Simulated neurons show that learning is simpler than you think

An international team led by computational neuroscientist Eilif Muller UdeM has simulated how neocortex synapses change to better understand how we learn.

Everyone knows that the human brain is very complex, but how exactly does it learn? Well, the answer may be much simpler than you think.

An international research team involving the University of Montreal has made great strides in accurately simulating synaptic changes in the neocortex, which are believed to be key to learning, opening the door to greater understanding of the brain.

The Scientists’ Study, an open source model, was published on June 1 Nature Communications.

“The world of new directions”

“This opens up a world of new directions for how we learn to do scientific research,” said Eilif Muller, an IVADO research assistant professor at UdeM and a CIFAR AI Chair in Canada, who led a study on the Blue Brain Project at École. -en. Polytechnique Federale de Lausanne (EPFL), Switzerland.

Muller moved to Montreal in 2019 and is doing his research at the Biological Learning Architecture Laboratory, which he founded at the CHU Sainte-Justine Research Center, along with UdeM and Mila Quebec Artificial Intelligence Institute.

“Neurons are tree-shaped, and the synapses are the leaves on their branches,” said Muller, lead author of the study.

“Previous approaches to model plasticity have neglected this tree structure, but we now have computational tools to demonstrate that synaptic branch interactions play a key role in guiding learning. in vivo“he said.

“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.”

Collaborators from five countries

Muller collaborated with a team of scientists from EPFL’s Blue Brain Project, Université de Paris, Jerusalem Hebrew University, Instituto Cajal (Spain) and Harvard Medical School to invent a model of synaptic plasticity in the neocortex based on limited data. postsynaptic calcium dynamics.

How does it work? It’s complicated, but ultimately simpler than you think.

The brain is made up of billions of synapses that communicate with each other to form billions of synapses. These connection points between neurons are complex molecular machines that are constantly changing as a result of external stimuli and internal dynamics, a process called synaptic plasticity.

In the neocortex, a key area associated with the learning of higher cognitive functions in mammals, pyramidal cells (PCs) account for 80 to 90 percent of neurons and play an important role in learning. Importantly, the long-term dynamics of their synaptic changes have been experimentally characterized among a few PC types, and have been shown to be diverse.

Only limited understanding

As a result, there has been a limited understanding of the complex neural circuits they form, especially across stereotyped cortical layers, in which different regions of the neocortex promise how they interact. The innovation of Muller and his colleagues was the use of computational modeling to gain a broader view of the dynamics of synaptic plasticity that regulates learning in these neocortical circuits.

Comparing the results with the available experimental data, they showed in their study that their synaptic plasticity pattern could take on the multiple plasticity dynamics of the various PCs that make up the neocortical microcircuit. And they did so using a single set of parameters from a single unified model, indicating that the rules of plasticity of the neocortex could be shared between pyramidal cells, and therefore that they could be predictable.

Most of these plasticity experiments were performed on rodent brain slices in vitrowhere calcium dynamics, which promotes synaptic transmission and plasticity, varies significantly compared to whole-brain learning. in vivo. Most importantly, the study predicts different qualitative dynamics of plasticity from the reference experiments performed. in vitro. If future experiments confirm that the effects of understanding plasticity and learning in our brains would be profound, Muller and his team believe.

“What’s exciting about this study is that it shows that we can overcome the gaps in experimental knowledge using a model approach for scientists to study the brain,” said EPFL neuroscientist Henry Markram, founder and director of the Blue Brain Project.

“This is open science”

“Furthermore, the model is open source, available on the Zenod platform,” he added.

“Here we have shared hundreds of different types of pyramidal plastic cell connections. Not only is the plasticity pattern validated so far, it also represents the broadest prediction of the differences between plasticity seen in a Petri dish and the whole brain.

This leap is possible because of our collaborative team science approach. In addition, the community can take it further and develop its own versions by changing or adding to it; this is an open science, and it will accelerate progress. ”

Reference: Chindemi G, Abdellah M, Amsalem O, et al. A model of calcium-based plasticity to predict long-term potentiation and depression in the neocortex. Nature Communications. 2022; 13 (1): 3038. doi: 10.1038 / s41467-022-30214-w

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