Novel Model Sheds Light on Neuronal Connectivity Across Organisms

A collaborative effort by physicists and neuroscientists from the University of Chicago, Harvard, and Yale describes how connectivity among neurons comes about through general principles of networking and self-organization, rather than the biological features of an individual organism. The findings, which were published in Nature Physics, accurately describes neuronal connectivity in a variety of model organisms and could apply to non-biological networks like social interactions as well.

“When you’re building simple models to explain biological data, you expect to get a good rough cut that fits some but not all scenarios,” said Stephanie Palmer, senior author of the paper. “You don’t expect it to work as well when you dig into the minutiae, but when we did that here, it ended up explaining things in a way that was really satisfying.”

Key to the study is the identification of a "heavy-tailed" distribution of connections, dominating brain cell networks. This distribution, visualized graphically, serves as the foundation for the circuitry supporting various cognitive functions. The researchers questioned whether this pattern arises from specific biological processes in different organisms or from fundamental principles of network organization.

To answer this, the team analyzed connectomes from diverse model organisms, including fruit flies, roundworms, marine worms, and the mouse retina. Their model, based on Hebbian dynamics—the idea that "neurons that fire together, wire together"—consistently produced the observed heavy-tailed connection strengths across all organisms, indicating a universal organizational principle.

The study also unexpectedly explained clustering, a networking phenomenon where cells link with others they share connections with, drawing parallels to social situations. The researchers acknowledged the inherent randomness and noise in brain circuits, accounting for the occasional disconnection and rewiring of neurons. By incorporating randomness into their model, they achieved a more accurate representation of real brain networks.

“Without that noise aspect, the model would fail,” explained first author Christopher Lynn. “It wouldn’t produce anything that worked, which was surprising to us. It turns out you actually need to balance the Hebbian snowball effect with the randomness to get everything to look like real brains.”