Everyone knows that the human brain is extremely complex. But how exactly does he learn? Well, the answer may be a lot simpler than you think.
An international research group that includes the University of Montreal has taken a breakthrough by accurately simulating the 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 on 1uh June to Nature Communications.
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 at UdeM’s Department of Neuroscience, researcher at IVADO – the Institute for Data Enhancement – and CIFAR- Canada Chair in Artificial Intelligence (AI), who co-directed the study at the Blue Brain Project at the École polytechnique fédérale de Lausanne (EPFL), Switzerland.
Eilif Muller moved to Montreal in 2019 and continues his research at the Architectures of Biological Learning laboratory, which he 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 on branches play a vital role in driving learning in vivo. “
According to him, “this has important implications for understanding the mechanisms of neurodevelopmental disorders such as autism and schizophrenia, but also for the development of powerful new approaches to AI inspired by neuroscience.”
Employees in five countries
Eilif Muller collaborated with a group of scientists from the Blue Brain Project of EPFL, the University of Paris, the Hebrew University of Jerusalem, the Cajal Institute (Spain) and Harvard Medical School to develop a model of synaptic plasticity in neocortex based on post-synaptic calcium dynamics under data constraint.
How does it work? Simpler than you might think.
The brain is made up of billions of neurons that communicate with each other 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 account for 80-90% of neurons and are known to play an important role in learning. Despite their importance, the long-term dynamics of their synaptic changes have only been experimentally characterized among some of their types and have been shown to be diverse.
Therefore, understanding of the complex neural circuits formed, particularly through the stereotyped cortical layers, which determine 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 complete view of the dynamics of synaptic plasticity that govern learning in these neocortical circuits.
By comparing their results with the available experimental data, they showed in their study that their model of synaptic plasticity can explain the various dynamics of plasticity of the various pyramidal cells that make up the neocortical microcircuit. They achieved this by using a single unified set of model parameters, indicating that the rules of neocortical plasticity can be shared by all types of pyramidal cells and thus be predictable.
Most of these plasticity experiments were performed on rodent brain slices in vitro, where the calcium dynamics governing 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 were confirmed by future experiments, the implications for our understanding of plasticity and learning in the brain would be important, believe Eilif Muller and her team.
“The exciting thing about this study is that it is further confirmation for scientists that we can overcome gaps in experimental knowledge by using a modeling approach when studying the brain,” said EPFL neuroscientist Henry Markram, founder and director. of the Blue Brain Project.
It is open science
“Furthermore, the model is open source, available on the Zenedo platform,” he added. Here we have shared hundreds of plastic connections of pyramidal cells of different types. Not only is it the most widely validated model of plasticity to date, it also represents the most comprehensive prediction of the differences between plasticity observed in a petri dish and in an intact brain. ‘
Henry Markram concluded by saying that “this qualitative leap is made possible by our collaborative team-based science approach. In addition, the community can go further and design their own versions by modifying or supplementing them. It is open science and will accelerate progress ”.
About this study
The study entitled “A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex”, by Giuseppe Chindemi and collaborators, was published 1uh June 2022 a Nature Communications. Funding for the Blue Brain project was provided by the Council of Swiss Federal Institutes of Technology. Eilif Muller’s work was also financed by the CHU Sainte-Justine Research Center, by IVADO – the Data Valorization Institute -, by the Quebec Research Fund – Health, by the Canada-CIFAR AI Chairs program, by Mila – the Quebec Institute of Arti ﬁ cial Intelligence – and from Google.