I’ve thought a bit about how modelers approach brain areas whose functions are still not very well constrained by robust neurophysiological data. By this, I mean that there is simply not enough data to say, in plain terms, what that particular brain area does. In terms of visual cortex, this pretty much accounts for all areas beyond V1, namely V2, V3, V4, posterior IT (ITp), anterior IT (ITa), which all form a loose hierarchy (in the order they’re listed), and whatever areas of the temporal lobe may be ‘visual’, e.g. entorhinal. These words may sound a bit harsh, or even better, like flame-bait. Yet, when a major computationalist publishes an article titled “How Close Are We to Understanding V1?” (to be read in the accusatory sense), and one takes into account that V1 is supposed to be the one area neuroscience figured out decades ago, well, that changes things.

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First, a hearty welcome to Ethan, you’re starting to make this whole enterprise a little less incestuous! Anyway, your recent post raises a number of interesting issues regarding inferotemporal cortex (IT), most prominently: how does IT learn to do what we think it does?

I’d first like to address what we think IT does, which is a step I find myself skipping quite a lot (awful scientist am I!). Based a number of classical studies which compared lesions of IT with lesions of parietal cortex, for example, it was determined that IT mediated some form of visual discrimination and perhaps limited `size constancy’, or at least was a key pathway in whatever area in fact does this (see here, here, and here, for instance). The presumption, based on newer electrophysiology in macaque TE and TEO (analogous to anterior IT, ITa, and posterior IT, ITp, respectively) is that IT performs some sort of hashing to signal the presence of an object across sizes, retinal translations, clutter conditions, whatever.

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Max asked me to post some information about how time could act as a ‘supervising’ learning signal to create invariant representations in IT (particular in reference to Jim DiCarlo’s work in this area). Since I am lazy, the below post is a modified section of the background from my thesis proposal – hopefully it’s not too boring….

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For your humble average computational neuroscientist scrapping for a PhD, there are scant moments of reflection about the big picture, the bally-hoo, the why-we-spend-time-on-the-what-we-spend-so-much-freaking-time-doing. This disease can grow especially acute for the computationalist, in so many ways removed from the thing he/she simulates. So, I’d like to take a trip back to 1972 for my benefit (and maybe yours), when Charlie Gross and his lab at Princeton accidentally stumbled on something new and exciting, about a brain area considered `off-limits’ by some in the neuroscience establishment: inferotemporal cortex (IT, the thing I am currently embroiled in modeling). (Disclaimer: in case it wasn’t obvious, given that I was born in 1984, this particular nugget was acquired second-hand).

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Most researchers presume that the meat of visual object recognition occurs in inferotemporal cortex (IT), though there is nothing near a consensus on how this is done (i.e. the, eh em, how the meat is prepared). Some claim that the firing of IT cells, in particular cells in anterior IT (ITa), represent categories of objects. That is, a cell might fire for cats and another dogs, responding in the same way to different retinal images from one category. This sort of simplistic view seems approximately correct given the volume of data amassed over the past 30 years in monkey electrophysiology, but the evidence remains frustratingly indirect. Only a few things are certain: (1) ITa cells love “complex” objects (i.e. something more complicated than an oriented bar) and (2) they appear to have large receptive fields relative to striate cortex. How these characteristics lead to the formation of category representations in IT is a mystery, and it will probably stay that way until we find better ways to look at IT cells, perhaps using dual-photon calcium imaging. Current electrophysiological methods can only record from tens of nearby cells at the most, and imaging methods don’t have the resolution to tell us what particular cells are doing at the millisecond time scale.

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