A new study by physicists and neuroscientists from the University of Chicago, Harvard and Yale shows how networking and self-organization can lead to better connections among neurons. The research, which was published in Nature Physics on January 17, 2024, accurately describes neuronal connections in a variety of model organisms and could apply to non-biological networks. “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 the author of the paper. When we did that here, it ended up explaining things in a way that was really satisfying, because you don’t expect it to work as well when you dig into the mundane. A web of connections between brain cells is formed.
While the vast number of connections may seem random, networks of brain cells tend to be dominated by a small number of strong connections. The way the distribution of connections looks when plotted on a graph is called a “heavy-tailed” distribution of connections. Scientists were unsure if the heavy-tailed pattern was due to biological processes specific to different organisms or if it was due to basic principles of network organization.
Palmer and Lynn analyzed connectomes, or maps of brain cell connections, to answer the questions of how the brain works. Fruit flies, roundworms, marine worms and the mouse retina are some of the classic lab animals. They developed a model based on Hebbian dynamics, a term used by Canadian psychologist Donald Hebb in 1949, to understand how the brain works. The stronger the connection becomes, the more the two neurons are activated. The researchers found that the Hebbian dynamics produce strong connection strengths just like they saw in the different organisms.
The results show that this kind of organization is a result of general principles of networking, not specific to the biology of fruit flies, mice, or worms. The model provided an explanation for clustering, a phenomenon in which cells link with each other via connections they share. In social situations, clustering occurs. One person introducing a friend to a third person is more likely to become friends with that person than if they met separately. “These are mechanisms that everyone agrees are going to happen in neuroscience,” he said. The data can give rise to many different effects in clustering and distributions if you treat it carefully.
Palmer pointed out that biology doesn’t always fit a neat and tidy explanation, and there is still a lot of randomness and noise involved in brain circuits. Weak connections can be formed elsewhere, and stronger connections can be formed with the help of a nervy network. This randomness checks the kind of Hebbian organization the researchers found in this data, without which strong connections would grow to dominate the network.
The model was changed to account for randomness. Lynn said that the model would fail without that noise aspect. It wouldn’t work, which was surprising to us. You have to balance the Hebbian snowball effect with the randomness to get everything to look like real brains. The team hopes they can extend the work beyond the brain since these rules arise from general networking principles.
The way the science got done is another cool aspect of this work. There is a lot of knowledge on this team, from big data analysis to biochemical and evolutionary networks. We were focused on the brain here, but now we can talk about other types of networks.
This article was inspired by: https://biologicalsciences.uchicago.edu/news/simple-model-brain-cells-connect
When do we get more?