How does the brain learn? | UdeMNews

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 made a major breakthrough by accurately simulating synaptic changes in the neocortex thought to be 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 01.01ah June a nature communication.

A world full of new directions

“This opens a world of new directions for scientific investigations into how we learn,” said Eilif Muller, an assistant professor in the Department of Neuroscience at UdeM, a researcher at IVADO – the Institute for Data Valorization – and the CIFAR-Canada Chair in Artificial Intelligence ( 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 his research at the Architectures of Biological Learning Laboratory, which he founded at the CHU Sainte-Justine Research Center in collaboration with UdeM and Mila, the Quebec Institute of Artificial Intelligence.

‘Neurons are shaped like a tree and synapses are the leaves on the branches,’ explained Professor Muller, co-author of the study. Previous approaches to modeling plasticity ignored this tree structure, but now we have the computational tools to test the idea that synaptic interactions at branches play a fundamental 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 developing powerful new approaches to AI inspired by neuroscience.”

employees in five countries

Eilif Muller collaborated with a group of scientists from EPFL’s Blue Brain Project, 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 of postsynaptic calcium dynamics under data limitation.

How does it work? Easier than you might think.

The brain is made up of billions of neurons that communicate with each other by forming trillions of synapses. These junctions between neurons are complex molecular machines that are constantly changing under the influence 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 their importance, the long-term dynamics of their synaptic changes have only been experimentally characterized between a few of their types and have been shown to be diverse.

Limited understanding

Therefore, understanding of the complex neural circuits formed is limited, particularly by the stereotyped cortical layers that determine how the different regions of the neocortex interact. The innovation of Eilif Muller and her colleagues was to use computational models to get a more comprehensive view of the dynamics of synaptic plasticity that controls 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 different plasticity dynamics of the different pyramidal cells that make up the neocortical microcirculation. They achieved this by using a single consistent set of model parameters, indicating that the rules governing neocortical plasticity are shared by all pyramidal cell types and are therefore predictable.

Most of these plasticity experiments were performed in vitro on rodent brain slices, where the calcium dynamics that control 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 benchmark experiments conducted in vitro.

If confirmed by future experiments, the implications for our understanding of brain plasticity and learning would be significant, Eilif Muller and his team believe.

“What is exciting about this study is that it is further confirmation for scientists that we can fill 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

“Moreover, the model is open source and 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, 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, “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 adding to 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 his collaborators was released 1ah June 2022 on nature communication. The Blue Brain Project was financed by the Council of Swiss Federal Institutes of Technology. Eilif Muller’s work was also 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 Quebec Institute of Artificial Intelligence – and Google.

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