Our Brain Uses a Not-So-Instant Replay to Make Decisions | Hacker News Reader

Our Brain Uses a Not-So-Instant Replay to Make Decisions

scientificamerican.com

108 pointsprostoalex12 comments

Often when I read papers on neuroscience, I find it difficult to dismiss an analogy. It feels much like we're trying to analyze how different models of computers work by giving them all some input and then trying to discern meaning from the circuitry that then activates in response. The problem with this is imagine I give you even the precise specs of a fairly basic computing system. You're going to be able to create a lot of correlations, yet you'd probably make effectively 0 meaningful progress towards 'cracking' the system, or really gaining any meaningful degree of insight beyond repeating correlations. E.g. it may be that if you press the 'f' key, a certain area of your circuit board sees a heat spike but that doesn't really tell you much of anything. And, at worst, can give you false leads as you start to draw correlations such as 'ahh!!! it heats up when I press f, g, and h, but not i, j, k!!' When the actual reason, as is easy to imagine, might be entirely spurious.

And in this case the analogy is many orders of magnitude worse. The brain is, by far, the most complex computing system we know of. And instead of precise specs, you have nothing but previous correlations to try to even have a clue as to what you're studying. And even of the specs we can measure, it's not looking hot. The Openworm [1] group for instance has been trying to model a worm brain. The roundworm brain is about as simple as you can get: 302 neurons, 7,000 synapses. The human brain's at 86 billion neurons, 100 trillion synapses. Yet even that worm project seems to have hit some unforeseen hurdles since it appears to have stalled out since making headlines some half a decade ago.

Of course neuroscience is far from my specialty, and it's entirely possible I'm missing some critical nuance. I'd love to know why this analogy is inappropriate if anybody could share.

[1] - http://openworm.org/

Apparently the only neuroscientist around is on a feature phone. So forgive brevity. Correct re complexity of brain, but undrestimtes neuro methods. Behavioral design nuanced, convergant evidence from multi levels of description: genetic, cultures, tracers, lesion, animal humbn postmortem anatomy , human behavior imaging. Still tough but real progress.

Still analogous. Piles of correlations.

No. Experimental manipulation over multi levels description allows causal inference

My comment was in regards to the discipline of neuroscience as a whole, not parts of it.

> It feels much like we're trying to analyze how different models of computers work...

There is a paper on exactly this case - "Could a Neuroscientist Understand a Microprocessor?". Great read. https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...

Wow that was an eye opener to the state of the field, thanks for posting!

While the worm in question, C.Elegens, is vastly simpler than the human brain, the numbers don't paint the complete picture. When you look at the product of evolution in such a system, every single neuron has a very precise role. Moreover, the balance/interaction between those 302 neurons are also very difficult to disentangle. There are also some pretty big biological differences, for instance, C.elegens neurons don't typically transmit information through spikes! Instead they show gradual polarization and depolarization. Now you look at the human brain, and the immense complexity means that there's no way that every neuron can have a precise genetically encoded role - there simply wouldn't be enough information. Instead, we assume that there have to be more generalizable patterns of how neurons are organized and communicate. For example, we know that the way the visual cortex organizes information between the two eyes is dependent on correlated input from the eyes themselves ( https://en.wikipedia.org/wiki/Ocular_dominance_column) and that without sensory information provided by the eyes this organization will never develop.

All this to say that there are important differences between being able to fully model a small, tightly optimized bundle of specialized neurons (and non-neuronal cells, we've only recently begun understanding how important glial cells are to brain function), and searching for general abstractions of information processing in the human brain.

So not only the physical repetition of an activity makes it perfect, and not only replaying it mentally with eyes closed, but also dreaming of it...

It's the common sense being proven right, thus reinforcing how to improve it.

I wish i wasnt on a nokia feature phone or id go into depth on the misunderstandings u state about memory, the hippocampus, and the utility of repetition as a mnemonic. Perhaps another memory researcher can in my place, as it has taken me a while to type this already on this old style phn keyboard

Example. Repetition least advanced mnemonic. Better: integration concepts and rich imagery. Neuro replay not likely analogy 4 repetition mneumonic. hippocampus fast learn, cortex slow learning, hip replay trains cortex to remember and cortex integrates with other learned

Your comments are almost impossible to understand. It might be nice if you found a computer to type them on.

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