Daniel Wagenaar’s Past Research

This page presents a survey of my work before I started studying crossmodal sensory integration. For current work, please look at the lab’s Research page.

Motor pattern generation in the medicinal leech

In the lab of Bill Kristan at UCSD, I combined electrophysiology and voltage-sensitive dye imaging to study the neuronal basis of motor behavior associated with mating in the medicinal leech Hirudo verbana. This became 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 evoked behavior that looks just like motor behaviors observed during mating. The evoked behavior was very robust, and persists even in the isolated nerve cord. Thus, conopressin offered 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 possibility 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. Persistence 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, 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.

Particles in string theory

As an undergraduate at the University of Amsterdam, I studied theoretical physics, and wrote a master’s thesis on Particles in String Theory under supervision of Herman Verlinde and Robert Dijkgraaf.