What is computational neuroscience? (XXII) The whole is greater than the sum of its parts

In this post, I want to come back on methodological reductionism, the idea that the right way, or the only way, to understand the whole is to understand the elements that compose it. A classical rebuttal of methodological reductionism is that the “whole is greater than the sum of its parts” (Aristotle). I feel that this argument is often misunderstood, so I have thought of a simple example from biology.

Cells are enclosed by membranes, which are made of lipids. A membrane is a closed surface that defines an interior and an exterior. No part of a membrane is a membrane, because it is not a closed surface. You could study every single lipid molecule that forms a membrane in detail, and you would still have no understanding of what a membrane is, despite the fact that these molecules are all there is in the membrane (ontological reductionism), and that you have a deep understanding of every single one of them. This is because a membrane is defined as a particular relationship between the molecules, and therefore is not contained in or explained by any of them individually.

There is another important epistemological point in this example. You might want to take a “bottom-up” approach to understanding what a membrane is. You would start by looking at a single lipid molecule. Then you could take a larger patch of membrane and study it, building on the knowledge you have learned from the single molecule. Then you could look at larger patches of membrane to understand how they differ from smaller patches; and so on. However, at no stage in this incremental process do you approach a better understanding of what a membrane is, because the membrane only exists in the whole, not in a part of it, even a big part. “Almost a membrane” is not a membrane. In terms of models, a simple model of a cell membrane consisting of only a small number of lipid molecules arranged as a closed surface captures what a membrane is much better than a large-scale model consisting of almost all molecules of the original cell membrane.

This criticism applies in particular to purely data-driven strategies to understand the brain. You could think that the best model of the brain is the one that includes as much detailed empirical information about it as possible. The fallacy here is that no part of the brain is a brain. An isolated cortex in a box, for example, does not think or behave. A slice of brain is also not a brain. Something “close to the brain” is still not a brain. A mouse is a better model of a human than half a human, which is bigger and physically more similar but dead. This is the same problem as for understanding a membrane (a much simpler system!): the methodologically reductionist strategy misses that it is not the elements themselves that make the whole, it is the relationship between the elements. So the key to understand such systems is not to increase the level of detail or similarity, but to capture relevant higher-order principles.

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