no code implementations • ICML 2020 • Stephen Keeley, David Zoltowski, Jonathan Pillow, Spencer Smith, Yiyi Yu
Gaussian Process Factor Analysis (GPFA) hasbeen broadly applied to the problem of identi-fying smooth, low-dimensional temporal struc-ture underlying large-scale neural recordings. However, spike trains are non-Gaussian, whichmotivates combining GPFA with discrete ob-servation models for binned spike count data. The drawback to this approach is that GPFApriors are not conjugate to count model like-lihoods, which makes inference challenging. Here we address this obstacle by introduc-ing a fast, approximate inference method fornon-conjugate GPFA models.
no code implementations • ICML 2020 • David Zoltowski, Jonathan Pillow, Scott Linderman
An open question in systems and computational neuroscience is how neural circuits accumulate evidence towards a decision.
no code implementations • NeurIPS 2021 • David Zoltowski, Diana Cai, Ryan P. Adams
Slice sampling is a Markov chain Monte Carlo algorithm for simulating samples from probability distributions; it only requires a density function that can be evaluated point-wise up to a normalization constant, making it applicable to a variety of inference problems and unnormalized models.
1 code implementation • 9 Sep 2021 • Felix Pei, Joel Ye, David Zoltowski, Anqi Wu, Raeed H. Chowdhury, Hansem Sohn, Joseph E. O'Doherty, Krishna V. Shenoy, Matthew T. Kaufman, Mark Churchland, Mehrdad Jazayeri, Lee E. Miller, Jonathan Pillow, Il Memming Park, Eva L. Dyer, Chethan Pandarinath
We curate four datasets of neural spiking activity from cognitive, sensory, and motor areas to promote models that apply to the wide variety of activity seen across these areas.
no code implementations • NeurIPS 2018 • David Zoltowski, Jonathan W. Pillow
We use the quadratic estimator to fit a fully-coupled Poisson GLM to spike train data recorded from 831 neurons across five regions of the mouse brain for a duration of 41 minutes, binned at 1 ms resolution.