[rkp8000]

Connectome-adjacent neuroscientist here. Definitely not a dead end! But also definitely not the whole picture.

One of the main open questions in neuroscience right now is how network structure, dynamics, and function are related in the brain. Connectomes provide tremendous insight into structure, but as mentioned this does not generically solve either the dynamics or function problem. For example, for many of these neurons we don't have a good understanding of their input-output relationship, and the nature of this relationship can strongly affect the dynamics that emerge in a highly connected network. Individual variability across connectomes, and how connectomes change over development are also a significant issue, but at least for the fly it's thought that many of the basic structures are pretty conserved across adult animals, even if many of the details could differ.

Modulo these caveats, knowing the physical network structure of the brain does still impose huge constraints on what kinds of models we should be using for gaining insight into dynamics and function. For example, there are well known areas (the "mushroom bodies") with specific feed-forward connectivity patterns that are very different from a random recurrent network. Further, there are at least some areas in the fly brain where we think there are indeed quite clean structure-function relationships, e.g. in the central complex of the fly brain, which contains a physical ring of neurons and is thought to support a "bump" of activity that acts as a sort of compass that helps flies navigate via a ring-attractor-like dynamical system. Thus, even though it has many missing pieces, a wiring diagram like this can be tremendously useful for generating hypotheses to guide more targeted experiments and theoretical studies.

[Animats]

How's Open Worm coming along? The connectome of C. Elegans has been known for years, and Open Worm tries to simulate it. [1] Not with enormous success.

[1] https://openworm.org/assets/OpenWormPoster_Celegans_Glasgow_...

[xandrius]

Like everything in science: we don't know until we know.

No need to treat research like a business.

[SubiculumCode]

Very Nice. --from a Connectome-Centric neuroimager :) One technique that I am pursuing right now is information decomposition of timeseries to separate the mutual information of two timeseries into redundant and synergistic informational atoms (synnergystic here means the degree to which knowing both timeseries gives you more information than the individual parts give (more than sum of parts). The big limitation of the method is the geometric explosion in complexity of the decomposition as the number of time series grow, with most analyses being limited to two or three times series at a time. However, the scale of the data on which it is applied is not requisite, meaning the approach can equally be used on the mutual information between two regions of interest in rsfMRI , or two spiking timeseries from individual neurons. https://en.wikipedia.org/wiki/Partial_information_decomposit...

[taneq]

Thanks for your insight! How repeatable are these structures between individual animals? Are they very similar or is it more like “here’s a feed forward kinda bit, here’s a toroidal bit, and over here it’s just a mess”?

[j6m8]

preprint coming out soon about this specifically :)

in the meantime, here's a simple tool paper we wrote explaining how you can treat this like a cool graph database challenge [1] and a preprint showing how you could approach that question when your number of samples per animal is close to N=1 [2]. basically..... it's hard! but also.... it's cool!

[1]: https://www.nature.com/articles/s41598-021-91025-5 [2]: https://www.biorxiv.org/content/10.1101/2023.10.16.562590v1....