Faculty Seminar - Nicholas Pegard, Berkeley

Thu, 03/01/2018 - 10:00am
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Faculty Seminar by Nicholas Pegard from UC Berkeley.

Title : 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 theoretically possible 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, introducing inefficiencies and limiting 
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 without the need for image 
reconstruction and processing. We first show how functional fluorescence 
signals (e.g. with GCaMP) can be quantified 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 and 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 mice 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 optimize for the desired task outcome.

Please see the full announcement here.