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.

As of Janurary 2023, I have started as an assistant professor in the Department of Biomedical Engineering at Boston University.

Publications & Preprints [google scholar]

DePasquale, B, Sussillo, D., Abbott, L.F., Churchland, M.M. (2023). The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks. in press at Neuron [journal]

DePasquale, B., Brody, C.D., Pillow, J. (2022) Neural population dynamics underlying evidence accumulation in multiple rat brain regions. bioRxiv, in revision at eLife. [bioRxiv]

Cohen Z, DePasquale, B., Aoi, M., Pillow, J. (2020). Recurrent dynamics of prefrontal cortex during context-dependent decision-making. bioRxiv, [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

A New Era for the Neuroscience of Social Behavior (news and commentary on Willmore et al Nature 2022, Pereira et al Nature Methods 2022, & Nair et al BioRxiv 2022). Simons Collaboration on the Global Brain. [link]

Searching for Shapes in Neural Activity (news and commentary on Gardner et al Nature 2022 & Nieh, Schottdorf et al Nature 2021). Simons Collaboration on the Global Brain. [link]

Hippocampal Replay: Reflection on the Past or Planning for the Future (news and commentary on Gillespie et al Neuron 2021). Simons Collaboration on the Global Brain. [link]

Scoring the Brain: How Benchmark Datasets and Other Tools are Solving Key Challenges in Neuroscience. Simons Collaboration on the Global Brain. [link]

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]