Everyone knows that the human brain is extremely complex. But how exactly does he learn? Well, the answer could be much simpler than you think.
An international research team that includes the University of Montreal has made a breakthrough by accurately simulating synaptic changes in the neocortex that are thought to be essential for learning, paving the way toward a better understanding of brain function.
The scientists’ study, which is based on an open source model, was published on 1Ahem june in Nature Communications.
A world of new directions
“This opens up a world of new directions for scientific research on how we learn,” said Eilif Muller, assistant professor in the Department of Neurosciences at UdeM, researcher at IVADO – the Institute for Data Valorization – and CIFAR-Canada Chair in Intelligence Artificial (AI). ), who co-led 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 her research at the Biological Learning Architectures lab, which she founded at the CHU Sainte-Justine Research Center in partnership with UdeM and Mila, the Quebec Institute of Artificial Intelligence. .
“Neurons are tree-shaped and the synapses are the leaves of 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 critical role in guiding 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 new and powerful approaches to AI inspired by neuroscience.”
Employees in five countries
Eilif Muller collaborated with a group of scientists from the EPFL Blue Brain Project, 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 the neocortex based on postsynaptic calcium dynamics under data constraint.
How does it work? Simpler than you 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 called 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 its importance, the long-term dynamics of its synaptic changes have only been experimentally characterized among a few of its types and have been shown to be diverse.
Therefore, understanding of the complex neural circuits formed, particularly across stereotyped cortical layers, that dictate how the various regions of the neocortex interact, is limited. Eilif Muller and his colleagues’ innovation was to use computer models to gain a more complete view of the dynamics of synaptic plasticity that govern 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 plasticity dynamics 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 neocortical plasticity rules may be shared by all pyramidal cell types and thus be predictable.
Most of these plasticity experiments have been 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 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 significant, Eilif Muller and her team believe.
“What’s exciting about this study is that it is further confirmation for scientists that we can bridge experimental knowledge gaps by using a modeling approach when studying the brain,” said EPFL neuroscientist Henry Markram, Founder and Director of the Blue Brain Project.
it’s open science
“In addition, the model is open source, available on the Zenedo platform,” he added. Here we have shared 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 observed in a Petri dish and in an intact brain.”
Henry Markram concluded by saying that “this quantum leap is made possible by our team-based collaborative scientific approach. In addition, the community can go further and design their own versions by modifying or complementing them. It is 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 colleagues, was published 1Ahem June 2022 in Nature Communications. Funding for the Blue Brain Project was provided by the Council of Swiss Federal Institutes of Technology. Eilif Muller’s work has also been funded by the CHU Sainte-Justine Research Center, IVADO – the Data Valorization Institute -, the Quebec Research Fund – Health, the Canada-CIFAR AI Chairs program, Mila – the Institute of Artificial Intelligence of Quebec – and Google.