Theoretically at least, this blog is oriented towards explaining the scientific ecosystem surrounding the DARPA SyNAPSE project. To date, however, we haven’t clearly defined the origin and purpose of SyNAPSE. To fill the gap, I’m pleased to announce a new top-level page on Neurdon: About SyNAPSE.

This is a working document and will continue to evolve as the project proceeds. We hope it will be of use to both practitioners and newcomers to neuromorphic technology, though, so feedback is certainly welcome!

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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Is a robot that is able to learn and take decisions “responsible“ for its own actions? Or is the company that manufactured the robot liable for the damage that the robot may cause, regardless of whether or not the machine was programmed to hurt anybody? Or is the owner of the robot responsible for its “bad habits“? When a pet robotic dog will accidentally hurt a toddler, who will be responsible? The robot itself, the owner of the robot, or the manufacturer? Moreover, if a corporation is found guilty of fabricating robots that “learn” to harm people, who will go to jail, since corporations are non-human legal entities with “No Soul to Damn, No Body to Kick”? Read the rest of this entry »

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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