The result is a computationally cheap algorithm for connectivity at micro- meso- and macro-scale and a new view on the structure of connectivity. First diving into biological detail we emerged with a simplified model. Validated against the amazing MICrONS data.

We explain how morphologies work in terms of their constraints on connectivity, why these constraints are stronger than anticipated, and why they provide structure that other models don’t. Understanding this allowed us to capture all of this even without using morphologies.

How are we to understand the structure of synaptic connectivity at cellular resolution? Some say that predictions from neuronal morphologies can work well. Others point out that this provides limited insights into underlying mechanisms and is computationally too expensive.

Potentially even for inter-regional connectivity. Hope it will help with our understanding of neuronal connectivity. Readily usable for example in models and simulations of neuronal networks.

Much has been written about the structure and heterogeneity of local neuronal circuitry that is not captured by controls. We built a surprisingly simple graph based model and show that it recreates the complexity characterized in biological neuronal networks at different scales.

I have embraced the complexity of the brain and data for a long time, and believe that approach is required. But that does not have to be overly centralized. Can be decentralized and incremental. Just focus on improving existing resources instead of reinventing the wheel.

Maybe I read too much into the two sentences, but it seems to imply something like: You plug Bernoulli's principle into a computer, then optimize for agent mobility and end up with a weird looking quadcopter. Conclusion: Clearly birds are flying using four rotors.

www.biorxiv.org/content/10.1...

www.frontiersin.org/journals/neu...

Hello Monica @pkpd-babe.bsky.social . Could you please add me to the science feed? I am a neuroscientist working on connectomics with simulation-based methods. Scholar profile: scholar.google.com/citations?us...

🤔 Yeah that makes a lot of sense to me.
I like this way of thinking in the context of constraints, in this case: the imperative to conserve energy. Needed to understand the brain, in my opinion.

I would ask to what degree the current parcellation into brain regions works for describing the rules of the mapped connections. Can we improve it further?

But properly manipulating connectivity in detailed models is harder than it sounds. Changing one aspect while keeping others fixed is tricky due to complex dependencies. Our tool helps with this. We show examples and results of how this plays out in practice.

Ultimately, this can only be tested through manipulations that are currently only possible in simulation. Due to the wealth of information EM gives us about connections (dendritic locations, sizes of synapses, types of participating neurons) we would do this in very detailed models.

The motivation for the work came from ongoing and published electron microscopic connectome reconstructions that were said to solve the connectomics of brain microcircuitry. But even knowing all connections, one can describe any number of rules and trends - which ones are functionally meaningful?

Our SSCx model (linktr.ee/BlueBrainPjt) provided a valuable null model to compare findings against: It has the excitatory “fan-out tracks”, but not the specific inhibition. Clearly: Inhibition is computationally powerful and should be studied more! (3/3)