Stephen V. David | |
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I am a postdoctoral fellow with Shihab Shamma in the Neural Systems Laboratory at the University of Maryland. In May 2004, I completed a Ph.D. in Bioengineering at the University of California, Berkeley (joint program with UCSF), studying vision and attention with Jack Gallant. My research interests are (1) how animals integrate sensory inputs into a coherent representation of the world and (2) how that representation changes to meet changing behavioral context. The utility of a particular sensory stimulus (and the features of the stimulus that are useful) depends on the behavioral demands facing an animal. It is well-known that attention can modulate the activity of sensory neurons. However, less is known about how the observed modulation actually facilitates behavior. As a post-doc, I am studying how the spectrotemporal tuning properties of neurons in auditory and frontal cortices are modulated by changes in attention. I am also interested in methods for effective comparison of models for sensory processing by neurons. With the continuous increase in available computational power, we have the ability to test and compare a huge variety of models. This new potential raises new issues: What is the best way to compare functional models of neurons? How should the large and diverse neurophysiological datasets be stored so that they can be available for testing new models? The Neural Prediction Challenge, a collaboration with Jack Gallant and Frederic Theunissen at UC Berkeley, is a database of single neuron recordings from auditory and visual systems using natural stimuli. Interested researchers can download the data and compare the performance of their model against other models fit with the same data. A related project, STRFpak, is a software package providing model estimation and validation tools that can be applied to any neurophysiological data set. Recent publications (including some pdfs that can be difficult to locate):
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