Neuroscience has advanced rapidly during the past decade. This is to the level where scientists can record the activity of just one neuron, or create a map of a network of neurons and compare different organisms. What is missing from research so far is how the whole neural network functions. This has proved tantalizingly complex, even for trying to understand the brain of a simple organism like a fruit fly.
To find out if the approach used was correct a team of neuroscientists turned to video games. The reason for the scientists adopting the unusual approach of looking at a computer game was to see if they could develop and test out approaches that could one day interpret complexity of the brain.
Here Eric Jonas of U.C. Berkeley and Konrad Kording of Northwestern University/ Rehabilitation Institute of Chicago thought a human designed computer game would be easier to approach. What they discovered, however, was that neuroscience falls short in trying to interpret how a computer game actually works.
For this they applied classical neuroscience analysis techniques to the computing system – the 6502 microprocessor from the Atari 2600. The scientists began their analytical attempt to see if they could figure out transistors based upon their activity during video games and which transistors are necessary for a given game to function.
In a research brief, Professor Jonas explains further: “Since humans designed this processor from the transistor all the way up to the software, we know how it works at every level, and we have an intuition for what it means to ‘understand’ the system.”
He adds further: “Our goal was to highlight some of the deficiencies in ‘understanding’ that arise when applying contemporary analytic techniques to big-data datasets of computing systems.”
With the research the scientists attempted to use standard neuroscience techniques to analyze the Atari 2600’s microprocessor. Instead of being enlightened they found the techniques of conventional neuroscience did not achieve the same level of understanding that a typical electrical engineering student would have achieved had computer design principles been applied.
The consequences of this may well affect the course that neuroscience takes. As Professor Jonas comments: “without careful thought, current big-data approaches to neuroscience may not live up to their promise or succeed in advancing the field.”
This, in essence, means going ‘back to the drawing board’; suggesting that neuroscience requires better experiments, theories, and data analysis approaches.
The research has been published in the journal PLOS Computational Biology, under the title “Could a Neuroscientist Understand a Microprocessor?”
