Brian DePasquale [CV]

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

I conduct research in theoretical neuroscience and machine learning. My research uses mathematical models to characterize and explain how populations of neurons perform computations to produce behavior. 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 developing methods for training recurrent neural networks, and with Mark Churchland connecting these models conceptually to statistical models of low-dimensional dynamics applied to data. Before that I studied the basal ganglia in the laboratory of Ann Graybiel.

In addition to doing science, I sometimes write about other people's science, and also try to contribute to open-source computing libraries.

Manuscripts

DePasquale, B., Brody, C.D., Pillow, J. (2021) Neural population dynamics underlying evidence accumulation in multiple rat brain regions. in preparation

DePasquale, B, Sussillo, D., Churchland, M.M., Abbott, L.F. (2021). The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks. in revision

Publications & Preprints [google scholar]

Cohen Z, DePasquale, B., Aoi, M., Pillow, J. (2020). Recurrent dynamics of prefrontal cortex during context-dependent decision-making. bioRxiv, https://doi.org/10.1101/2020.11.27.401539. [bioRxiv]

Pinto, L., Rajan, K., DePasquale, B., Thiberge, S.Y., Tank, D.W., Brody, C.D. (2019). Task-dependent changes in the large-scale dynamics and necessity of cortical regions. Neuron, 104(4), 810-824. e9. [journal]

Panichello, M.F., DePasquale, B., Pillow, J.W. & Buschman, T.J. (2018). Error-correcting dynamics in visual working memory. Nature Communications 10, Article number: 3366 [journal]

Insanally, M.N., Carcea, I., Field, R.E., Rodgers, C., DePasquale, B., Rajan, K., DeWeese, M.R., Albanna, B.F. & Froemke, R.C. (2018). Spike-timing-dependent ensemble encoding by non-classically responsive cortical neurons. eLife 8, e42409. [bioRxiv | journal]

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]

Science Writing

Geometrical Thinking Offers a Window Into Computation. (news and commentary on Bernardi, Benna et al. Cell 2020 & Russo et al. Neuron 2020). Simons Collaboration on the Global Brain. [link]

In Olfactory System, a Balance of Randomness and Order. (news and commentary on Pashkovski et. al. Nature 2020). Simons Collaboration on the Global Brain. [link]

Open-source Contributions

AdvancedMH.jl (numerical methods for MCMC in Julia) [link]