Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Matt Deible"'
Autor:
Daniel Haşegan, Matt Deible, Christopher Earl, David D’Onofrio, Hananel Hazan, Haroon Anwar, Samuel A. Neymotin
Publikováno v:
Frontiers in Computational Neuroscience, Vol 16 (2022)
Artificial neural networks (ANNs) have been successfully trained to perform a wide range of sensory-motor behaviors. In contrast, the performance of spiking neuronal network (SNN) models trained to perform similar behaviors remains relatively subopti
Externí odkaz:
https://doaj.org/article/e48300e666124b53b4fabec9e6c8dc41
Autor:
Haroon Anwar, Simon Caby, Salvador Dura-Bernal, David D’Onofrio, Daniel Hasegan, Matt Deible, Sara Grunblatt, George L. Chadderdon, Cliff C. Kerr, Peter Lakatos, William W. Lytton, Hananel Hazan, Samuel A. Neymotin
Publikováno v:
PLoS ONE, Vol 17, Iss 5 (2022)
Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism. Most of these models have not been tested in dynamic visual enviro
Externí odkaz:
https://doaj.org/article/1b4f56c6ca6644df967cc5c65d03cb5f
Autor:
Christopher Earl, Matt Deible, Samuel A. Neymotin, Haroon Anwar, Daniel Hasegan, David D’Onofrio, Hananel Hazan
Biological learning operates at multiple interlocking timescales, from long evolutionary stretches down to the relatively short time span of an individual9s life. While each process has been simulated individually as a basic learning algorithm in the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ad21e007ccf6fa411bf1054a67d3230f
https://doi.org/10.1101/2021.11.20.469405
https://doi.org/10.1101/2021.11.20.469405
Autor:
Haroon Anwar, Simon Caby, Salvador Dura-Bernal, David D’Onofrio, Daniel Hasegan, Matt Deible, Sara Grunblatt, George L. Chadderdon, Cliff C. Kerr, Peter Lakatos, William W. Lytton, Hananel Hazan, Samuel A. Neymotin
Publikováno v:
PloS one. 17(5)
Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism. Most of these models have not been tested in dynamic visual enviro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e2a72ea4d21ff4cd12c27d160ce0c38
https://pubmed.ncbi.nlm.nih.gov/35544518
https://pubmed.ncbi.nlm.nih.gov/35544518
Autor:
Samuel A. Neymotin, Matt Deible, Salvador Dura-Bernal, Hananel Hazan, George L. Chadderdon, Haroon Anwar, Cliff C. Kerr, Daniel Hasegan, David D’Onofrio, Simon Caby, Sara Grunblatt, Peter Lakatos, William W. Lytton
Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism. Most of these models have not been tested in dynamic visual enviro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a380db508560d3e0ed9c7da7f1ddb9fa
https://doi.org/10.1101/2021.07.29.454361
https://doi.org/10.1101/2021.07.29.454361