Theory & Data Analysis Tools

Project Summary/Abstract The broad, long-term objective of this grant is to advance a new theoretical approach to identify synchronized building blocks of neural circuits based on group theory and its application to understand the permutation symmetries of these circuits. Based on the developed theoretical framework we will validate our theory by probing brain dynamics at single-cell resolution and in real-time, i.e. sub-second scale, in C. elegans, which is a system with a fully mapped synapse-resolution connectome.

Project summary: Modern electron-microscopy (EM) imaging and analysis methods now permit the comprehensive reconstruction of all neurons and synapses in large volumes of brain tissue or the entire brains of individual organisms. However, relating this structure to function is difficult. The rapidly increasing scale of these datasets requires the develop- ment of new quantitative techniques to address this challenge.

The brain is a complex dynamical system, with a hierarchy of spatial and temporal scales ranging from microns and milliseconds to centimeters and years. Activity at any given scale contributes to activity at the scales above it and can influence activity at smaller scales. Thus a true understanding of the brain requires the ability to understand how each level contributes to the system as a whole. Most brain research focuses on a single scale (single unit firing, activity in a circuit), which cannot account for the constraints imposed by activities at other scales.

Innovations in human neuroimaging tools have driven profound advances in our understanding of brain function under well-controlled and constrained conditions.

ABSTRACT (30 Lines) The BRAIN initiative (RFA-EB-19-002) has called for the development of entirely new or next-generation noninvasive human brain imaging tools and methods that will lead to transformative advances in our understanding of the human brain.

Project Summary Advances in neural recording technology now provide access to neural activity at high temporal resolutions, from many brain areas, and during complex and naturalistic behavior. Interpreting these types of high-dimensional and unconstrained neural recordings is still a major challenge in neuroscience. The aim of this project is to develop innovative methods for distilling high-dimensional neural activity patterns into simpler low-dimensional formats that can be effectively compared across time, conditions, or even across species.

Project Summary: Organisms' actions and decisions are guided by experience. Models of such behavior often appeal to the formalism of probabilistic inference, in which expectations about the world build up sequentially due to past observations. These models can account for typical response patterns of subjects performing cog- nitive tasks. However, a theory grounded in biophysical principles of neural circuit architecture and activity is lacking.

We propose to develop a probe technology for monitoring human brain function with molecular precision; in conjunction with magnetic resonance imaging (MRI) or other imaging modalities, the probes will provide a combination of sensitivity and resolution that could permit unprecedented noninvasive studies of dynamic neu- rophysiological processes in people.

Abstract The field of neuroscience is experiencing unprecedented growth in the ability to record from and manipulate brain circuits in humans and in animal models. MEG/EEG are the leading methods to non-invasively record human neural dynamics with millisecond temporal resolution. However, it is still extremely difficult to interpret the underlying cellular and circuit level generators of these `macro-scale' signals without simultaneous invasive recordings.

Project Summary Abstract Learning new tasks and exposure to new environments lead to changes in the dynamics of brain circuits, as observed in various recent experiments. The ability to embed the statistics of the environment within brain circuits is essential for animals ability to thrive and survive in changing environments. However, the mechanisms by which circuits dynamics are implemented and learned are not well understood, and pose significant theoretical challenges. Recent work in both theoretical and experimental labs has highlighted the importance of circuit dynamics.