Category: GLMs

SFN 2014 poster – converging encoding strategies in dorsal and ventral visual streams

November 14, 2014

I have a poster session on Sunday afternoon at SFN 2014 in DC. It’s on a spiffy new method I’ve been working on for estimating the nonlinear transformation performed by an ensemble of sensory neurons, and its application to understanding visual representation in the dorsal and ventral visual streams. Some background: there’s a growing consensus … More SFN 2014 poster – converging encoding strategies in dorsal and ventral visual streams

PhD thesis – Parametric Models of Visual Cortex at Multiple Scales

April 2, 2014

*Updated Sun April 6th* Well it’s done! I successfully defended my thesis on April 3rd. I now have what I’ve been longing for all these years – an obnoxious title that I can remind people of whenever I’m about to lose an argument. Unfortunately, this only works when erguing with non-PhDs. Entitled Parametric Models of … More PhD thesis – Parametric Models of Visual Cortex at Multiple Scales

Why second-order methods can be futile in non-convex problems

April 4, 2013

I’ve been working on fitting a convolutional model of neurons in primary and intermediate visual cortex. A non-convex optimization problem must be solved to estimate the parameters of the model. It has a form similar to: There are some shared weights to further complicate things, but the most salient features is that it’s a 3-layer … More Why second-order methods can be futile in non-convex problems

Fitting a spline nonlinearity in a Poisson model

June 25, 2012

I was talking to Jeremy Freeman at CSHL and he asked about an easy way to fit a spline nonlinearity in a Poisson regression model. Recall that with the canonical exponential nonlinearity, we have the following setup: And the negative log-likelihood is given by: Start by fitting w by maximum likelihood. Compute . Then you … More Fitting a spline nonlinearity in a Poisson model