Brian D. DePasquale, Ph.D.
Affiliations: | 2009-2016 | Center for Theoretical Neuroscience | Columbia University, New York, NY |
| 2016- | Princeton Neuroscience Institute | Princeton University, Princeton, NJ |
Area:
Computation & TheoryWebsite:
http://www.princeton.edu/~briandd/Google:
"Brian DePasquale"Mean distance: 14.16 (cluster 17) | S | N | B | C | P |
Parents
Sign in to add mentor| Ann M. Graybiel | research assistant | 2005-2009 | MIT |
| Larry F. Abbott | grad student | 2009-2016 | Columbia |
| Carlos D. Brody | post-doc | 2016- | Princeton |
| Jonathan W. Pillow | post-doc | 2016- | Princeton |
Collaborators
Sign in to add collaborator| Mark M. Churchland | collaborator | ||
| Michele N. Insanally | collaborator | Princeton |
BETA: Related publications
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Publications
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| Gupta D, DePasquale B, Kopec CD, et al. (2023) Trial-history biases in evidence accumulation can give rise to apparent lapses. Biorxiv : the Preprint Server For Biology |
| DePasquale B, Sussillo D, Abbott LF, et al. (2023) The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks. Neuron |
| Pinto L, Rajan K, DePasquale B, et al. (2019) Task-Dependent Changes in the Large-Scale Dynamics and Necessity of Cortical Regions. Neuron |
| Panichello MF, DePasquale B, Pillow JW, et al. (2019) Error-correcting dynamics in visual working memory. Nature Communications. 10: 3366 |
| Insanally MN, Carcea I, Field RE, et al. (2019) Spike-timing-dependent ensemble encoding by non-classically responsive cortical neurons. Elife. 8 |
| DePasquale B. (2019) Decision letter: Local online learning in recurrent networks with random feedback Elife |
| Insanally MN, Carcea I, Field RE, et al. (2019) Author response: Spike-timing-dependent ensemble encoding by non-classically responsive cortical neurons Elife |
| DePasquale B, Cueva CJ, Rajan K, et al. (2018) full-FORCE: A target-based method for training recurrent networks. Plos One. 13: e0191527 |
| Panichello M, DePasquale B, Pillow J, et al. (2018) Memory load modulates the dynamics of visual working memory. Journal of Vision. 18: 189 |
| Abbott LF, DePasquale B, Memmesheimer RM. (2016) Building functional networks of spiking model neurons. Nature Neuroscience. 19: 350-5 |