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.
 B DePasquale, CJ Cueva, K Rajan, GS Escola, LF Abbott. full-FORCE: A target-based method for training recurrent networks. arXiv preprint arXiv:1710:03070 (2017) [PDF].
 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].
 LF Abbott, B DePasquale, RM Memmesheimer. Building functional networks of spiking model neurons. Nature Neurosci. 19:350-355 (2015) [PDF].
 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].
 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 learning [git]