Training the next generation of interdisciplinary neuroscientists
The Stanford Interdisciplinary Graduate Fellowship (SIGF) is a competitive, university-wide program that awards three-year fellowships to outstanding doctoral students engaged in interdisciplinary research. Three independent institutes, Bio-X, Wu Tsai Neurosciences Institute, and Sarafan ChEM-H award these graduate fellowships in the biosciences.
The Wu Tsai Neurosciences Institute partners with the Vice Provost for Graduate Education and Stanford BioX to award Stanford Interdisciplinary Graduate Fellowships (SIGFs) in the area of neuroscience. We are grateful to Bio-X and the Bio-X Leadership Council for incorporating the fellowships affiliated with the Institute into their application process.
2024 SIGF Application
All applications submitted through the portal will be considered for fellowships associated with Stanford Bio-X, Wu Tsai Neurosciences Institute, and Sarafan ChEM-H. Students with research especially aligned with the scientific mission of the Wu Tsai Neurosciences Institute graduate fellowships should select "Wu Tsai Neurosciences Institute Fellowship" on their application.
Funded SIGF projects
Uncovering the neurochemical basis of colonic water absorption
Constipation and diarrhea, caused by aberrant water absorption in the colon, impose substantial health burdens. The enteric nervous system (ENS) harbors a specialized circuit for water absorption, the secretomotor/vasodilator circuit, but its role in the proximal colon remains poorly understood.
Uncovering behavior-dependent entorhinal maps with state space models
The medial entorhinal cortex (MEC), the brain’s “inner GPS”, contains an internal map of external space. Rather than representing a static spatial map, however, MEC neurons can spontaneously switch between multiple maps (Low et al., 2021). In this project, we will investigate if spontaneous map switches reflect changes in an animal’s latent internal state.
Uncovering the roles of representational drift in the brain through the lens of dynamical systems and their practical implications in brain-computer interfaces
Understanding representational drift—the brain’s evolving representation of its environment—is pivotal to gaining insights into neural computation. Despite its significance, the study of representational drift has been constrained by the scarcity of suitable datasets and methods.
Engineering objective physiologic measures to characterize nonmotor aspects of Parkinson’s disease
Parkinson’s disease (PD) is a complex, heterogeneous neurodegenerative disorder whose prevalence is increasing rapidly. Not only do patients experience motor symptoms, but many experience debilitating nonmotor symptoms caused by peripheral degeneration in the autonomic nervous system, including atrophy of the vagus nerve, and the enteric nervous system.
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.
Optimizing computational modeling of traumatic brain injury with machine learning: biomechanics and beyond
Traumatic brain injury (TBI) has become a global health hazard. If undetected, the brain damage of TBI can accumulate, calling for better TBI modeling and warning systems. TBI modeling involves three stages: head impact kinematics, brain deformation, and injuries.
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.
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.
