Brian DePasquale

Postdoctoral Research Associate
Princeton Neuroscience Institute
Princeton University
depasquale {at} princeton {dot} edu

I conduct research in theoretical neuroscience and machine learning. Presently, I work with Carlos Brody and Jonathan Pillow on latent variable models of evidence accumulation. During my Ph.D. I worked with Larry Abbott on supervised learning in recurrent neural networks. Before that I studied the basal ganglia in the laboratory of Ann Graybiel.

Publications

Panichello, M.F., DePasquale, B., Pillow, J.W. & Buschman, T.J. (2018). Error-correcting dynamics in visual working memory. [bioRxiv]

Insanally, M.N., Carcea, I., Field, R.E., Rodgers, C., DePasquale, B., Rajan, K., DeWeese, M.R., Albanna, B.F. & Froemke, R.C. (2018). Nominally non-responsive frontal and sensory cortical cells encode task-relevant variables via ensemble consensus-building. [bioRxiv]

DePasquale, B., Cueva, C.J., Rajan, K., Escola, G.S. & Abbott, L.F. (2018). full-FORCE: A target-based method for training recurrent networks. PLoS One 13(2): e0191527. [journal | pdf | git | press]

DePasquale, B. (2016). Methods for Building Network Models of Neural Circuits. Ph.D. thesis, Columbia University. [Columbia digital repository | pdf]

Abbott, L.F., DePasquale, B., Memmesheimer, R.-M. (2016). Building functional networks of spiking model neurons. Nature Neuroscience 19:350-355. [journal | pdf]

DePasquale, B., Churchland, M.M. & Abbott, L.F. (2016). Using firing-rate dynamics to train recurrent networks of spiking model neurons. arXiv:1601.07620. [arXiv | git]

Feingold, J., Gibson, D.J., DePasquale, B. & Graybiel, A.M. (2015). 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. [journal | pdf]

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