Ph.D., University of Pittsburgh
The overall goal of my research is to understand how and what the brain computes. My approach to this question is to pick systems in which the computations being performed are well understood and which are tractable from several levels of analysis. Currently, work in my lab focuses on understanding the physiological mechanisms underlying the functional and computational properties of brain neuronal networks, focusing on the olfactory system. In particular, I am interested in describing the detailed physiological properties of cells and synapses, and then constructing models that provide insight into how these physiological properties give rise to the functional circuits that transform and store the representations of information in the brain. My goal is to use these models to get at the underlying computations that these physiological systems can be seen as implementing. For modeling to be more than an exercise in fitting the data, these models must be sufficiently abstract to allow the essential properties to be understood and analyzed. Thus, I am very interested approaches that allow complex models to be reduced to their essential elements.
Lateral inhibition in the olfactory bulb. The working hypothesis of this work is that inhibitory interactions between nearby mitral cells can be seen as suppressing particular signals, allowing neurons to engage in a sort of local competition. This competition results in some signals being suppressed or filtered, while others pass through to the cortex. In particular, by combinations of paired whole cell recording and calcium imaging we have shown that the competitive inhibitory interactions between mitral cells are temporally specific (Kapoor and Urban, 2006) spatially/anatomically constrained (Egger and Urban, 2006) and activity-dependent (Arevian Kapoor and Urban submitted).
Neuronal synchronization and reliability. Neurons work more effectively when they are active together. Simultaneous firing, especially oscillatory firing, is a common feature of brain activity in many areas and across many species. We are interested in uncovering computational and biophysical mechanisms of such synchronization. This work involves use of a combination of computational and physiological approaches to determine which aspects of neuronal dynamics, synaptic properties and anatomical connectivity are critical for the generation of synchronized activity in large networks of neurons. This work has led us to develop methods that, through a combination of experiment and analysis, allow essential features of neuronal dynamics to be determined for real neurons. For example, the phase resetting curve (PRC) is a mathematical object that describes the response of a repetitively firing neuron (or of any oscillator) to stimuli. The PRC is very useful in determining whether a group of neurons, with a specified connectivity, will synchronize. We have developed methods to determine the phase resetting curve for real neurons and we are working on understanding how the biophysical properties of particular cells are related to their phase resetting curves.
Dendritic computation in the accessory olfactory system. In the accessory olfactory system my work has focused on understanding how the accessory olfactory bulb neurons maintain high levels of both sensitivity and selectivity in their response properties. Our working hypothesis is that the response of cells in the accessory olfactory bulb is influenced by local hotspots of activity in their dendritic trees. These local hotspots of activity allow input to be integrated in a highly non-linear fashion and thus to respond with high fidelity to low concentration stimuli. If this functional description is correct, it would represent the best known case in which a hypothesis about the connection between dendritic excitability and function at the level of whole animals could be tested.