understand communication flows in the brain using machine learning

Scientific research allows us to better understand the functioning of the complex organ that is our brain and that of its 86 to 100 billion neurons. Researchers from Carnegie Mellon University, Albert Einstein College of Medicine and the Champalimaud Foundation have been working for more than 10 years to better understand the flow of communication in the brain and have recently developed a new statistical method, Delayed Latents Across Groups (DLAG). They present their work in the study “Disentangling the flow of signals between populations of neurons” published in Nature Computational Science on August 18.

The brain is essential to the functioning of the human body, neural communication flows allow us to interact with the world around us: taste, touch, sight, hearing and smell are thus transmitted by neurons via sensory flows. Interactions between large collections of neurons fired simultaneously in the brain enable us to see, hear, smell or walk.

Untangling the simultaneous and bidirectional flow of signals between populations of neurons

To untangle signals relayed between brain areas, even when communication between brain areas is bidirectional, the research team developed a new statistical method, named Delayed Latents Across Groups (DLAG).

Evren Gokcen, a graduate student in electrical and computer engineering at Carnegie Mellon, explains:

“The method we developed, DLAG, falls into the broader category of machine learning or statistical methods that examine high-dimensional neural signals. The new aspect is to identify activity patterns that are shared between different areas of the brain”.

He adds :

“For decades, studies have focused on recording one or a handful of neurons from one area of ​​the brain at a time. But with advances in neural recording technology, the bottleneck has shifted to the ability to analyze and interpret recordings of large populations of neurons from multiple areas of the brain. »

Activity models allow us to understand how neurons coordinate their activity with each other. Identifying the patterns of activity involved in communication between different areas of the brain involves several challenges, one is due to the fact that this communication usually occurs bi-directionally and simultaneously.

Evren Gokcen explains the team’s approach:

“To advance in untangled communication, we leveraged a simple insight: you can’t send signals instantly; it takes a while for the information to flow. Videoconferencing is a great point of reference when thinking about a delay in communication; it’s similar in the brain. With DLAG, we exploit this delay, so if the signal appears first in area A and then in area B, we take this to mean that area A sent the signal to area B. Using the DLAG method, we can separate signals relayed simultaneously. »

Study results

The researchers demonstrated that DLAG performs well on similar synthetic datasets at the scale of current neurophysiological recordings. Next, they simultaneously studied the populations recorded in the visual areas of V1 and V2 primates, where DLAG reveals signatures of bidirectional but selective communication. Their framework lays the groundwork for dissecting the complex flow of signals through populations of neurons, and how this signaling contributes to cortical computation.

DLAG could be used for other neuroscience applications, such as understanding the interaction between different cell types, neurons or between different layers of the brain.

Byron YU, professor of biomedical engineering and electrical and computer engineering at Carnegie Mellon, concludes:

“Introducing DLAG is like introducing a scalpel to gain potentially deeper insight into how areas of the brain communicate with each other. Along with this article, we are making our source code available to other members of the scientific community. DLAG can be used to study other brain systems outside of the visual system where we have focused, for example to study memory, decision making, and motor control. »

Sources of the article:

“Unraveling signal flows between populations of neurons”

Study published on August 18, 2022 in Nature Computational Science,

Authors and affiliations:

  • Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA: Evren Gokcen, João D. Semedo, and Byron M. Yu;
  • Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA: Anna I. Jasper, Amin Zandvakili, and Adam Kohn;
  • Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, New York, NY, USA: Adam Kohn;
  • Department of Systems and Computational Biology, Albert Einstein College of Medicine, New York, NY, USA: Adam Kohn;
  • Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon, Portugal: Christian K. Machens;
  • Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA: Byron M. Yu.

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