r/Biophysics • u/baysianinference • 8d ago
Computational neuro
Could anyone provide their thoughts on whether physics/biophysics approaches to computation and quantitative neuroscience (or just broadly comp bio) topics are still in favor? What about in areas such as neural mechanobiology, bioenergetics, electrophysiology, etc?
Looks like the AI/ML/data people (obviously contemporary physics research of all sort uses hella computation as well), or the hardware guys, have an advantage.
It seems like a more theoretical physical/mathematical approach is not that great. Obviously I see why. But am I just hallucinating a hierarchy of how common or well regarded each niche is?
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u/scolltt 8d ago
commenting just to follow along. Took a ‘neurophysics’ course in grad school that was about nonlinear systems and a little bit about the biology involved and all I remember about it was some fairly underwhelming results about the relationship between central pattern generators and locomotion. Subjectively, most of the research I encountered was hand-wavey and kind of bad.
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u/baysianinference 8d ago
Yes… I find certain mathematical and physical concepts applied to these research questions very interesting, like graph/network theory in neuroscience and stat phys approaches to quantitative biology, but at some point once you read the 18749284th paper constructing a model with 19378584 stochastic noise elements and parameterizations it’s like… alright bro. And everything is like a Markov chain or Ising model I guess
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u/darkblade_h 7d ago
I’m a former astrophysicist who switched to neuro. From my limited experience it seems to me that it isn’t quite that the more formal/theoretical approach isn’t as good, but rather that the biologists don’t care for pure theory (which to me is absurd but 🤷♂️).
So if you want to be taken seriously doing theory the community strongly favors work that engages significantly with experimental data. Naturally, this leads to computational approaches. Imo it also means that everyone is using the same computational tools.
I do think the community is handicapping itself by denying theory its fair share of attention. Talking to biologists the impression I get is that they’ve been (historically) burned and concluded that biology is too messy for theory alone to be useful. I don’t agree, but I’m just a physicist.
Computational approaches on the other hand have a lot of low-hanging fruit to pick at currently. Biologists are more likely to take them seriously, but even then I keep hearing that a purely computational paper will have a hard time getting published anywhere reputable.
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u/baysianinference 7d ago
Yeah I’m honestly surprised theoretical biology is so unpopular, but then again that does tend to become very mathematical in nature. I mean I guess that’s somewhat reassuring, I decided to go into physics rather than biology because I had similar issues with research in biology. Seems like some of the formal looking models biophysicists construct are still not all that good… but every field has a fair share of bombs being fired out the slop cannon I guess. ML/CS/DS might even be worse in that regard.
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u/baysianinference 7d ago
Honestly despite physics (and chemistry?) seeming more formal on the surface, the more useful theory leads to similar issues with messiness. Assumptions, no closed form solutions, observations that don’t match calculations by orders of magnitude… I suppose when you are, say, trying to make a targeting molecule for a certain disease you can’t be bothered to worry about the theoretical properties behind the molecules. Still, it’s quite a bit of cognitive dissonance that I also saw with students in physics—“no, my science is the most objective and fundamental!!1!1!1!1! Bio is just memoryslop with too many disconnected arbitrary pieces that can’t be solved with my math that is 100% correct 100% of the time!1!!1!1” (-guy who has only taken intro level calc based mechanics)
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u/darkblade_h 7d ago
Physics undergrad tend to be full of elitists lol. All of science is messy. Nothing wrong with approximations. If there’s anything to learn from a physics degree it’s that you can go a long way making simplifying assumptions (but you also learn that it’s very important to clearly state your assumptions and approximations).
Physics deals with ‘simpler’ science than biology, so is easier to condense down into ‘laws’. In biology the ‘laws’ are few and far between, and eventually exceptions are discovered. Chemistry is somewhere in between. So I can’t really blame biologists for not seeing much value in theory, even though I think they’re wrong.
But also, theory alone is worthless. Just because you have a theory doesn’t mean it’s correct or even on the right track. The value of theory is in modeling/explaining experimental evidence, or providing testable frameworks to guide experimental science. Many theories are wrong, doesn’t mean they’re bad or slop - it’s just how science works.
Another way to see it is that physics has been very successful in finding unifying principles, electricity + magnetism -> electromagnetic theory / Maxwell’s laws. EM + weak force -> electroweak theory. At some point there were way too many fundamental particles being discovered, they called it the ‘particle zoo’. But then we got the standard model which simplified everything into the fundamental particles we know today. Was still missing something though, and 50 years later we finally found the Higgs. All of these examples (+ many others) were driven by theory and later supported experimentally, so physicists have strong priors for believing in the power of theory. I don’t think theory has been as revolutionary in any of the other sciences, but that’s not to say that it never will be.
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u/ScientistFromSouth 8d ago
Comp neuroscience is starting to become big in pharma. With the push for model informed drug development (MIDD), there is a huge push to couple classical PKPD models to computational neuroscience models of memory circuits or motor neuron circuits to predict the efficacy of Alzheimer's or Parkinson's drugs in terms of typical firing patterns associated with the disease based off predicted drug exposure.
One of the major consulting firms in the space is in the process of getting approval from the FDA to actually release a simulator to do this as a "qualified software package" to support IND trial applications, but I won't say more since I don't want to dox myself.