Funded Projects

Project's stimulation method may provide a powerful tool to reduce disability after a stroke, and the wearable form factor allows users to receive intensive therapy during their normal daily routine

Developing a protein-engineered nerve implant that mimics the biochemical and mechanical cues of native tissue in order to enhance the potential for neural regeneration following injury.

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.

We propose to design a lightweight, wearable system for integrated ultrasonic drug uncaging and fUS neuroimaging to noninvasively pharmacologically modulate a brain target and then image the resultant changes in neural activity without significant motion limitations.

We believe our research has the potential of generating transformative results for both neuroscience research and neurological applications, also offering strategies to manipulate key intracellular pathways to prevent gliosis in therapeutic neural implants.

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).

This team is developing wearable stimulation devices to improve limb function after stroke. The technology includes a tactile stimulation method, and the wireless, lightweight, and low-cost wearable computing devices to apply this stimulation.

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.

Sensorineural hearing loss is an increasingly prevalent condition that causes disability to over a third of US adults aged over 65. We are developing a breakthrough device to restore high-frequency hearing that preserves residual hearing through a reversible and minimally invasive approach.

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.

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.

We propose a new magnetic sensor that is sensitive to picoTesla-scale fields, a localized magnetic stimulator with small form-factor, and a seamless integration of both systems for applications in experimental and clinical neuroscience.