Nathan N. Urban -Department of Biological Sciences - Carnegie Mellon University

Nathan N. Urban

Dr. Frederick A. Schwertz Distinguished Professor of Life Sciences

Interim Provost, Carnegie Mellon University

173 Mellon Institute
Department of Biological Sciences
Carnegie Mellon University
4400 Fifth Avenue
Pittsburgh, PA 15213
Office: Wh 607 / MI 173
Phone: 412-268-6684 / 412-268-5122
Fax: 412-268-7129


Ph.D., University of Pittsburgh
Postdoctoral Appointment, Max-Planck Institut fur medizinische Forschung


My long-term research interests center around understanding the physiological mechanisms underlying the functional and computational properties brain neuronal networks. That is, I want to understand the brain as an organ of biological computation and by extension to understand brain dysfunction as examples of failed computation. I believe that the mechanisms underlying computation are best uncovered by detailed studies of the physiological properties of the synapses, cells and circuits involved in the performance of a given task. In particular, I am interested in how neuronal diversity, synchrony, circuitry, dendritic integration and synaptic plasticity may allow small groups of neurons to perform complex and interesting functions. Understanding such computational properties of brain networks often requires the simultaneous acquisition of data from several cells within a network and/or from multiple locations within a single cell. Thus, I also am interested in the application and development of physiological and optical techniques that facilitate this sort of parallel data acquisition in vitro and in vivo.

One key aspect of this approach in the last few years has been gaining an understanding of the role of different sources of variability in neuronal computation. Students and postdocs in my lab have studied the ways in which apparently noisy signals can generate structured, synchronous activity across populations of neurons. This mechanism, which we have called stochastic synchrony, seems to provide a basis by which additional noise from synapses or even fro sensory stimuli can generate useful patterns of brain activity that may enhance sensitivity and selectivity in sensory systems.

Another area of interest has been to examine the degree to which cell to cell variability in intrinsic biophysical properties can be considered to be a beneficial "feature" of brain computation rather than a "bug" of biological imprecision.  In this vein we have combined computational and experimental approaches to characterize cell to cell variability and then to assess how this variability affects the ability of populations of neurons to represent information about stimuli. We have found that populations of neurons in the real "noisy" brain (in the sense of a brain in which cells are somewhat stochastic in their properties) can convey information about twice as efficiently as a "perfect" brtaion in which all neurons of a given type have identical properties.  

Finally we have recently been applying these approaches to study mouse models of autism.  In this case we are looking at trial to trial variability as a kind of "noise".  Human studies demonstrate that reliability of sensory evoked responses is impaired in autistic subjects.  Our goal is to identify sources of variability of neuronal responses in our mouse models and determine whether this kind of noise differs between control mice and those with autism-related mutations.


Wang W, Tripathy SJ, Padmanabhan K, Urban NN, Kass RE. An Empirical Model for Reliable Spiking Activity. Neural Comput. 2015 Jun 16:1-15.

Padmanabhan K, Urban NN. Disrupting information coding via block of 4-AP sensitive potassium channels. J Neurophysiol. 2014 Jun 3. pii: jn.00823.2013.

Tripathy SJ, Savitskaya J, Burton SD, Urban NN, Gerkin RC. NeuroElectro: a window to the world's neuron electrophysiology data. Front Neuroinform. 2014 Apr 29;8:40.

Burton SD, Urban NN.Greater excitability and firing irregularity of tufted cells underlies distinct afferent-evoked activity of olfactory bulb mitral and tufted cells . J Physiol. 2014 Mar 10.

Zhou P, Burton SD, Urban NN, Ermentrout GB (2013) Impact of neuronal heterogeneity on correlated colored noise-induced synchronization. Aug 21;7:113. doi: 10.3389/Front Comput Neurosci. ncom.2013.00113.

Gerkin, RC, Tripathy SJ, Urban NN.  (2013) Origins of correlated spiking in the mammalian olfactory bulb. In Press. Proc Natl Acad Sci U S A.

Tripathy SJ, Padmanabhan K, Gerkin RC, Urban NN (2013) Intermediate intrinsic diversity enhances neural population coding. Proc Natl Acad Sci U S A 110:8248-8253.

Urban N, Tripathy S. (2012) Circuits drive cell diversity. Nature. Aug 16;488(7411):289-90. doi: 10.1038/488289a.

Burton SD, Ermentrout GB, Urban NN.  (2012) Intrinsic heterogeneity in oscillatory dynamics limits correlation-induced neural synchronization.   J. Neurophysiol. 2012 Jul 18. [Epub ahead of print]  PMID: 22815400

Hovis KR,  Ramnath R, Dahlen JE, Romanova AL, LaRocca, G, Bier ME , Urban NN.  (2012) Activity Regulates Functional Connectivity from the Vomeronasal Organ to the Accessory Olfactory Bulb.  J. Neuroscience.  Jun 6;32(23):7907-16.

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.

Litwin-Kumar A, Oswald AM, Urban NN, Doiron B. Balanced synaptic input shapes the correlation between neural spike trains. PLoS Comput Biol. 2011 Dec;7(12):e1002305.

Dahlen JE, Jimenez DA, Gerkin RC, Urban NN. Morphological analysis of activity-reduced adult-born neurons in the mouse olfactory bulb. Front Neurosci. 2011 May 9;5:66.

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

Padmanabhan, K. and Urban NN.  (2010) Intrinsic biophysical diversity decorrelates neuronal firing while increasing information content.  Nature Neuroscience. 2010 Oct;13(10):1276-82.

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