Postdoctoral Scholar, Lab of William B. Kristan, Jr
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Contact Information
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 In Sequoia NP, 2005 |
Motor pattern generation and decision making in the medicinal leech (current)
In the lab of Bill Kristan at UCSD, I am combining electrophysiology and voltage-sensitive dye imaging to study the neuronal basis of motor behavior associated with mating in the medicinal leech, Hirudo verbana. This has become possible thanks to the discovery of the remarkable effects of conopressin, a vasopressin/oxytocin analog isolated from the venom of the sea snail Conus imperialis. When injected into adult leeches, conopressin evokes behavior that looks just like motor behaviors observed during mating. The evoked behavior is very robust, and persists even in the isolated nerve cord. Thus, conopressin offers us a novel and very exciting opportunity to study the activity of the nervous system that drives mating, circumventing the unresolved challenge of electrophysiologically recording directly from leeches as they mate.
Development of activity patterns in cultured cortical neurons
As a graduate student at Caltech with Jerry Pine and Steve Potter (at Georgia Tech), I grew cultures of rat cortical neurons on multi-electrode arrays, and studied the networks they formed. We were interested in their spontaneous activity patterns, as well as in the possiblity of modifying those patterns using electrical stimulation. We found that their spontaneous activity was extremely rich, and consisted largely of network-spanning bursts of spikes. We hypothesized that these bursts were anomalous, since in healthy animals network-spanning bursts only occur during a brief developmental period. Persistance of bursting could be a result of lack of afferents into the culture, and may be related to epilepsy. By sprinkling in electrical stimulation through many electrodes, we found that we could quiet these bursts. Burst control persisted for as long as electrical stimuli were applied. Achieving a permanent change in network activity patterns through long-term plasticity turned out to be difficult, but initial results indicate that control of plasticity is facilitated by burst quieting.
Information geometry of artificial neural networks
After completing my degree in theoretical physics at the University of Amsterdam with a master’s thesis on string theory, I spent a year at the Math Department of King’s College, London, learning about information theory and neural networks. In particular, I studied information geometry, an elegant framework that describes information and probability theory in terms of Riemannian geometry (which is the mathematics underlying general relativity), and how it applies to neural networks.
Preprints for papers and abstracts for all other works may be downloaded from the complete list of my publications. For many, links into the PubMed database give access to the full text of articles.
Information on MEABench and other programs I wrote in the course of research and otherwise is available on a separate page.
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