*** BME Faculty Candidate ***

Nicolas Pegard, Ph.D.*

Postdoctoral Researcher,
Department of Molecular & Cell Biology
Department of Electrical Engineering and Computer Science
University of California, Berkeley

Computational Optics Beyond Imaging: New Instrumentation to Monitor and Manipulate Neural Activity with Light

ABSTRACT

Future progress in neuroscience research and medicine requires high-performance instruments that can monitor and manipulate brain activity on demand, in 3D, and at high speeds. With the recent development of optogenetics and optical reporters of neural activity, it is already possible, in theory, to measure, trigger or inhibit activity in individual neurons with light. However, current optical instrumentation is unfit to address these new challenges since it relies on recording then processing large 3D images, which introduces inefficiencies and limits the number of neurons that can be simultaneously addressed.

In this presentation, we propose new optical instrumentation and algorithms that are jointly designed and optimized to perform brain-machine interfacing tasks with a fundamentally different approach that moves away from image acquisition and processing. We first show how functional fluorescence signals (e.g. with GCaMP) can be measured simultaneously in many neurons and in 3D by directly processing a single raw data frame acquired with a light-field microscope. Specifically avoiding image reconstruction improves speed, but also better preserves relevant quantitative information in the presence of strong optical aberrations.

We then consider the reverse problem of sculpting light in 3D to photostimulate custom neuron ensembles on demand. For this, we have developed a new multiphoton illumination technique called 3D-SHOT, which combines 3D computer generated holography and temporal focusing. Experiments in awake mouse demonstrate precise targeting capabilities with high temporal precision and single neuron spatial resolution in large volumes, outperforming other image-forming strategies.

Finally, we present new algorithms to compute better holograms by solving an optimization problem with an explicit cost function. For applications in neuroscience, we show how performance can be further improved by tailoring the cost function to account for known biological properties of brain tissue and to directly optimize for the desired end goal.

Host: Jaydev Desai, Ph.D.