@neurofrontiers This was interesting to read! I enjoyed seeing all these models compared.
I think there's room for analysis at all levels of abstraction. In fact, I'd argue one of the central problems of neuroscience is to reconcile the different levels. That is, how to go from modeling ion channels to psychological concepts?
Going further, an appropriate explanation of the brain may come as a series of hierarchical models. Perhaps it could be like in physics, where simple models exist but only within some bounds. Beyond the bounds, the approximations break down and a different theory is needed. Similarly, we can model some behaviors on longer timescales at the level of groups of neurons while others need to be explained at single neuron level.
@lili Thank you so much!
That’s a great point! Reconciling the different levels is still very much a challenge and I think you’re right, a hierarchy of models could be incredibly useful.
@neurofrontiers I love that you are asking the question but I think it starts from a false premise--the idea that if "we" understand something it should be "explainable in simple terms". Has that proven to be true anywhere? Ask someone to explain how the heart works in simple terms, and you'll get the elementary school explanation. Is that really explaining "how the heart works" for someone who has spent their career studying it? You know it isn't. There is so much more complexity than that.
@neurofrontiers What IS an example of something complicated that we now understand well enuf to explain in simple terms? Experts can explain parts of their domain in "simplified" terms, but you don't want to confuse simple and simplified.
@chiasm What do you think would be a better premise for this?
That's a fair point, there will always be different levels of "simplicity", so to say, or abstraction, at which things can be explained, based on the level of expertise and the purpose of the explanation. In this context, I used "explainable in simple terms" as I see it used by physicists. Basically being able to reduce the dimensionality of a complex system and to describe it using a reduced set of equations.
One approach borrowed from statistical mechanics in neuroscience is the mean-field approximation, in which the activity of the neurons in a population is represented as the mean of the activity of that population. But depending on the dimensionality of the system, this might not be a good approximation, so simplification might not work. Going beyond that, I would also see as a “simple” explanation of the brain one where, for example, we can use the same model to describe different regions of the brain or different neurons, instead of needing different models with different parameters for each of them. But to me it’s unclear whether this will ever be a good enough representation of the brain at some level or whether it’s something that will be abandoned at some point. (I am, however, not a physicist, and I am still very much fleshing out my understanding and thoughts on the topic, so I might be missing the mark here. If so, please do let me know what I’m misinterpreting/missing.)
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