Brains and Computers, a Poor Comparison
Why comparing your brain to a computer may be more inaccurate than you think.
Posted April 18, 2018
It’s a very common metaphor to compare brains to computers, although this comparison falls short to illustrate how complex our brains are. Eric Chudler puts it very clearly here, in his Neuroscience for Kids section—because, why make it harder than it should be?
For instance, both brains and computers may be damaged, but there is a substantial difference between fixing one or the other. Fixing a computer is just a matter of replacing whatever is broken. Unfortunately, we cannot replace broken parts in the brain.
However, scientists from UC Berkeley and Northwestern University in Chicago took the metaphor of brains as computers to the next level in their article Could a neuroscientist understand a microprocessor? Their intention was to confront the possibility that current neuroscience techniques might not be the best to decipher the workings of the brain. To do this they analyzed a microprocessor as if it were a brain. They collected data using standard neuroscience tools to see whether they could infer the way the machine processes information, just like neuroscientists analyze large datasets to untangle brain mechanisms.
They used three video games, well known to all the 80’s kids reading: Donkey Kong, Space Invaders and Pitfall. For the biological equivalent, the microprocessor would be the mouse, and each one of the three video games would be a different behavioral pattern. Although they acknowledge the limitations of comparing a microprocessor to a living organism’s brain, the authors argue that there are enough similarities to justify the study: both a brain and a microprocessor consist of interconnections of smaller units that can be differentiated and studied individually. They compare the structure of the microprocessor to that of a brain, where we find circuits, subdivided into microcircuits, comprised of neurons that make connections through their synapses. Of course, the microprocessor is simpler than a brain in many ways (e.g. brain requires complex routes to produce the energy that every cell needs to function, and it is made of intricate circuits that we still don’t fully understand).
Using neuroscience protocols to study a microprocessor
They used established protocols to analyze diverse features of the microprocessor MOS6502, a model that is very well understood. Using the approach presented in one of their previous articles, they were able to identify types of transistors within the microprocessor and the connections between them, similar to what we would do to the brain. In the microprocessor, they only found one type of transistor. However, it was impossible to infer the operation of the microprocessor by just looking at the connections. In neuroscience, this is even more complicated, as the brain is made of different types of cells, and other components such as synapses, channels and neurotransmitters have to be integrated into the whole picture. The authors stated the importance of the study of connections, but emphasized the lack of algorithms to determine the functions of the brain regions assessed, hence the difficulty of understanding the brain through the sole analysis of connections.
They also studied the effect of game performance when they removed one or more transistors from the microprocessor. This is similar to what we do in the lab, when a gene is knocked out to study the effects. They identified the contribution of each transistor to each video game considered, but they could not generalize to the rest of the games without further analysis. According to the authors, these results relate to neuroscience in that it is unlikely that a certain behavior would be triggered without the interaction of different brain circuits/regions.
Throughout the article, they looked into other aspects of the transistors. With every set of experiments they concluded that, although interesting and necessary results were drawn, no individual dataset provided a full understanding of how the MOS6502 processes information.
Better approaches for better conclusions
We cannot forget that brain possesses plasticity and is capable of repairing circuits or compensating for lesions and other impairments that MOS6502 does not. This renders the data much cleaner and clearer than that from neuroscience experiments in vivo.
So, can neuroscientists really understand a microprocessor? According to the study, we just need different methods to do so, and that testing these methods in a microprocessor could provide certain validation. But perhaps this study should not be considered as confirmation or rebuttal of the value of neuroscience to understand microprocessors, or even as measurement of the worth of current neuroscientific methods. This study offers additional evidence that brains are not computers.
We definitely need a better metaphor.
Originally posted in PLOS Neuro Community.
Jonas E, Kording KP (2017) 'Could a Neuroscientist Understand a Microprocessor?' PLOS Computational Biology 13(1): e1005268. doi: 10.1371/journal.pcbi.1005268
Jonas E, Kording K, ‘Automatic Discovery of Cell Types and Microcircuitry from Neural Connectomics’, eLife, 4 (2015), e04250