The question, at this point, is not how do IT cells respond to visual stimuli, or what the biophysical properties of IT cells are (though this would be wonderful, insanely useful data to have). We have a truckload of electrophysiological data, even if some of it falls prey to the sort of difficulties pointed out in an article from Pinto et al. in PLoS. I think the general problem is that we’re looking at a filter’s response, assuming IT does some form of filtering at a reasonable level of approximation, without a clue about what its input looks like. It makes trying to derive a transfer function, perhaps one you could use in an object recognition system, impossible.

Before incorporating precise biological data into the functional picture of IT, we need a good, basic, characterization at the electrophysiological level, something akin to Hubel and Wiesel’s seminal work in LGN and V1. The power of their cartoon model is that it gives a picture of how input from the retina is transformed into useful information at V1 via LGN. We have no such cartoon for V2, V4, ITp and ITa. This is not due to a lack of recordings from IT, but rather, a lack of data from multiple areas that we can tie into a unified picture. Perhaps simultaneous recordings of a V4 cell that synapses onto an ITp cell attacks this problem most directly, similar to Alonso and Reid’s critical work in LGN and V1. I recall someone in DiCarlo’s lab working on something like this, or at least he mentioned it at his last talk. We’ll see what fruit it bears.

Anyway, after establishing such a general framework, then maybe it makes sense to unpack laminar dynamics, followed by fine-grained in vitro work, etc. etc. So, in short, the answer is: not much, but maybe other coarser-grained data will give us a rough picture in the near future.

Max