Abstract:
For an animal to perform any function, millions of neurons in its nervous system furiously interact with each other. Be it a simple computation or a complex behavior, all biological functions involve many individual units. A theory of function must specify how to bridge different levels of description at different scales. For example, to predict the weather, it is irrelevant to follow the velocities of every molecule of air. Instead, we use coarser quantities of aggregated motion of many molecules, e.g., pressure fields. Statistical physics provides us with a theoretical framework to specify principled methods to systematically ‘move’ between descriptions of microscale quantities (air molecules) to macroscale ones (pressure fields). Can we hypothesize equivalent frameworks in the nervous system? How can we use descriptions at the level of neurons and synapses to make precise predictions of activity and behavior? My research group will develop theory, modeling, and machine learning tools to discover generalizable forms of scale bridging across species and behavioral functions.
In this talk, I will present lines of previous, ongoing, and proposed research that highlight the potential of this vision. I shall focus on two seemingly very different systems: mouse brain neural activity patterns, and octopus skin cells activity patterns. In the mouse, we reveal striking scaling behavior and hallmarks of a renormalization group- like fixed point governing the system. In the octopus, camouflage skin pattern activity is reliably confined to a (quasi-) defined dynamical space. Finally, I will touch upon the benefits of comparing across animals to extract principles of multiscale function in the nervous system, and propose future directions to investigate how macroscale properties, such as memory or camouflage, emerge from microscale level activity of individual cells.