Funes, or parallax

In a CSHL lecture on attention, Marisa Carrasco used the fictional Funes as an illustration of the idea that perception is about prioritizing and throwing data away. Funes the Memorious is a famous short story by Borges about a man with a photographic memory that couldn’t make sense of the world as he couldn’t throw … More Funes, or parallax

Adam Kohn on population coding

Adam delivered a pretty intense lecture at CSHL on population coding, correlations and phase-locking. Consider myself mindfucked. Mainen & Sejnowski (1995) showed that single neurons have very reliable responses to current injections. Nevertheless, cortical neurons seem to have Poisson or supra-Poisson variability. It’s possible to find a bound on decodability using the Fisher information matrix (Sompolinsky … More Adam Kohn on population coding

Geoff Boynton on fMRI

Geoff just delivered a lecture at CSHL computational vision on fMRI. He pointed out that it’s an incredibly convenient coincidence that hemoglobin and deoxyhemoglobin have sufficiently different magnetic moments that they can be picked up using MRI. I made a comment (which I thought was mind-blowing but others thought was funny; it wasn’t a joke, … More Geoff Boynton on fMRI

Fitting a spline nonlinearity in a Poisson model

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

CSHL computational vision: day 4

Today was a little less intense than yesterday, mercifully. Geoff Boynton Geoff did a tutorial on signal detection theory and estimating psychophysical measures in Matlab. He emphasized that given the signal detection model, it is easy to find good estimates using Bayesian inference. Whenever the observer’s response is binary, you should use the binomial likelihood … More CSHL computational vision: day 4