Experimental and theoretical advances push study of metastable cortical neural networks forward
Experimental and theoretical advances push study of metastable cortical neural networks forward lead image
The dynamics of neural networks has garnered attention as researchers explore the multitude of patterns of activity generated by cortical neurons. Recent advances have allowed investigators to model the transiently occupied, metastable states of neurons that might underlie how the brain perceives stimuli or performs internal computations.
Brinkman et al. provided an overview of metastable neural dynamics, highlighting experimental evidence for metastable dynamics together with theoretical approaches to study metastable activity in neural circuits.
“We have known for a long time that these neural responses are not ‘static’, but instead have some temporal modulation. The meaning of these modulations is hard to capture, due to their variability,” said author Giancarlo La Camera. “The idea emerging now is that specific temporal modulations with the right characteristics are the main message carried by the neurons, and could underlie our percepts, memories, or decisions.”
The review called attention to a wide range of sensory and cognitive functions in which metastable dynamics may have an important role. These phenomena have been observed in brain regions of rodents, macaques, and humans and include stimulus coding, spatial navigation, and decision making.
The paper also described theoretical approaches for providing a more cohesive view of metastable neural activity. Understanding the origin and role of such activity from a theoretical viewpoint would provide a better understanding of the neural underpinnings for perception, memory, expectation, and disease.
The group aims to determine the structure of real cortical circuits and establish how well the current models agree with this structure and to what extent they must be modified.
Source: “Metastable dynamics of neural circuits and networks,” by Braden A.W. Brinkman, Han Yan, Arianna Maffei, Il Memming Park, Alfredo Fontanini, Jin Wang, and Giancarlo La Camera, Applied Physics Reviews (2022). The article can be accessed at https://doi.org/10.1063/5.0062603 .
