Theory & Data Analysis Tools

Over the past several decades, the ability to record from large populations of neurons (e.g., multi-electrode arrays, neuropixels, calcium imaging) has increased exponentially, promising new avenues for understanding the brain. These data have the promise to provide a qualitatively different view of activity within and across brain areas than was previously possible, but the effort will require the development of advanced analytical tools.

Project Summary/Abstract This project constructs a unifying model that links synaptic morphodynamics, the fundamental process of learning and memory in the brain, to the underlying molecular signaling pathways that regulate it. The motivation for this work is a new class of machine learning methods for multiscale modeling that are a promising candidate for linking the disparate spatial and temporal scales involved, from s calcium events in nano-domains to actin reorganization on the order of minutes across a dendritic spine head.

Project Summary A prominent feature of biological neuronal networks is the astonishing diversity of their cell types.

PROJECT ABSTRACT Large-scale realistic model of neuronal network is a powerful tool for studying neural dynamics and cognitive functions. It integrates multi-scale neurobiological mechanisms/processes identified through diverse hypotheses and experimental data into a single platform. However, due to its high complexity and lack of neuron-to-neuron correspondence to experimental data, it is difficult to constrain, validate and optimize such a model using large- scale neural activities recorded from behaving animals, which are most relevant to cognitive processes.

ABSTRACT Recent advances in neural recording technologies have made it possible to study increasingly large and di- verse subsets of neurons, producing a growing interest in the collective computational properties of neural pop- ulations. Ideally, causally testing these population hypotheses requires timing and selecting experimental ma- nipulations based on the current state of neural dynamics, but technical limitations have rendered this difficult in practice.

Project Summary How do distributed neural circuits drive purposeful movements from the complex musculoskeletal system? This understanding and characterization will be critical towards the application of principled neurostimulation to specific brain regions to study the effect of neural circuit perturbations on behavior, and conversely towards predictions of the neural activity during perturbations in the behavior.

PROJECT SUMMARY/ ABSTRACT Advances in neurotechnologies are producing large and complex datasets at unprecedented rate. Large-scale electrophysiological and optical imaging recordings provide an urging need to develop novel theoretical and analytical approaches to analyze and interpret these multi-scale, multi-resolution brain recordings.

Project Summary Divisive normalization (DN) is a well-established theory of how interactions between neurons in a circuit modulate the activity of individual neurons. DN has been termed a canonical operation because it describes a wide range of empirical data across species and brain areas, and theory predicts that DN underlies behavioral gains of sensory integration and visual attention. Despite this progress, it has been difficult to tie DN to circuit and cellular mechanisms, and to quantify its impact on neural coding and behavior. Two main obstacles have limited progress.

ABSTRACT Much evidence suggests that the development of orientation selectivity and maps in primary visual cortex (V1) is instructed by spontaneous patterns of input activity, without the necessity of visual experience.

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.