Brian D. DePasquale

Brian D. DePasquale

Brian Postdoc, Princeton Neuroscience Institute, Princeton University
PhD Neurobiology & Behavior 2016, Center for Theoretical Neuroscience, Columbia University
BS Physics 2005, Fordham University
depasquale [at] princeton [dot] edu

I am working with Jonathan Pillow and Carlos Brody on latent variable models of evidence accumulation. Before this I worked with Larry Abbott on supervised learning in spiking neural networks. My PhD thesis Methods for building network models of neural circuits can be viewed here. Before that I was research assistant in Ann Graybiel's lab at MIT.


[5] B DePasquale, CJ Cueva, RM Memmesheimer, LF Abbott, GS Escola. Full-FORCE: A least-squares algorithm for training recurrently connected neural networks. submitted (2016)

[4] B DePasquale, MM Churchland, LF Abbott. Using firing-rate dynamics to train recurrent networks of spiking model neurons. arXiv preprint arXiv:1601.07620. (2015) [PDF].

[3] LF Abbott, B DePasquale, RM Memmesheimer. Building functional networks of spiking model neurons. Nature Neurosci. 19:350-355 (2015) [PDF].

[2] J Feingold, DJ Gibson, B DePasquale, AM Graybiel. Bursts of beta oscillation differentiate postperformance activity in the striatum and motor cortex of monkeys performing movement tasks. Proceedings of the National Academy of Sciences 112(44):13687-13692 (2015) [PDF].

[1] L Paninski, M Vidne, B DePasquale, DG Ferreira. Inferring synaptic inputs given a noisy voltage trace via sequential Monte Carlo methods. Journal of Computational Neuroscience 33(1):1-19 (2012) [PDF].

A list of my publications is also available on my Google scholar page.


Full-FORCE [git]