Reverse engineering the brain?

In a recent invited talk at the Department of Cognitive and Neural Systems, Lloyd Watts, neuroscientist turned entrepreneur (founder, chairman and CTO of Audience Inc., a Silicon Valley company that commercializes technology derived from auditory neuroscience research), presented his “strategy” on how to go about a gargantuan task: reverse-engineering the brain. With a military strategy analogy, the problem is the following: what is the best way to occupy an enemy territory? Should the invading army occupy simultaneously the target territory from all its borders, or should all troops focus on a narrow strip of land, occupy it, consolidate the territory and exploit its resources, and then move on to the next target? Lloyd Watts, the neuroscientist-entrepreneur, seems to suggest that the second strategy is the winning one.

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Feinerman et al. Figure 1b: logic components fabricated from hippocampal neurons

Neuromorphic technology is a young field, with little in the way of established paradigms or techniques. Most of the recent related work, however, focuses on silicon implementation of neural-inspired mechanisms. Feinerman et al. buck the trend and build reliable computation devices using actual neurons.

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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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The challenge of building, within a few decades, a computer chip on the scale of a patch of biological cortex is a race involving many labs in academics and industry around the world.

The basic assumption is that, in order to build machines that imitate the cortex, the intuitive way to go is capture in a chip the architecture and functional principles of cerebral cortex. Building a chip that emulates the cortex needs to solve several challenging problems. For example, how can you pack millions of processing elements and billions of synapses into a small enough chip and be able to perform computations at a speed compatible with human thought. All this must be done without consuming a lot of power. Easy, right? Read the rest of this entry »

I recently saw a talk by a luminary, i.e. one of the few people you may have heard of, in computational neuroscience. It touched on issues of attention and object recognition, covering a good deal of theoretical and experimental ground. At least, the majority of the talk focused on how to mathematically model brain data, unifying neurobiological facts and behavioral data. This is the MO of computational neuroscience, all well and good. I started to worry when this particular luminary bragged about how much code was written for a particular simulation. Again, listing it is one thing, but this luminary bragged about it. You can write 10 trillion lines of Assembly and I will be suitably impressed at the ability of a graduate student to sit and write code for a 30 year PhD project. Just don’t expect everyone’s knees to buckle and accept that your simulation, by virtue of the size of its codebase, does anything meaningful.

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Humans are remarkably good at identifying the same face across illuminations, positions, deformations, and depths. The same face can even be identified through fences, glass, and water. The possible number of contexts for a face to appear in are infinite, yet we can identify it instantaneously. For whatever reason, we are really good at identifying objects, but researchers have struggled to make computers even semi-competent at it. One of the more valiant efforts is Yann LeCun’s use of convolutional nets, but its primary successes are in controlled situations. Any reasonable person in the field would agree that any human can wipe the floor with even the best algorithm running on the best supercomputer (programmed by the best programmer in the best department in the best state in the best country!). So what gives?

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In december 2008, a video post has been published on Abovetopsecret.com  with the title “DARPA & IBM building a “global brain” “cognitive computer” for “monitoring people”. In this video, the leader of the IBM SyNAPSE project, Dharmendra Modha, talks about SyNAPSE.

This is an excerpt from the video:
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SyNAPSE is not a project DARPA undertook lightly. Many attempts at large-scale neuromorphic engineering have been made in the past. None met their goals. As such, SyNAPSE owes its existence to a number of recent game-changing developments. From HP Labs, the discovery of the memristor was one such keystone innovation. It took Greg Snider’s 2007 work in Nanotechnology, however, to establish memristors as a viable platform for the implementation of self-organizing recurrent neural networks.

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After running through the Businessweek article posted by Max, I am equally excited and nervous. Anyone has to be excited over the prospect of a new computing paradigm, though honestly I’m not sure what that looks like yet. These sorts of articles claim that computers will look more like brains, which is all well and good, because brains tend to do dominate the “competition”, i.e. computers, at messy things like object recognition and speech recognition. Conversely (and obviously), computers tend to dominate tasks amenable to decomposition into easily formalizable sequential steps, e.g. chess or even eye surgery. So, maybe we know what Deep Blue looks like, but what on Earth would a computer expert in messy things, a messy computer if you’ll excuse the phrase, even look like? We all agree that computers stink at these messy things, and if they didn’t stink at them it would be a huge boon to, well, humankind. So let’s make the computers more like brains so they can do what brains do so well! But how do we make computers, both in terms of hardware and software, more like brains?

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