Neural Systems Lab
The lab's research focuses on understanding the brain using computational models and simulations and applying this knowledge to the task of building intelligent robotic systems and brain-computer interfaces (BCIs). The lab utilizes data and techniques from a variety of fields, ranging from neuroscience and psychology to machine learning and statistics. Current efforts are directed at: (1) understanding probabilistic information processing and learning in the brain, (2) building biologically inspired robots that can learn through experience and imitation, and (3) developing interfaces for controlling computers and robots using brain- and muscle-related signals.
People
- Matthew Bryan
- Yanping Huang
- Stefan Martin
Publications
- A Hierarchical Architecture for Adaptive Brain-Computer Interfacing (2011)
- Predictive Coding (2011)
- A rational decision making framework for inhibitory control (2010)
- Decision Making under Uncertainty: A Neural Model based on Partially Observable Markov Decision Processes (2010)
Research Groups
Last changed Fri, 2012-11-16 11:28


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