Funded Projects

Millions of people are blind, yet we still don’t have the technology to satisfactorily restore vision. I aim to create a prosthetic device to do so. This device can be implanted in the eyes of a blind patient, resting on a tissue layer called the retina.

Brain-computer interfaces (BCIs) are systems that enable using neural activity to control and interact with external devices. For people who lose the ability to move or speak due to injury or disease, BCIs provide a potential avenue to restore this loss of function.

Understanding the brain requires understanding how the neurons that constitute it perform computations, and how those computations relate to human behavior.

Synaptic junctions linking individual neurons constitute the fundamental building blocks of our brain. Understanding their inner working is crucial to unravel the mechanisms by which our brain processes information. However, imaging structures at a relevant sub-synaptic level is challenging and has often hampered advances in neuroscience.

Spinal cord injury (SCI) causes permanent damage to about 12,000 new patients in the US each year, primarily young adults. A common result of SCI is paralysis, and unfortunately, less than 1% of SCI patients have full neurological recovery by the time of hospital discharge.

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.

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.

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.

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.

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.

A top priority for people with paralysis is reach and grasp ability. Technologies such as robotic arm prostheses or electrically stimulating paralyzed muscles can meet this need. Existing methods rely on the remaining muscles, are unintuitive and require laborious sequences of simple commands. Reading out a patient’s desired movement directly from their brain could overcome these limitations.

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.

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.

Dr. Dante Muratore's goal is to design the next generation of neural interfaces that allow single-cell resolution when communicating with the nervous system. To achieve this, he has conceived a new way of reading information from the neural system.

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.

Communication between cells in the nervous system regulates the senses, memory, and information processing. Using electrical and biochemical sensors, such as patch clamps, voltage-sensitive dyes, and calcium-sensitive dyes, scientists have mapped with extraordinary detail the interactions of the nervous system.

Neural circuits can exhibit remarkable stability (e.g., when supporting long-term memory) as well as flexibility (e.g., when supporting rapid learning).

What are the principles that guide curiosity-based exploration? What is the neural circuitry that implements curiosity? How can insights related to the phenomenon of curiosity improve the education and capabilities of humans and artificially intelligent agents? To address these questions, Isaac Kauvar will take an interdisciplinary approach — positioned at the intersection of computer science, neuroscience, and psychology.

Multicellular organisms consist of numerous cell types with specialized biological functions. To understand such complex biological systems, genetic access to each cell type is needed for functional analysis and manipulations.

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.