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Yes, it's true, for certain definitions of "unclear" and "processes". The whole issue here is that the mind/brain is too complex to describe at any single level

Yes, it's true, for certain definitions of "unclear" and "processes". The whole issue here is that the mind/brain is too complex to describe at any single level of abstraction. There are just so many different, valid levels of description, all the way from physics to high level computational approaches. Wikipedia lists 22 distinct branches of neuroscience [1], and each has their own set of problems they are trying to answer. The problem this article brings up is that one researcher (who has received a lot of money to fund his project) has come up with an explanation of "process" that is "clear" to him for the questions he is interested in. However, for everyone else, "process" means something different, and they are unable to see how to translate his model to answers questions they are interested in. Without a simplified model, one that abstracts away the huge complexity, there's no ability to generalize its conclusions beyond the exact simulation at hand, and this means it has virtually no explanatory power. So yes, because there are many valid definitions of "process", that statement is true for many of the interesting definitions. [1] https://en.wikipedia.org/wiki/Neuroscience#Major_branches https://en.wikipedia.org/wiki/Neuroscience#Major_branches

bjornsing 11y ago
> the mind/brain is too complex to describe at any single level of abstraction Sure, but now we're talking about a single neuron... > Wikipedia lists 22 distinct branches of neuroscience [1], and each has their own set of problems they are trying to answer. Sure, but only one or two of those 22 are about single neurons... In artificial neural networks neurons are approximated basically as a weighted summation and a thresholding/sigmoid/rectified linear operation. It would be interesting to know how far from the truth this is. Is the biological neuron doing something entirely different than what our artificial neurons are? That's what I feel would be a pressing research question.
- shmageggy 11y ago
  > Sure, but only one or two of those 22 are about single neurons... I count four or five, but I agree that may not be the best example for my point, which is that (even within those few subfields) there are many valid levels of description. > Is the biological neuron doing something entirely different than what our artificial neurons are? Yes, very; they are highly non-linear and have complex timing dependencies. This illustrates the point. Once you model all those intricate biological details it's unclear how to translate that into a mathematical function that we can understand for the purposes of higher level computation. Not to mention, we're still talking about spiking models, so we'd also have to abstract away spiking in order to look like an artificial neuron. The gulf between the level of description that you and I want and what biologically realistic models provide is huge.

bjornsing 11y ago

> the mind/brain is too complex to describe at any single level of abstraction Sure, but now we're talking about a single neuron... > Wikipedia lists 22 distinct branches of neuroscience [1], and each has their own set of problems they are trying to answer. Sure, but only one or two of those 22 are about single neurons... In artificial neural networks neurons are approximated basically as a weighted summation and a thresholding/sigmoid/rectified linear operation. It would be interesting to know how far from the truth this is. Is the biological neuron doing something entirely different than what our artificial neurons are? That's what I feel would be a pressing research question.

shmageggy 11y ago
> Sure, but only one or two of those 22 are about single neurons... I count four or five, but I agree that may not be the best example for my point, which is that (even within those few subfields) there are many valid levels of description. > Is the biological neuron doing something entirely different than what our artificial neurons are? Yes, very; they are highly non-linear and have complex timing dependencies. This illustrates the point. Once you model all those intricate biological details it's unclear how to translate that into a mathematical function that we can understand for the purposes of higher level computation. Not to mention, we're still talking about spiking models, so we'd also have to abstract away spiking in order to look like an artificial neuron. The gulf between the level of description that you and I want and what biologically realistic models provide is huge.