A recent study conducted by physicists and neuroscientists from the University of Chicago, Harvard, and Yale has shed light on how connectivity among neurons is established through general principles of networking and self-organization, rather than being solely determined by the biological features of an individual organism .
The researchers analysed connectomes, which are maps of brain cell connections, from various model organisms such as fruit flies, roundworms, marine worms, and the mouse retina. They developed a model based on Hebbian dynamics, a concept coined by Canadian psychologist Donald Hebb in 1949, which states that "neurons that fire together, wire together." In other words, the more two neurons activate together, the stronger their connection becomes.
The study found that this model of Hebbian dynamics accurately explains the "heavy-tailed" distribution of connections observed in brain networks. This distribution refers to the dominance of a small number of strong connections among neurons, which is crucial for cognitive processes like thinking, learning, communication, and movement.
One interesting finding of the study is that the principles of networking and self-organization apply not only to biological networks like the brain but also to non-biological networks such as social interactions. The researchers discovered that the model could explain the phenomenon of clustering, which describes the tendency of cells to link with other cells via connections they share. This clustering effect is observed not only in brain networks but also in social situations, where individuals are more likely to become friends with people introduced to them by mutual acquaintances.
It is important to note that while the model provides valuable insights into the organization and connectivity of brain cells, there is still an element of randomness and noise involved in brain circuits. Neurons can disconnect and rewire with each other, weak connections can be pruned, and stronger connections can be formed elsewhere. This randomness acts as a check on the Hebbian organization described by the model, preventing strong connections from dominating the network. The researchers adjusted their model to account for this randomness, which improved its accuracy.
