2018 Conference on Cognitive Computational Neuroscience

5-8 September 2018    Philadelphia, Pennsylvania

Technical Program

Paper Detail

Paper: PS-2B.4
Session: Poster Session 2B
Location: Symphony/Overture
Session Time: Friday, September 7, 19:30 - 21:30
Presentation Time:Friday, September 7, 19:30 - 21:30
Presentation: Poster
Publication: 2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania
Paper Title: Additive Continuous-time Joint Partitioning of Neural Variability
Manuscript:  Click here to view manuscript
DOI: https://doi.org/10.32470/CCN.2018.1087-0
Authors: Adam Charles, Jonathan Pillow, Princeton University, United States
Abstract: Accurate estimation of neural spike-rates is challenging due to fact that both stimulus-dependent spike-rates and trial-by-trial noise are continuously time-varying and that neural spiking is well known to exhibit super- or sub-Poisson behavior. In particular, the time-varying nature of the noise makes spike-count statistics sensitive to choices in temporal bin-size selection (Cohen & Kohn, 2011). While methods have been proposed for both binless rate estimation and non-Poisson activity (R. Goris, Movshon, & Simoncelli, 2014; Charles, Park, Weller, Horwitz, & Pillow, 2018), no current over-dispersion model can perform arbitrary continuous-time rate estimation. We present here such a model, where we model the stimulus-based rate as a Gaussian Process (GP), and the rate driving the observed spiking is an additive combination of the stimulus GP and a noise process (also modeled as a GP), passed through a rectifying nonlinearity. Our model significantly generalizes previous over-dispersion models by both removing the bin-size dependence, as well as allowing estimation of the latent continuous-time spike-rates. Our model also explains the difference in statistics across bin sizes by accounting for temporal correlations. Given the noise parameters, we can estimate the stimulus GP via a maximum a-posteriori optimization, using a Laplace approximation to marginalize over the noise instantiations. We demonstrate out model both on simulated data as well as macaque V1 activity.