2022 Research Grant Awards
Awarded to Research Professor Katalin Fejes Toth (BBE)
Regulation of neurogenesis by the piRNA pathway
The goal of this project is to illuminate how neurogenesis is regulated by piRNAs, a class of non-coding RNAs known to repress transposable elements (TEs) and other genomic targets. Neurogenesis is a complex process that requires the orchestrated action of many genes and gene regulatory networks to produce the countless cell types, structures and functions of the nervous system.
Awarded to Research Professor Carlos Lois (BBE) and Assistant Professor Rebecca Voorhees (BBE)
Rapid genetic perturbation of neuronal protein function with nanobodies
The Lois and Voorhees labs plan to to establish a flexible, high-throughput screening platform to identify nano-bodies that acutely modulate the function of a model ion channel in mammalian cells, with the ultimate goal of using them in organismal models and as potential therapeutics.
T&C CHEN CENTER FOR SYSTEMS NEUROSCIENCE AWARDS
Awarded to Professor Michael Dickinson (BBE) and Assistant Professor Joseph Parker (BBE)
Evolution of anatomical novelties and their neural control
In this project Dickinson and Parker will study the mechanisms by which central and peripheral circuits have been evolutionarily modified to harness volitional control of an anatomically novel structure. To do so, they will make use of the evolution of a novel defensive gland and its neural innervation within a hyper-diverse clade of beetles—the Staphylinidae—also known as rove beetles.
Awarded to Assistant Professor Elizabeth Hong (BBE) and Assistant Professor Matt Thomson (BBE)
Investigating an instructive role for spontaneous activity in the multistage assembly of an insect olfactory network
This project combines a new theoretical framework from Thomson with experimental neuroscience (imaging) from Hong, to model and understand the role of spontaneous neural activity during larval olfactory system development in Drosophila.
Awarded to Professor David Prober (BBE)
Exploring neuromodulator and neuropeptide dynamics in sleep using gentetically encoded fluorescent biosensors
In this project Prober will perform whole-brain imaging of GRAB sensors in 5-day old larval zebrafish to monitor neuromodulator dynamics in the brain during natural sleep-wake cycles and in response to perturbations that affect homeostatic or circadian regulation of sleep.
T&C CHEN CENTER FOR SOCIAL AND DECISION NEUROSCIENCE AWARDS
Awarded to Professor Ralph Adolphs (HSS) and Mike Tyszka (HSS), Associate Director, Caltech Brain Imaging Center
What does the amygdala do?
Adolphs and Tyszka plan to scan three individuals for 20-30 hours each in the Caltech Brain Imaging Center (CBIC) while they watch a rich set of visual stimuli. The overall goal is to cast a broad net on rich sensory stimuli, focusing on the amygdala with the best imaging parameters possible, to discover those stimulus features most reliably associated with amygdala activation.
T&C CHEN BRAIN-MACHINE INTERFACE CENTER AWARDS
Awarded to Professor Anima Anandkumar (EAS)
Fourier Neural Operator for Ultrasonic Brain-Machine Interfaces
Using existing ultrasound neuro-imaging datasets, collected and annotated by the Andersen and Shapiro labs at Caltech. Anandkumar will train a Fourier neural operator, a machine learning technique developed by her lab, to accomplish the job of transferring high frequency sampled ultrasound images to high-quality power doppler output. This project will be the first to incorporate operator learning using high-fidelity spatio-temporal neural data to make real-time functional ultrasound possible.
Awarded to Assistant Professor Katherine Bouman (EAS)
A Probabilistic Framework for Robust Neural Inversion in an Evolving System
In this project Bouman proposes to use statistical modelling techniques to build forward models between observed neural data and behavior. A major problem in brain-machine interfaces that use single neuron population recordings is that of stability. Neurons come and go over time and as a result the decoders need to be constantly recalibrated. By treating this as a statistical model of estimation by neural inversion, this machine learning technique will learn the statistical properties of the forward model over time.