The ability to flexibly and adaptively integrate information from a variety of sources is a fundamental feature of brain function, from higher cognition to sensory and motor processing. Even a simple behavior such as reaching to a target relies on the integration of multimodal sensory signals and, moreover, exhibits rapid adaptation in response to changes in these signals. The lab's research uses reaching and similar goal-directed movements as a model system for understanding these abilities and their underlying neural mechanisms.
To that end, the lab employs a combination of complementary approaches:
Psychophysics: With human psychophysics (or quantitative behavioral studies), identify behavioral phenomena that illustrate important features of sensorimotor processing. The goal is to find phenomena that are experimentally tractable for human and animals and are amenable to theoretical/computational modeling.
Modeling: Computational and theoretical models link understanding of brain and behavior. Two levels of modeling are used:
- Predictive models, typically cast in statistical or control-theoretical terms, provide intuition about why the behavior is the way it is.
- Networks models, can be found that yields an approximation to our behavioral models. Network models provide intuition and about the circuit-level mechanisms that underlie behavior. Furthermore, these models generate testable hypotheses about the dynamics of cortical networks and design of physiological experiments.
Physiology: The ultimate goal of is to discover cortical mechanisms for sensorimotor integration and learning. Physiological studies aimed at this goal are the current primary experimental focus of the lab. Three general approaches are being used:
- Large-scale cortical recordings: Record large neural populations using multiple 96-channel electrode arrays. Array recordings allow us to perform quantitative analyses at the level of the population responses in multiple brain areas.
- Manipulating cortical activity patterns: A major focus of the lab is developing techniques to measure changes in network dynamics and drive those changes by manipulating patterns of cortical activity. Two approaches are being developed in parallel: patterned electrical stimulation and patterned light stimulation in tissue expressing light-sensitive ion channels ("optogenetics").
- Human physiology: The Lab has access to a variety of human neurophysiological tools.
Philip Sabes earned undergraduate degrees in Physics and French from Washington University in St. Louis and in Mathematics from University of Cambridge before pursuing a PhD in Brain and Cognitive Sciences at Massachusetts Institute of Technology.
Dr. Sabes is the director of the UCSF Swartz Center for Theoretical Neurobiology and has been a Sloan Research Fellow, McKnight Scholar, and received the 2013 Annual Brain Computer Interface Research Award. Additionally, he holds the Jack D. and DeLoris Lange Endowed Chair in Cell Physiology.
Timothy Hanson, Engineer
Joseph Makin, Postdoc
Joseph O'Doherty, Postdoc
Azadeh Yazdan Shahmorad, Postdoc
Lindsey Presson, SRA/Lab Manager