On July 28th, I presented the following paper in lab meeting:

- Efficient and direct estimation of a neural subunit model for sensory coding,

Vintch, Zaharia, Movshon, & Simoncelli,*NIPS*2012

This paper proposes a new method for characterizing the multi-dimensional stimulus selectivity of sensory neurons. The main idea is that, instead of thinking of neurons as projecting high-dimensional stimuli into an arbitrary low-dimensional feature space (the view underlying characterization methods like STA, STC, iSTAC, GQM, MID, and all their rosy-cheeked cousins), it might be more useful / parsimonious to think of neurons as performing a projection onto convolutional *subunits*. That is, rather than characterizing stimulus selectivity in terms of a bank of arbitrary linear filters, it might be better to consider a subspace defined by translated copies of a *single* linear filter.