Understanding Circuits

Abstract The goal of this research is to develop new multivariate data analysis techniques for neural recordings that reveal causal dependencies between recording sites. Delay Differential Analysis (DDA) is a robust and efficient nonlinear time-domain algorithm for time series data that complements linear spectral methods. DDA combines delay and differential embeddings in nonlinear dynamical systems to discriminate between different normal and abnormal cortical states with high temporal resolution and insensitivity to artifacts.

PROJECT SUMMARY. New advances in calcium imaging make it possible to survey the brains of behaving animals at single-neuron resolution, thereby promising to transform the field of neuroscience. However, existing statistical models and methods are inadequate for this complex and noisy data. This proposal involves developing statistical models and methods for the analysis of calcium imaging data. Aim 1 involves deconvolving a neuron's fluorescence trace in order to infer its underlying spike times.

Project Summary / Abstract Our main goal is to unravel communication dynamics in the brain, as they relate to various sensory-motor actions and to the learning process. The sensory-motor system operates through the concerted interaction of multiple closed-loops feedback systems. While some broad level knowledge is available about single neuron properties and general high-level operations, we lack understanding of functional aspects of neural dynamics, of inter-neuronal interactions and of the modular interaction and integration of brain regions contributing to motor activity.

PROJECT SUMMARY Although rhythms are a prominent feature of brain activity, the role of rhythms in brain function (and dysfunction) remains elusive. Rhythms have been proposed to organize information transfer within and between brain regions by modulating neural excitability at different time scales. Rhythms have also been proposed to interact across these different time scales, a phenomenon labeled cross-frequency coupling or CFC.

Project Summary The theory hub put forward in this proposal will work to translate successful and powerful approaches to describing emergent collective behavior in physical systems so they can be applied to the brain.

PROJECT SUMMARY Neuroscience is producing increasingly complex data sets, including measures and manipulations of sub- cellular, cellular, and multi-cellular mechanisms operating over multiple timescales and in the context of different behaviors and task conditions. These data sets pose several fundamental challenges. First, for a given data set, what are the relevant spatial, temporal, and computational scales in which the underlying information-processing dynamics are best understood?

Project Summary / Abstract High-throughput experimental neuroscience has made it possible to observe behavior of many animals, as well as a large groups of neurons simultaneously, providing an exciting opportunity for figuring out how the neural system performs computations that underlie perception, cognition, and behavior. However, there is a major bottleneck in the scientific cycle of data analysis and data collection due to the complexity and scale of noisy, high-dimensional data.

Project Summary/Abstract How neural activity is coordinated within local microcircuits and across brain regions to drive behavior is a central open question in neuroscience. Recent advances in massively-parallel neural recording tech- nologies are producing dynamic activity maps during complex behaviors, with single-neuron granularity and single-spike resolution. To reveal fundamental dynamic features in these large-scale datasets, new principled and scalable computational methods are urgently needed.

Project Summary: The cortex must both track and process dynamically changing environments as well as store and combine diverse inputs to generate complex behavior. Further, the neuronal circuits that accomplish this must be malleable to changing contexts, such as during attention related tasks. Charged with these tasks it is perhaps unsurprising that the response dynamics of populations of cortical neurons is then dauntingly complex. Currently, we lack a deep understanding of the circuit mechanics that underlie the rich dynamics exhibited in the nervous system.

Summary The BRAIN initiative is enabling ground-breaking techniques for brain recordings that will permit a unique view onto the dynamics of neural activity. However, inferring brain function from multi-channel physiological recordings is challenging.