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High-speed microscope captures fleeting brain signals
- ykevinator 7y agohttps://youtube.com/watch?v=lhkK6jURljs https://youtube.com/watch?v=lhkK6jURljs
- jcims 7y agoNeeds some explanatory voiceover. Reminds me of slow motion lightning strikes strikes after the leader connects.
- stilley2 7y agoFull article https://www.biorxiv.org/content/10.1101/543058v2 https://www.biorxiv.org/content/10.1101/543058v2
- ngold 7y ago>Abstract >Understanding information processing in the brain requires us to monitor neural activity in vivo at high spatiotemporal resolution. Using an ultrafast two-photon fluorescence microscope (2PFM) empowered by all-optical laser scanning, we imaged neural activity in vivo at up to 3,000 frames per second and submicron spatial resolution. This ultrafast imaging method enabled monitoring of both supra- and sub-threshold electrical activity down to 345 μm below the brain surface in head fixed awake mice.>
- p1esk 7y ago3,000 times per second. That's fast enough to trace electrical signals flowing through brain circuits. It's incredible how much slower brain signals are than what we are used to dealing with in modern electronics. To debug a CPU in my laptop I'd need a scope with multi-GHz sampling rates.
- trhway 7y agoPower consumption grows with the square of frequency. Brain working frequency is just like 200Hz. 100T of synapses (or sizeable share of it) at that frequency puts us into hundreds tera-ops/second of calculation power - all at just 20watt of electrical power. To get evolutionary smarter though we possibly would have to devote bigger share of our body 100watt energy budget or find a way to produce more energy.
- leshokunin 7y agoThat's really insightful, thanks. Somehow made me think of AMD's "let's add more cores" strategy, but it makes sense!
- p1esk 7y agoCan't compare those teraops directly: my laptop's CPU is a lot more precise than my brain.
- softwarejosh 7y agowhich is good and bad for your brain, brain is more like a decentralized conputing source
- p1esk 7y agoWhen you have to be extremely noise/fault tolerant (e.g. you can literally remove half a brain and still function normally [1]), you over-design and over-engineer the system with massive redundancies everywhere. We don't know how much information is actually encoded in these trillions of low precision synapses. Our best deep learning models tend to be very over-parametrized, and are highly compressible. A CPU is nowhere as fault tolerant but it does not have to be, because modern solid state electronics is extremely reliable (and has much wider ranges of operating conditions than human brains). [1] https://en.wikipedia.org/wiki/Hemispherectomy https://en.wikipedia.org/wiki/Hemispherectomy
- 7y ago
- wjn0 7y agoSo cool. In my opinion, so many of the open problems in biology can be approached from the direction of specificity - from specificity in measurement to specificity in targeting interventions. For example, tools like this for elucidating cognition; alternatively, cancer (identifying and targeting problematic cells).
- justlexi93 7y agoI think it is awesome! I can see real utility in finding the originating site in seizure disorders.