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      A three-compartment conductance-based model of the rat olfactory receptor neuron

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      1 , , 1
      BMC Neuroscience
      BioMed Central
      Nineteenth Annual Computational Neuroscience Meeting: CNS*2010
      24–30 July 2010

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          Abstract

          Reduced compartmental models of single neurons, i.e. models with few Hodgkin-Huxley (HH) type compartments, provide an interesting balance between highly detailed, morphologically realistic models, and abstract, point-like models [1]. Simões-de-Souza and Roque [2] constructed a four-compartment model of the vertebrate olfactory receptor neuron (ORN), which was used in a large-scale simulation of the olfactory system [3]. Their model contains a biochemical compartment, which allows the study of the role of molecular pathways in odor representation. However, the use of this compartment slows down computer simulations of models with many receptor cells and makes the analysis of network behavior more complicated – because of the large number of variables and parameters. In this work, we present a simpler ORN model which does not include biochemical pathways and contains only three HH type compartments. Our model is different from [2] not only because it has one compartment less but also because its passive and active parameters have new values adjusted by a combination of “hand-fitting” with automatic fitting procedures so as to reproduce well experimental results for the rat [5]. The model was constructed in GENESIS [4]. Its three compartments kept the same names used in [2]: soma, dendrite and dendritic knob. The latter two are passive compartments while soma has four voltage-gated ionic currents: high-voltage-activated calcium current, fast voltage- and calcium-dependent potassium current, slow calcium-dependent potassium current, and delayed rectifier potassium current. These ionic currents were modeled according to the standard HH formalism with kinetic parameters adjusted as mentioned above. Input stimuli were constant current steps injected directly in dendritic knob to simulate experimental results [5]. Some simulation results are shown in Figure 1. Our reduced three-compartment model may allow faster and more efficient large-scale network simulations of the rat olfactory system. Figure 1 Some simulation results. A. Spike train generated by a 10 pA step current (to be compared with Figure 2B of [5]); B. Action potential (to be compared with the inset of Figure 2A of [5]).

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          Modeling single-neuron dynamics and computations: a balance of detail and abstraction.

          The fundamental building block of every nervous system is the single neuron. Understanding how these exquisitely structured elements operate is an integral part of the quest to solve the mysteries of the brain. Quantitative mathematical models have proved to be an indispensable tool in pursuing this goal. We review recent advances and examine how single-cell models on five levels of complexity, from black-box approaches to detailed compartmental simulations, address key questions about neural dynamics and signal processing.
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            Electrophysiological characterization of rat and mouse olfactory receptor neurons from an intact epithelial preparation.

            To understand the coding mechanisms underlying olfactory discrimination, it is necessary to characterize odor response properties of olfactory receptor neurons (ORNs). In contrast with rapid progress in molecular biology, there is little physiological data from ORNs in rodent. To facilitate acquisition of such data, we have developed an intact olfactory epithelial preparation from both rat and mouse. We have carried out initial studies of this preparation by monitoring odor responses by patch-clamping directly on the ORN dendritic knobs, a subcellular site very close to the locus of olfactory signal transduction. Our results show that rat and mouse ORNs have similar intrinsic membrane properties. Most cells fired spontaneously at a low frequency (f) and about one half fired repetitively in response to current (I) injection with a linear f/I relation. About one third of rat and mouse ORNs responded to a mixture of four odors in a dose-dependent manner and about 60% of them responded to IBMX, a potent inhibitor of phosphodiesterase. The results suggest that this intact preparation offers the advantage of approximating in vivo physiological conditions, while furnishing an opportunity to map single neuron responses in the epithelium in a spatially-defined manner, using electrophysiological or cell imaging methods.
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              A biophysical model of vertebrate olfactory epithelium and bulb exhibiting gap junction dependent odor-evoked spatiotemporal patterns of activity.

              This work describes a biophysical model of the initial stages of vertebrate olfactory system containing structures representing the olfactory epithelium and bulb. Its main novelty is the introduction of gap junctions connecting neurons both in the epithelium and bulb, and of biologically detailed dendrodendritic synapses between granule and mitral cells in the bulb. The model was used to simulate the effect of an odor presentation on the neural activity pattern in the epithelium and bulb. During the time for which an odor is presented with a constant concentration, there are spatiotemporal patterns in the epithelium and bulb generated by the couplings due to the gap junctions and/or dendrodendritic synapses. A study varying the strength of the gap junction coupling shows that the spatiotemporal patterns, both in the epithelium and bulb, are dependent of the coupling strength. It is also shown that the olfactory bulb's spatiotemporal pattern depends on the existence of the dendrodendritic connections between mitral and granule cells. If these spatiotemporal patterns really exist in the early processing stages of the olfactory system they may be used for odor coding and the gap junctions and dendrodendritic synapses might have a role on it.
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                Author and article information

                Conference
                BMC Neurosci
                BMC Neuroscience
                BioMed Central
                1471-2202
                2010
                20 July 2010
                : 11
                : Suppl 1
                : P130
                Affiliations
                [1 ]Departamento de Física e Matemática, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, 14040-901 Ribeirão Preto, SP, Brazil
                Article
                1471-2202-11-S1-P130
                10.1186/1471-2202-11-S1-P130
                3090833
                5c554152-4f3e-48bb-b567-81e0d3079823
                Copyright ©2010 Castro e Silva and Roque; licensee BioMed Central Ltd.
                Nineteenth Annual Computational Neuroscience Meeting: CNS*2010
                San Antonio, TX, USA
                24–30 July 2010
                History
                Categories
                Poster Presentation

                Neurosciences
                Neurosciences

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