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Urban, Nathan


Dr. Frederick A. Schwertz Distinguished Professor of Life Sciences

Head, Department of Biological Sciences 

Carnegie Mellon University

Phone: (412) 268-5122
Fax: (412) 268-8423

Individual Website:

Ph.D., University of Pittsburgh

Research Interests

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.

Recent Publications

  • Padmanabhan, K. and Urban, NN (2010) Intrinsic biophysical diversity decorrelates neuronal firing while increasing information content. Nature Neuroscience. 2010 Oct;13(10):1276-82.
  • Giridhar S, Doiron B, Urban NN. (2011) Timescale-dependent shaping of correlation by olfactory bulb lateral inhibition. Proc Natl Acad Sci U S A. 2011 Apr 5;108(14):5843-8. Epub 2011 Mar 21.
  • Litwin-Kumar A, Oswald, AM, Urban, N and Doiron, B. (2011) Balanced synaptic input shapes the correlation between neural spike trains. PloS Computational Biology. 2011 Dec;7(12):e1002305.
  • Oswald AM, Urban NN. (2012) Interactions between behaviorally relevant rhythms and synaptic plasticity alter coding in the piriform cortex. J Neuroscience. May 2;32(18):6092-104.
  • Burton SD, Ermentrout GB, Urban NN (2012) Intrinsic heterogeneity in oscillatory dynamics limits correlation-induced neural synchronization. J. Neurophysiol. 2012 Oct;108(8):2115-33. PMID: 22815400
  • Urban N, Tripathy S. (2012) Circuits drive cell diversity. Nature. Aug 16;488(7411):289-90.
  • Tripathy SJ, Padmanabhan K, Gerkin RC Urban NN. (2013) Intermediate intrinsic diversity enhances neural population coding. In Press PNAS.
  • Castro JB, Hovis KR, Urban NN: Recurrent dendrodendritic inhibition of accessory olfactory bulb mitral cells requires activation of group I metabotropic glutamate receptors. J Neurosci 27(21): 5564-5671, 2007.
  • Galán RF, Ermentrout GB, Urban NN: Reliability and stochastic synchronization in type I vs. type II neuraloscillators. Neurocomputing 70: 2102-2106, 2007.
  • Galán RF, Fourcaud-Trocme N, Ermentrout GB, Urban NN: Correlation-induced synchronization of oscillations in olfactory bulb neurons. J Neurosci 26(14): 3646-3655, 2006
  • Kapoor V, Urban NN: Glomerulus-specific, long latency activity in the olfactory bulb granule-cell network. J Neurosci 26(45): 11709-11719, 2006.
  • Castro J, Urban NN: Tuft calcium spikes in accessory olfactory bulb mitral cells. J Neurosci 25(20): 5024-5028, 2005.
  • Fernández GR, Ermentrout GB, Urban NN: Efficient estimation of phase-resetting curves in real neurons and its significance for neural-network modeling. Phys Rev Lett 94(15): 158101, 2005.
  • Schoppa NS, Urban NN: Dendritic processing within olfactory bulb circuits. Trends Neurosci 26(9): 501-506, 2003.