Not all mugs are born equal. You will realize it if you think about it for a second: there is a big difference between each and every mug. Some are good for the morning coffee, some for the afternoon tea. Others when you read a book. Some are perfect for writing a paper. The dilemma, until now, was: where should I drink from to gain inspiration when I write a Neurdon post? Problem solved (see mug on the left for solution).

Neurdon is giving away a mug per month to the best post. Inspire us, and you will be inspired!

The Center of Excellence for Learning In Education, Science & Technology (CELEST), a NSF-funded center comprised of Boston University, Brandeis University, Harvard University & Massachusetts Institute of Technology, is hosting the third annual inter-Science of Learning Centers (iSLC) Student and Postdoctoral Conference.

iSLC is a meeting of junior researchers from the NSF-funded Science of Learning Centers (SLCs). iSLC 2010 immediately follows CELEST’s annual International Conference on Cognitive and Neural Systems (ICCNS). Read the rest of this entry »

All this Neurdon hullabaloo over memristors and Kurzweilian futurism has got me thinking about the inevitable media question concerning all this: Will our RoboSlave Bots learn to love us in a somewhat creepy, Haley Joel Osment “Artificial Intelligence: AI” kind of way? In other words, will humans be able to one day produce conscious, silicon-based offspring? There are obviously a cornucopia of contingencies when discussing artificial sentience, however, I am going to not-so-subtly sidestep all the philosophical snafus and approach the problem from a modeler’s POV. Read the rest of this entry »

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’s recent post brings up the issue of Ray Kurzweil, a polarizing figure if there ever was one. First, I try to take his musings with a truckload of salt, but his proclamations about the progress of neuroscience seem to go so far beyond the pale that its shade has surpassed the visible light spectrum. He seems to completely trivialize the daunting task set before neuroscience: create a biologically and mathematically precise functional characterization of the human brain. In other words, how does it do what it does with what it has? It seems like Ray is fixated, naturally, on the exponential growth of information technology and its implications for the field. I’ll summarize his thesis, based on the 7 minute video linked by Max, thusly: computers will become insanely powerful, along with ways to measure the brain, and so we’ll have a brain simulation by 2020, or 2029, or sometime in the next fifty years.

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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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Some time ago, a professor at a British university once told me that the introduction of yearly 50 pound “top-up” fees would corrupt education. He reasoned that if students could not completely concentrate on their work without undue influence, e.g. worrying about making money to pay for their education, how could they possibly engaged in the unbiased learning experience of the university? To American ears this sounds ridiculous. Some students accumulate hundreds of thousands of dollars in debt, and this professor is worried about his students paying 50 pounds a year!

I found the statement completely histrionic back then, but I’m starting to sympathize with him more and more these days. This has become especially acute since I left the holy order of philosophy for the decidedly greener pastures of computational neuroscience. Green seems pretty good to a former philosopher, but the closer I get to it the more I worry at times (as I am certainly wont to do!).  As with those 50 pounds and the undergrads, could the mighty green seriously warp the priorities of researchers?

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Riesenhuber and Poggio supplied a seminal model of object recognition in 1999. It derived a lot of its power from sheer simplicity. With just a few mathematical operations it seemed to model the entirety of the ventral stream, the area of the brain dedicated to processing “What” information, i.e. information about the identity of an object. It starts with a layer of Gaussian-tuned `simple’ or S cells, which respond to particular line orientations. That is, a particular S cell might respond to a diagonal line in a particular spot in an image. Then, all S cells of the same orientation feed to a ‘complex’ or C cell, which represents the maximally activated S cell. In CS terms, they take an argmax over a local neighborhood.

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