Nathaniel N. Urban

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Mitral and tufted cells, the two classes of principal neurons in the mammalian main olfactory bulb, exhibit morphological differences but remain widely viewed as functionally equivalent. Results from several recent studies, however, suggest that these two cell classes may encode complementary olfactory information in their distinct patterns of(More)
Neural reliability and stochastic synchronization are remarkable features of real neurons with important consequences for neural computation. Both phenomena are general properties of any device with a resetting threshold, as neurons are. However, certain characteristics of the single neuron dynamics can notably improve neural reliability and stochastic(More)
Neural-network dynamics frequently organize in assemblies of synchronized neurons that are thought to encode and store sensory information. We have investigated the mechanisms leading to the emergence of these neural assemblies with models of coupled oscillators. In particular, we used experimentally estimated phase-resetting curves (PRC) of real neurons(More)
Mitral/tufted (M/T) cells of the main olfactory bulb transmit odorant information to higher brain structures. The relative timing of action potentials across M/T cells has been proposed to encode this information and to be critical for the activation of downstream neurons. Using ensemble recordings from the mouse olfactory bulb in vivo, we measured how(More)
Splitting sensory information into parallel pathways is a common strategy in sensory systems. Yet, how circuits in these parallel pathways are composed to maintain or even enhance the encoding of specific stimulus features is poorly understood. Here, we have investigated the parallel pathways formed by mitral and tufted cells of the olfactory system in mice(More)
Understanding a neuron's transfer function, which relates a neuron's inputs to its outputs, is essential for understanding the computational role of single neurons. Recently, statistical models, based on point processes and using generalized linear model (GLM) technology, have been widely applied to predict dynamic neuronal transfer functions. However, the(More)
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