Zobrazeno 1 - 6
of 6
pro vyhledávání: '"David H Terman"'
Autor:
Sharmila Venugopal, Soju Seki, David H Terman, Antonios Pantazis, Riccardo Olcese, Martina Wiedau-Pazos, Scott H Chandler
Publikováno v:
PLoS Computational Biology, Vol 15, Iss 6, p e1007154 (2019)
Neurons utilize bursts of action potentials as an efficient and reliable way to encode information. It is likely that the intrinsic membrane properties of neurons involved in burst generation may also participate in preserving its temporal features.
Externí odkaz:
https://doaj.org/article/9be6bbeeb4bc43f49cc3eca91cea0662
Autor:
G. Bard Ermentrout, David H. Terman
Publikováno v:
Interdisciplinary Applied Mathematics ISBN: 9780387877075
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c42d1732846cc17bfe27040cf81a2289
https://doi.org/10.1007/978-0-387-87708-2_7
https://doi.org/10.1007/978-0-387-87708-2_7
Autor:
G. Bard Ermentrout, David H. Terman
Publikováno v:
Interdisciplinary Applied Mathematics ISBN: 9780387877075
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9017ae026ef599e5e7a4648723ff9c61
https://doi.org/10.1007/978-0-387-87708-2_2
https://doi.org/10.1007/978-0-387-87708-2_2
Autor:
G. Bard Ermentrout, David H. Terman
Publikováno v:
Interdisciplinary Applied Mathematics ISBN: 9780387877075
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1c83630ca6b55ed924637d508dae6130
https://doi.org/10.1007/978-0-387-87708-2_10
https://doi.org/10.1007/978-0-387-87708-2_10
Autor:
G. Bard Ermentrout, David H. Terman
Publikováno v:
Interdisciplinary Applied Mathematics ISBN: 9780387877075
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8c4b41623a9c2316ff8b8a3cb525104c
https://doi.org/10.1007/978-0-387-87708-2_5
https://doi.org/10.1007/978-0-387-87708-2_5
Autor:
G. Bard Ermentrout, David H. Terman
This book applies methods from nonlinear dynamics to problems in neuroscience. It uses modern mathematical approaches to understand patterns of neuronal activity seen in experiments and models of neuronal behavior. The intended audience is researcher