the differentiation method enables the neuron model to reproduce typical nonlinear responses of a given neuron. Then a virtual clinical trial of neural prosthesis is executed, i.e., a target neuron model in a network of biologically plausible differential equation neuron models is replaced with the presented neuron model that is differentiated to reproduce the target neuron model. It is shown that the prosthesis can recover typical neural behaviors of the network for a wide parameter range and has a generalization ability. The presented neuron model is implemented by a field programmable gate array and the virtual clinical trial is validated by experiments. It is concluded that the presented neuron model has better reproduction ability of neural phenomena and much higher hardware efficiency compared to a commonly used simplified differential equation neuron model. |
| doi_str_mv | 10.1109/TCSII.2024.3388549 |
| format | Article |
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It is concluded that the presented neuron model has better reproduction ability of neural phenomena and much higher hardware efficiency compared to a commonly used simplified differential equation neuron model.</description><identifier>ISSN: 1549-7747</identifier><identifier>EISSN: 1558-3791</identifier><identifier>DOI: 10.1109/TCSII.2024.3388549</identifier><identifier>CODEN: ITCSFK</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>asynchronous sequential logic ; Biological system modeling ; Brain modeling ; cellular differentiation ; Clocks ; Differential equations ; Differentiation (biology) ; Field programmable gate arrays ; FPGA ; Hardware ; Integrated circuit modeling ; Mathematical models ; Neural prostheses ; Neural prosthesis ; neuron model ; Neurons ; Nonlinear response ; Prosthetics</subject><ispartof>IEEE transactions on circuits and systems. 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It is concluded that the presented neuron model has better reproduction ability of neural phenomena and much higher hardware efficiency compared to a commonly used simplified differential equation neuron model.</description><subject>asynchronous sequential logic</subject><subject>Biological system modeling</subject><subject>Brain modeling</subject><subject>cellular differentiation</subject><subject>Clocks</subject><subject>Differential equations</subject><subject>Differentiation (biology)</subject><subject>Field programmable gate arrays</subject><subject>FPGA</subject><subject>Hardware</subject><subject>Integrated circuit modeling</subject><subject>Mathematical models</subject><subject>Neural prostheses</subject><subject>Neural prosthesis</subject><subject>neuron model</subject><subject>Neurons</subject><subject>Nonlinear response</subject><subject>Prosthetics</subject><issn>1549-7747</issn><issn>1558-3791</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNUMtOwzAQjBBIlMIPIA6WOKf4mcTcqlBopVKQWrhGjh-tqxAXJwHxGfwxTtsDp531zsyuJ4quERwhBPndKl_OZiMMMR0RkmWM8pNogBjLYpJydNpjyuM0pel5dNE0WwgxhwQPot8xWLgvXYGp8OpbeB1PjLHS6roFE792ykqw1J9d6K2owNytw8NCd97V4NkpXd2DXFdVVwkPHqwx2u-Zre3nut04BUStwLv1bRf0eWVrKwNY-d7Omb1XQK_eNe1GN7a5jM6MqBp9dazD6O1xssqn8fzlaZaP57HENG1jKsuk5ExgXCaIp4plXFMGiaQIl2nCCIUKKsFkiVKqeMJ5gjMhk9CZjHFDhtHtwXfnXfhf0xZb1_k6rCwIgojQIKCBhQ8sGQ5svDbFztsP4X8KBIs--mIffdFHXxyjD6Kbg8hqrf8JKA9XUPIHTj2A7Q</recordid><startdate>20240901</startdate><enddate>20240901</enddate><creator>Shiomi, Yuta</creator><creator>Suzuki, Haruto</creator><creator>Torikai, Hiroyuki</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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II, Express briefs</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Shiomi, Yuta</au><au>Suzuki, Haruto</au><au>Torikai, Hiroyuki</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Novel Hardware-Efficient Ergodic Sequential Logic Neuron Model: Cellular Differentiation Method and Virtual Clinical Trial of Neural Prosthesis</atitle><jtitle>IEEE transactions on circuits and systems. II, Express briefs</jtitle><stitle>TCSII</stitle><date>2024-09-01</date><risdate>2024</risdate><volume>71</volume><issue>9</issue><spage>4311</spage><epage>4315</epage><pages>4311-4315</pages><issn>1549-7747</issn><eissn>1558-3791</eissn><coden>ITCSFK</coden><abstract>A novel sequential logic neuron model and its cellular differentiation method are presented. It is shown that the differentiation method enables the neuron model to reproduce typical nonlinear responses of a given neuron. Then a virtual clinical trial of neural prosthesis is executed, i.e., a target neuron model in a network of biologically plausible differential equation neuron models is replaced with the presented neuron model that is differentiated to reproduce the target neuron model. It is shown that the prosthesis can recover typical neural behaviors of the network for a wide parameter range and has a generalization ability. The presented neuron model is implemented by a field programmable gate array and the virtual clinical trial is validated by experiments. It is concluded that the presented neuron model has better reproduction ability of neural phenomena and much higher hardware efficiency compared to a commonly used simplified differential equation neuron model.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TCSII.2024.3388549</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0002-1428-1355</orcidid><orcidid>https://orcid.org/0000-0003-2795-9628</orcidid></addata></record> |
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| ispartof | IEEE transactions on circuits and systems. II, Express briefs, 2024-09, Vol.71 (9), p.4311-4315 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | asynchronous sequential logic Biological system modeling Brain modeling cellular differentiation Clocks Differential equations Differentiation (biology) Field programmable gate arrays FPGA Hardware Integrated circuit modeling Mathematical models Neural prostheses Neural prosthesis neuron model Neurons Nonlinear response Prosthetics |
| title | A Novel Hardware-Efficient Ergodic Sequential Logic Neuron Model: Cellular Differentiation Method and Virtual Clinical Trial of Neural Prosthesis |
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