During the study of the mouse ear muscle described above, it became clear that every instantiation of the wiring diagram was different from every other one. Some will take such variability to mean that nothing can be learned from doing this kind of tedious, data-intensive, and highly expensive work.
As an analogy, imagine a hypothetical universe in which the DNA of every organism in the same species were exactly the same, and all of the differences between individuals were mediated via epigenetic modifications.
If this were the case, knowledge of an individual’s DNA sequence would have greatly diminished utility. We wouldn’t be able to correlate genetic variability with molecular, cellular, and organismal variability.
To be fair, it is similarly true that if the DNA of every organism were so variable that we could call it totally random, it would also not have any utility in explaining differences between individuals. The same is true for neural connectivity patterns.
So, for both neural connectivity and DNA base pairs, we can loosely think of the relationship between potential explanatory power and structural variability like this:
The shape of this distribution is modeled after the expected surprisal of a coin flip versus the fairness of the coin. That is, I’d expect extreme degrees of variability or non-variability to be especially uninformative.
The great assumption of connectivity research is that the variability patterns will fall in the “sweet spot” of the above distribution. But Lichtman’s point is that this assumption is not just limited to neural connectivity research–it is an overarching theme of biology.
Lichtman and Denk. 2011 The Big and the Small: Challenges of Imaging the Brain’s Circuits. Science DOI: 10.1126/science.1209168
Link to Lichtman’s NPR interview.