Research Projects

The brain uses its own previous activity to adapt to an ever-changing environment. This history dependent adaptation takes place at all scales of organization of the nervous system. The objective of this project is to develop a common theoretical formalism to be applied to multiple history dependent phenomena, from the biochemical reactions that underlie synaptic plasticity, to the emergent patterns in complex neural networks. At the core of this formalism is the recognition that most models of neuronal activity are based on the classical reaction-diffusion equation.

Abstract: Causality is central to neuroscience. For example, we might ask about the causal effect of a neuron on another neuron, or its influence on perception, action, or cognition. Moreover, any medical approaches aim at producing a causal effect – effecting improvements for patients. Randomized controlled trials (RCTs) are the gold standard to establish causality, but they are not always practical. For example, while we can electrically or optogenetically activate entire areas, large-scale targeted stimulation of individual neurons is hard.

The massive neural activity data collected through transformative new technologies present an incredible opportunity in facilitating the mission of the BRAIN Initiative and the wider neuroscience community: to understand brain function.

Project Summary A central goal of systems neuroscience is to relate behavior to its underlying circuit dynamics. This task is complicated by the complex and circuitous paths along which information flows as it is encoded and processed in the many steps between sensory inputs and motor outputs. Currently, we understand little regarding the organization and dynamics of interactions between brain areas. For example, we do not know the degree to which specific brain areas have separate representations versus when information is encoded jointly across brain areas.

A theory of population coding in the cerebellum In order to move accurately, the brain relies on internal models that predict the sensory consequences of motor commands. Evidence for this idea comes from human behavioral experiments [1-7] and animal lesion studies [8- 11], suggesting that the critical structure for forming internal models is the cerebellum.

Abstract How is connectivity between neurons related to patterns of activity exhibited by these neurons in vivo? This question of structure-function relations in brain circuits is of fundamental importance. Answering it in a quantitative manner would have far-reaching consequences both for our theories of how brain works and for applications ranging from better disease treatments to new tools for artificial intelligence.

PROJECT SUMMARY Advanced brain research demands ultra-high field MRI systems. The 11.7 T Neurospin CEA MRI magnet pushed the use of superconducting NbTi materials to the limit by using superfluid helium to cool. To design and build a cost effective 16 T head-only MRI magnet, Nb3Sn wires must be used. To reduce the risks with such a high-field, high-stress, and high-stored energy magnet, critical technologies must be developed before a 16 T MRI system can be realized. One of the biggest risks is the cracking of the Nb3Sn coil composite matrix under high mechanical and thermal stress.

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.