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Reverse-Engineering Brains, One Neuron At A Time


Most posts here are electrical or mechanical, with a few scattered hacks from other fields. Those who also keep up with advances in biomedical research may have noticed certain areas are starting to parallel the electronics we know. [Dr. Rajib Shubert] is in one such field, and picked up on the commonality as well. He thought it’d be interesting to bridge the two worlds by explaining his research using analogies familiar to the Hackaday audience. (Video also embedded below.)

He laid the foundation with a little background, establishing that we’ve been able to see individual static neurons for a while via microscope slides and such, and we’ve been able to see activity of the whole living brain via functional MRI. These methods gradually improved our understanding of neurons, and advances within the past few years have reached an intersection of those two points: [Dr. Shubert] and colleagues now have tools to peer inside a functional brain, teasing out how it works one neuron at a time.

[Dr. Shubert]’s talk makes analogies to electronics hardware, but we can also make a software analogy treating the brain as a highly optimized (and/or obfuscated) piece of code. Virus stamping a single cell under this analogy is like isolating a single function, seeing who calls it, and who it calls. This pairs well with optogenetics techniques, which can be seen as like modifying a function to see how it affects results in real time. It certainly puts a different meaning on the phrase “working with live code”!

Afterwards there was both a lively Q&A session and multiple small group discussions. Unlike high energy physicists and their multi-billion dollar experimental facilities, [Dr. Shubert] believes this technology will be accessible to modest research labs in the near future. This potential both excited and worried the audience, as individuals cited various pieces of science fiction that loomed close to become our reality. There is agreement that public policy lags behind technology development, but no consensus on how that would be addressed.

And a final note of trivia: with better understanding of neurons, [Dr. Shubert] says we now know real neural networks to be quite different from the software creations they inspired, spurring research into a new generation of software. This and more fascinating insights absent from the video stream are great reasons to come to Hackday Los Angeles meetups in person!



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