Funded Projects

Browse wide-ranging research at the frontiers of neuroscience supported by Wu Tsai Neurosciences Institute grants, awards, and training fellowships.

Wu Tsai Neurosciences Institute
SIGF - Graduate Fellowship
2016
A principled investigation into the heterogeneous coding properties of medial entorhinal cortex that support accurate spatial navigation

Navigation through an environment to a remembered location is a critical skill we use every day. How does our brain accomplish such a task? Over the last few decades, several lines of evidence have suggested that a brain region called medial entorhinal cortex (MEC) supports navigation by encoding information our location and movement within an environment.

Wu Tsai Neurosciences Institute
SIGF - Graduate Fellowship
2016
Understanding why neurons die in disease

Many neurological diseases feature the death of neurons, but the mechanisms that mediate cell death in these disorders are unknown. Astrogliosis, the response of a cell-type called “astrocytes” to injury, is common to most diseases of the central nervous system (CNS), and recent studies in our lab suggest that some reactive astrocytes may release a protein that is potently toxic to neurons.

Wu Tsai Neurosciences Institute
SIGF - Graduate Fellowship
2015
Understanding cellular responses induced by chronic implantation of electrodes using a novel human neural differentiation platform

Electrodes implanted in the brain have great potential, with applications in neurodegenerative disease, brain-computer interfaces, and more. However, the presence of electrodes in brain tissue causes a response known as gliosis, in which a scar forms around the electrode, reducing its effectiveness and access to neurons.

Wu Tsai Neurosciences Institute
SIGF - Graduate Fellowship
2017
Engineering versatile deep neural networks that model cortical flexibility

In the course of everyday functioning, animals (including humans) are constantly faced with real-world environments in which they are required to shift unpredictably between multiple, sometimes unfamiliar, tasks. But how brains support this rapid adaptation of decision making schema, and how they allocate resources towards learning novel tasks is largely unknown both neuroscientifically and algorithmically.

Wu Tsai Neurosciences Institute
Research Accelerator Award
2017
The neural prosthetics translational laboratory
Our research focuses on the twin goals of investigating fundamental principles of human neuroscience and translating laboratory insights into clinically viable assistive devices for people with paralysis.
Wu Tsai Neurosciences Institute
Research Accelerator Award
2017
StrokeCog

StrokeCog is focused on cognitive problems after stroke. The team leads a study aimed at identifying if neuroinflammation plays an important role in the development of post-stroke cognitive decline.

Wu Tsai Neurosciences Institute
SIGF - Graduate Fellowship
2018
Deep brain microstimulation for memory recovery

Yi Lui's project aims to use deep brain microstimulation (DBMS), which causes even less brain damage and has higher spatial resolution than DBS, for memory recovery.

Wu Tsai Neurosciences Institute
SIGF - Graduate Fellowship
2018
Synaptic rules and circuit architectures for learning from feedback

Dr. Brandon Jay Bhasin will use engineering principles from modern control theory, experimental neuroscience and computational neuroscience to significantly advance understanding of how feedback driven plasticity in a tractable neural circuit is orchestrated across multiple synaptic sites and over various timescales so that circuit dynamics are changed to improve performance.

Wu Tsai Neurosciences Institute
SIGF - Graduate Fellowship
2019
Weak supervision in medical multi-modal time series

The project aims to alleviate this bottleneck by developing a weak supervision system that optimally deals with time-series data and takes advantage of multiple data modalities.

Wu Tsai Neurosciences Institute
SIGF - Graduate Fellowship
2020
How do Schwann cells sort and myelinate axons in the developing peripheral nervous system?

Schwann cells (SCs) sort and myelinate peripheral axons, and impairments in either process can cause long-term disability. There are no therapeutic strategies for targeting SC dysfunction, underscoring the need to investigate mechanisms of sorting and myelination. Both processes require highly motile SC cytoplasmic protrusions, but the basis of this motility is unclear.

Wu Tsai Neurosciences Institute
SIGF - Graduate Fellowship
2020
Magnetic Resonance Imaging of Epileptogenesis

Absence epilepsy is a form of pediatric epilepsy which causes seizures with brief lapses in awareness. Electron microscopy results in a murine model of absence epilepsy support the hypothesis that maladaptive myelination plays a role in disease progression.

Wu Tsai Neurosciences Institute
Research Accelerator Award
2021
Neurodevelopment Initiative

Elucidating the development of the infant’s brain structure & function.

Wu Tsai Neurosciences Institute
Research Accelerator Award
2021
NeuroPlant Initiative

The NeuroPlant Initiative aims to leverage a botanical armamentarium to manipulate the brain — by building a pipeline to explore chemicals synthesized in plants as potential new treatments for neurological disease and as a window into the chemistry of the brain.

Wu Tsai Neurosciences Institute
SIGF - Graduate Fellowship
2021
Elucidating mechanisms of microglial tiling

In a process called tiling, homeostatic microglia homogenously organize in a grid-like fashion to achieve efficient surveillance of the brain. The molecular mechanisms underlying tiling are unknown. I hypothesize that microglia use cell-surface proteins to sense density of neighboring microglia, thereby contributing to constant cell-to-cell distances.

Wu Tsai Neurosciences Institute
SIGF - Graduate Fellowship
2021
Inference via Abstraction: A framework for efficient Bayesian cognition

We propose a novel framework for efficient Bayesian cognition called Inference via Abstraction (IvA), which learns to approximate complex world models with simpler abstractions that capture main dependencies, but leverage structure in the prior distribution for efficient inference. We instantiate IvA with a combination of probabilistic graphical models and deep neural networks.