Mouse, insect or worm — in all these creatures, the same principle guides the formation of super strong connections between neurons in the brain, a new study confirms. The research helps validate the idea that, regardless of species, there's a universal mechanism that underlies how brain networks form.
Different animals carry contrasting numbers of neurons in their brains, ranging from hundreds in worms to tens of billions in humans. Neurons form connections with each other, called synapses, that enable information to pass from one region of the brain to another in the form of electrical signals. Together, these connections form a network that enables animals to function and process information about the world.
This network is flexible; it is always changing and rearranging. Some of the connections between neurons are fairly weak and thus easily broken and replaced, while a small group are super strong. These strong links are known as "heavy-tailed" connections because, on a graph of connection density in the brain from low to high, they're the outliers plotted at the dense end of the scale — like the tail of an animal.
These heavy-tailed connections play a bigger role in controlling major cognitive processes, such as learning and memory, compared with the weaker connections that far outnumber them in the brain. However, it was unknown whether these strong links formed via simple, known principles of network organization or via mechanisms that were species-specific, according to the authors of the new study, published Wednesday (Jan. 17) in the journal Nature Physics.
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"It has been known for some time that the number of neurons that a neuron is connected to varies widely with some neurons in the network being highly-connected hubs," Marcus Kaiser, a professor of neuroinformatics at Nottingham University in the U.K., who was not involved in the research, told Live Science in an email.
"However, across species, the distribution of weights [strengths] of a connection also varies widely," he said. The team wanted to see if this variation might stem from differences in how each species' brain comes to be wired.
The authors analyzed maps of the wiring between neurons, called connectomes, based on the brains of mice, fruit flies and two worm species. They created these maps by analyzing tissue samples with specialized imaging techniques.
To deduce how heavy-tail connections may form, they used the data from the connectomes to develop a mathematical model based on a principle of neuronal self-organization known as Hebbian plasticity. This principle can be summed up with the phrase "neurons that fire together, wire together." In other words, when one neuron repeatedly activates another via chemical messages, the connection between the two cells gets stronger. This basic principle underlies how we learn and form memories.
However, some previous research has suggested that Hebbian dynamics alone may not completely explain animals' ability to rewire their synapses and strengthen connections between neurons.
The authors' model confirmed that Hebbian plasticity explained the formation of heavy-tail connections in all of the animals they studied, without the need for additional mechanisms specific to each species. In addition to explaining heavy-tailed connections, this principle likely guides neurons' tendency to cluster together and form tightly knit groups depending on their activity levels, the researchers said.
To make their model better resemble a real brain, the authors ensured it accounted for some randomness in its network organization, they said in a statement. They assumed that neurons would typically rearrange and connect due to their activity, as in Hebbian dynamics, or randomly, with synapses sometimes disconnecting or forming without clear reason, Christopher Lynn, first author of the new study and an assistant professor of physics at Yale University, said in another statement.
"Overall, this is a promising first step to explain the variation in synaptic weight [the strength of connections between neurons] across biological neural networks," Kaiser said.
However, a limitation of the article may be that the authors only compared a few features in their model to real neuronal networks, he said. For example, they tested clustering with their model but not other features you'd expect to see in brain networks with heavy-tail connections, he said. These include modules — densely connected regions of neurons — and short overall path lengths, meaning the distance between the cells.
The authors didn't study human brains in the work, but they think that studying this seemingly universal principle of network development could help scientists better understand the structure and function of the brain in many animals, including humans.
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