Dynamical Mechanisms of Odor Processing in Olfactory Bulb Mitral Cells

Abstract

Rubin, Daniel B. and Thomas A. Cleland. Dynamical mechanisms of odor processing in olfactory bulb mitral cells. J Neurophysiol 96: 555-568, 2006. First published May 17, 2006 doi:10.1152/jn.00264.2006. In the olfactory system, the contribution of dynamical properties such as neuronal oscillations and spike synchronization to the representation of odor stimuli is a matter of substantial debate. While relatively simple computational models have sufficed to guide current research in large-scale network dynamics, less attention has been paid to modeling the membrane dynamics in bulbar neurons that may be equally essential to sensory processing. We here present a reduced, conductance-based compartmental model of olfactory bulb mitral cells that exhibits the complex dynamical properties observed in these neurons. Specifically, model neurons exhibit intrinsic subthreshold oscillations with voltage-dependent frequencies that shape the timing of stimulus-evoked action potentials. These oscillations rely on a persistent sodium conductance, an inactivating potassium conductance, and a calcium-dependent potassium conductance and are reset via inhibitory input such as that delivered by periglomerular cell shunt inhibition. Mitral cells fire bursts, or clusters, of spikes when continuously stimulated. Burst properties depend critically on multiple currents, but a progressive deinactivation of I A over the course of a burst is an important regulator of burst termination. Each of these complex properties exhibits appropriate dynamics and pharmacology as determined by electrophysiological studies. Additionally, we propose that a second, inconsistently observed form of infrathreshold bistability in mitral cells may derive from the activation of ATP-activated potassium currents responding to hypoxic conditions. We discuss the integration of these cellular properties in the larger context of olfactory bulb network operations.

Role of intraglomerular circuits in shaping temporally structured responses to naturalistic inhalation-driven sensory input to the olfactory bulb

“…These results suggest that, while MC intrinsic properties can generate some degree of temporal patterning in response to unpatterned synaptic inputs (Balu et al 2004;Rubin and Cleland 2006), the ORN-MC circuit by itself does not generate MC spiking patterns with a temporal structure matching those measured in vivo in response to natural odorant sampling. Rather, MC spiking responses roughly follow that of ORN synaptic input.…”

“…The intrinsic properties of these neuron types may also play an important role in shaping temporally-structured activity in the OB. For example, the intrinsic bursting of ET cells generates patterned excitation in the frequency range of natural sniffing behavior (Hayar et al 2004b), and the intrinsic properties of MCs appear to facilitate temporally precise and well-structured output in response to phasic inputs, such as those driven by inhalation (Balu et al 2004;Desmaisons et al 1999;Heyward et al 2001;Margrie and Schaefer 2003;Rubin and Cleland 2006). With a few exceptions, however (Cang and Isaacson 2003;Fukunaga et al 2012;Phillips et al 2012;Schaefer et al 2006), these phenomena have been largely described in OB slice experiments, and thus the contribution of synaptic and intrinsic properties of glomerular neurons to the temporal sharpening of inhalation-driven ORN inputs in vivo, and in particular during natural odor sampling, remains unclear.…”

Relational representation in the olfactory system

“…In concert with related glomerular processing circuitry (22,36), this mechanism enables functional interpretation of observations that odors commonly evoke inhibitory responses in mitral cells and that increasing odorant concentrations do not monotonically increase spike rates in mitral cells (29), setting the stage for subsequent processing by the mitral-granule cell network (35,37). Moreover, behavioral assays confirm that normalized olfactory representations predict perceptual relationships across concentrations, whereas raw glomerular representations do not.…”

Distinct current modules shape cellular dynamics in model neurons

“…Researchers have proposed different approaches to develop simplified biophysical models (Stratford et al, 1989, Rall, 1990, Traub et al, 1991, Bush and Sejnowski, 1993, Pinsky and Rinzel, 1994, Destexhe, 2001, Traub et al, 2004, Hendrickson et al, 2011). These have been formalized and several hand-tuning to automated search algorithms have been proposed (Prinz et al, 2003, Rubin and Cleland, 2006, Hemond et al, 2008, Pospischil et al, 2008). Automated searches have also been used successfully in conjunction with large databases of model neurons to select parameter sets that replicate a range of neuronal properties (Prinz et al, 2003, Gunay et al, 2008), by typically varying the maximal conductance densities, and have helped enhance the model development process considerably.…”