Current technology allows the simultaneous recording from hundreds of cells in the brains of awake behaving animals. Using this information to understand how the brain represents and processes complex information is a fundamental scientific challenge with large practical benefits to human health. In particular this project will develop technologies for neural prostheses which promise a new generation of therapies for the severely disabled to allow them to regain the ability to interact with the world. To that end, the project has three specific aims: 1) New probabilistic models of the neural code will be developed that exploit machine learning methods (Gibbs learning and boosting) and high performance computing resources. These models will represent the high dimensional probabilistic relationship between multiple behavioral variables and the firing activity of a population of neurons. 2) Neural decoding methods will be developed that model the uncertainty in neural recordings to make sound inferences that can drive neural prostheses. As part of this effort new probabilistic spike sorting algorithms will be developed and tested. 3) Adaptation of cells in the brain will be studied using the statistical models developed here and an understanding of this adaptation will be used to design new algorithms for prosthetic applications. These algorithms will themselves be adaptive in a way that optimizes prosthetic control in the face of a changing neural code. To learn such models from vast amounts of neural data, a new class of mathematical and computational tools is required. An interdisciplinary team including computer scientists, applied mathematicians, and neuroscientists will work in close concert to exploit existing infrastructure and experience with neural prostheses to address fundamental problems necessary for the practical application of these devices in humans. The coupling between the prosthesis application and basic research on neural coding provides a tight cycle of hypothesis, development, testing, and validation that will impact public health.
| Wood, Frank; Black, Michael J (2008) A nonparametric Bayesian alternative to spike sorting. J Neurosci Methods 173:1-12 |
| Wood, Frank; Goldwater, Sharon; Black, Michael J (2006) A non-parametric Bayesian approach to spike sorting. Conf Proc IEEE Eng Med Biol Soc 1:1165-8 |
