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Brian D. DePasquale, Ph.D.

2009-2016 Center for Theoretical Neuroscience Columbia University, New York, NY 
 2016- Princeton Neuroscience Institute Princeton University, Princeton, NJ 
Computation & Theory
"Brian DePasquale"
Mean distance: 14.16 (cluster 17)


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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
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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
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