Project Summary Current electrode based approaches for recording of brain activity, as well as EEG and MEG, provide high temporal resolution, but are limited to thousands of channels. Functional MRI can interrogate hundreds of thousands of brain voxels in parallel, but is limited by hemodynamics to a temporal resolution on the order of seconds.
Project Abstract The goal of this R21 proposal is to develop a robust, minimally invasive wireless photometry system for in vivo calcium measures in freely moving behavior. To achieve this, a miniaturized, wireless, `injectable' photometry platform (~300 mm wide, ~100 mm thick and several mm long) that enables quantitative measurements of fluorescence stimulated using a high performance microscale inorganic light emitting diode (µ-ILED) and captured using a co-located, sensitive microscale inorganic photodetector (µ-IPD) is proposed.
Project Summary Rapid Electrode Multiplexing for Scalable Neural Recording Large-scale recording of neural signals is essential for gaining a better understanding of the elaborate, dynamic picture of the brain that emerges from interactions involving individual cells and complex neural circuits. Over the past few decades extracellular neural recording capabilities have progressed from single unit in vitro recordings to simultaneous monitoring of the activity of about one hundred neurons in vivo.
Project Summary: This proposal outlines the development of a fundamentally new optogenetic technology capable of flexibly manipulating the activity of thousands of neurons contributing to the dynamic activity of distributed neural circuits with single neuron resolution. No method that currently exists even remotely meets the need of flexible, selective control of thousands of neurons distributed across large volumes of the brain.
Abstract Many scientific efforts have been devoted to understanding the brain functions, and the relevance of its dynamics during development, aging, and in diseased conditions. Alterations of the brain functions can result from multifactorial processes and be reflected by various biomarkers. The ability to quantify these changes at multiple scales will improve our understanding of brain anatomical and functional architectures, and the relations between these networks in both normal and diseased conditions.
Project Summary/Abstract A central problem in neuroscience is to understand how activity arises from neural circuits to drive animal behaviors. Solving this problem requires integrating information from multiple experimental modalities and organization levels of the nervous system. While modern neurotechnologies are generating high-resolution maps of the brain-wide neural activity and anatomical connectivity, novel theoretical frameworks are urgently needed to realize the full potential of these datasets.
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.
