17 January 2017

Still a Few Bugs in the System

Some neuroscientists decided to see if the latest neuroscience tools could handle a simpler case than the human brain.

They chose a 40+ year old CPU, and they failed abysmally:
In 2014, the US announced a new effort to understand the brain. Soon, we would map every single connection within the brain, track the activity of individual neurons, and start to piece together some of the fundamental units of biological cognition. The program was named BRAIN (for Brain Research through Advancing Innovative Neurotechnologies), and it posited that we were on the verge of these breakthroughs because both imaging and analysis hardware were finally powerful enough to produce the necessary data, and we had the software and processing power to make sense of it.

But this week, PLoS Computational Biology published a cautionary note that suggests we may be getting ahead of ourselves. Part experiment, part polemic, a computer scientist got together with a biologist to apply the latest neurobiology approaches to a system we understand far more completely than the brain: a processor booting up the games Donkey Kong and Space Invaders. The results were about as awkward as you might expect, and they helped the researchers make their larger point: we may not understand the brain well enough to understand the brain.

On the surface, this may sound a bit ludicrous. But it gets at something fundamental to the nature of science. Science works on the basis of having models that can be used to make predictions. You can test those models and use the results to refine them. And you have to understand a system on at least some level to build those models in the first place.


That's where Donkey Kong comes in.

Games on early Atari systems were powered by the 6502 processor, also found in the Apple I and Commodore 64. The two authors of the new paper (Eric Jonas and Konrad Paul Kording) decided to take this relatively simple processor and apply current neuroscience techniques to it, tracking its activity while loading these games. The 6502 is a good example because we can understand everything about the processor and use that to see how well the results match up. And, as they put it, "most scientists have at least behavioral-level experience with these classical video game systems."

So they built upon the work of the Visual 6502 project, which got ahold of a batch of 6502s, decapped them, and imaged the circuitry within. This allowed the project to build an exact software simulator with which they could use to test neuroscience techniques. But it also enabled the researchers to perform a test of the field of "connectomics," which tries to understand the brain by mapping all the connections of the cells within it.

To an extent, the fact that their simulator worked is a validation of the approach. But, at the same time, the chip is incredibly simple: there is only one type of transistor, as opposed to the countless number of specialized cells in the brain. And the algorithms used to analyze the connections only got the team so far; lots of human intervention was required as well. "Even with the whole-brain connectome," Jonas and Kording conclude, "extracting hierarchical organization and understanding the nature of the underlying computation is incredibly difficult."
Remember, in a microprocessor, a transistor is a transistor is a transistor, in the brain, neurons and ganglia vary from cell to cell.

This is a valid test of the software, the 6502 is arguably the most thoroughly understood CPU in existence, and Donkey Kong is arguably one of the best understood pieces of software in existence.

And they still could not do it on a  processor that can access only 64K of RAM.

We are much further from mapping the brain in any detail than is implied in the mainstream media reports.


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