Everyone knows that the human brain is extremely complex. But how exactly does he learn? Well, the answer might be a lot simpler than you think.
An international research team, including the University of Montreal, has achieved a major breakthrough by accurately simulating synaptic changes in the neocortex that are considered essential for learning, paving the way for a better understanding of brain function. .
The scientists’ study – which is based on an open source model – was published Dec.er in June NatureCommunication.
A world of new directions
“This opens up a world of new directions for scientific inquiry into how we learn,” said Eilif Muller, assistant professor in the Department of Neuroscience at UdeM, researcher at IVADO – Instituto de Valorização de Dados – and CIFAR-Canada Chair in Artificial Intelligence (AI ) ), who co-led the study in the Blue Brain Project at the École polytechnique fédérale de Lausanne (EPFL), Switzerland.
Eilif Muller moved to Montreal in 2019 and continues her research at the Architectures of Biological Learning lab, which she founded at the CHU Sainte-Justine Research Center in association with UdeM and Mila, the Quebec Institute of Artificial Intelligence. .
“The neurons are shaped like a tree and the synapses are the leaves on the branches,” explained Professor Muller, co-lead author of the study. Previous approaches to modeling plasticity ignored this tree structure, but we now have the computational tools to test the idea that synaptic interactions in branches play a key role in guiding learning in vivo.”
“This has important implications for understanding the mechanisms of neurodevelopmental disorders such as autism and schizophrenia, but also for the development of powerful new AI approaches inspired by neuroscience,” he said.
Employees in five countries
Eilif Muller collaborated with a group of scientists from the Blue Brain Project at EPFL, University of Paris, Hebrew University of Jerusalem, Instituto Cajal (Spain) and Harvard Medical School to develop a model of synaptic plasticity in the neocortex based on calcium postsynaptic dynamics. under data restriction.
How it works? Simpler than you might think.
The brain is made up of billions of neurons that communicate with each other by forming trillions of synapses. These connection points between neurons are complex molecular machines that constantly change under the effect of external stimuli and internal dynamics, a process commonly referred to as synaptic plasticity.
In the neocortex, a key area associated with learning high-level cognitive functions in mammals, pyramidal cells represent 80-90% of neurons and are known to play an important role in learning. Despite its importance, the long-term dynamics of its synaptic changes has only been characterized experimentally among some of its types and has been shown to be diverse.
Therefore, understanding of the complex neural circuits formed, particularly through the stereotyped cortical layers, which dictate how the various regions of the neocortex interact, is limited. The innovation of Eilif Muller and her colleagues was to use computer modeling to gain a more comprehensive view of the dynamics of synaptic plasticity that governs learning in these neocortical circuits.
By comparing their results with available experimental data, they showed in their study that their model of synaptic plasticity can explain the varied dynamics of plasticity of the various pyramidal cells that make up the neocortical microcircuit. They achieved this using a single unified set of model parameters, indicating that the rules of neocortical plasticity can be shared by all pyramidal cell types and therefore be predictable.
Most of these plasticity experiments were performed on rodent brain slices in vitro, where the calcium dynamics that govern synaptic transmission and plasticity are dramatically altered compared to learning in the intact brain in vivo. Importantly, the study predicts plasticity dynamics that are qualitatively different from the reference experiments performed in vitro.
If this is confirmed by future experiments, the implications for our understanding of plasticity and learning in the brain would be great, believe Eilif Muller and her team.
“What’s exciting about this study is that it’s further confirmation for scientists that we can bridge experimental knowledge gaps using a modeling approach when studying the brain,” said EPFL neuroscientist Henry Markram, founder and director of Blue Brain Project.
It’s open science
“In addition, the model is open source, available on the Zenedo platform,” he added. Here we share hundreds of plastic pyramid cell connections of different types. Not only is it the most widely validated model of plasticity to date, but it also represents the most comprehensive prediction of the differences between plasticity seen in a Petri dish and an intact brain.”
Henry Markram concluded by saying that “this quantum leap is made possible by our collaborative team-based scientific approach. Furthermore, the community can go further and design their own versions, modifying or complementing them. It is an open science and will accelerate progress.”
About this study
The study entitled “A calcium-based plasticity model to predict long-term potentiation and depression in the neocortex”, by Giuseppe Chindemi and his collaborators, was published 1er June 2022 in NatureCommunication. Funding for the Blue Brain Project was provided by the Swiss Federal Institutes of Technology Council. Eilif Muller’s work was also supported by the CHU Sainte-Justine Research Center, IVADO – Data Valorization Institute –, Quebec Research Fund – Health, Canada-CIFAR AI Chairs program, Mila – Quebec Institute of Artiﬁcial Intelligence – and Google .