Browse wide-ranging research at the frontiers of neuroscience supported by Wu Tsai Neurosciences Institute grants, awards, and training fellowships.
Projects
Stanford NeuroTechnology Initiative (Phase 2)
Our goal is to develop the next generation of neural interfaces that match the resolution and performance of the biological circuitry. We will focus on two signature efforts to spearhead the necessary advances: high-density wire bundles for electrical recording and stimulation, and analog and digital bi-directional retinal prostheses for restoration of vision.
The NeuroFab: The hub for new ideas in neuro-engineering
Creating an incubator for next-generation neural interface platforms.
Brain-machine interfaces: Science, engineering, and application
Developing technology to interface with the brain and create intelligent prosthetics.
Geometric analysis and variability mapping in human white matter brain structures
Understanding the relationship between structure and function in the human brain is a key interest in neuroscience. In recent years the focus is turning to understanding the role of the white matter in human cognition, brain function and neurological disorders.
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.
Modeling proprioceptive deficits for the design of novel sensory augmentation for post-stroke movement rehabilitation
Stroke is the main cause of adult disability; 80% of survivors sustain motor (movement) deficits that interfere with activities of daily living. There exists no proven therapeutic strategy for motor recovery of the upper extremity following stroke.
Neural mechanisms of learning multiple motor skills and implications for motor rehabilitation
A hallmark of the motor system is its ability to execute different skilled movements as the situation warrants, thanks to the flexibility of motor learning. Despite many behavioral studies on motor learning, the neural mechanisms of motor memory formation and modification remain unclear.
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.
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.
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.
Neuro-omics Initiative (Phase 1)
Creating new tools to help neuroscientists bridge the study of genes and proteins operating in the brain to the study of brain circuits and systems, which could lead to a deeper understanding of brain function and disease.
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.
Neuro-Omics Initiative (Phase 2)
Creating new tools to help neuroscientists bridge the study of genes and proteins operating in the brain to the study of brain circuits and systems, which could lead to a deeper understanding of brain function and disease.
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.