Literature DB >> 7688798

Exploring parameter space in detailed single neuron models: simulations of the mitral and granule cells of the olfactory bulb.

U S Bhalla1, J M Bower.   

Abstract

1. Detailed compartmental computer simulations of single mitral and granule cells of the vertebrate olfactory bulb were constructed using previously published geometric data. Electrophysiological properties were determined by comparing model output to previously published experimental data, mainly current-clamp recordings. 2. The passive electrical properties of each model were explored by comparing model output with intracellular potential data from hyperpolarizing current injection experiments. The results suggest that membrane resistivity in both cells is nonuniform, with somatas having a substantially lower resistivity than the dendrites. 3. The active properties of these cells were explored by incorporating active ion channels into modeled compartments. On the basis of evidence from the literature, the mitral cell model included six channel types: fast sodium, fast delayed rectifier (Kfast), slow delayed rectifier (K), transient outward potassium current (KA), voltage- and calcium-dependent potassium current (KCa), and L-type calcium current. The granule cell model included four channel types: rat brain sodium, K, KA, and the non-inactivating muscarinic potassium current (KM). Modeled channels were based on the Hodgkin-Huxley formalism. 4. Representative kinetics for each of the channel classes above were obtained from the literature. The experimentally unknown spatial distributions of each included channel were obtained by systematic parameter searches. These were conducted in two ways: large-scale simulation series, in which each parameter was varied in turn, and an adaptation of a multidimensional conjugate gradient method. In each case, the simulated results were compared wtih experimental data using a curve-matching function evaluating mean squared differences of several aspects of the simulated and experimental voltage waveforms. 5. Systematic parameter variations revealed a single distinct region of parameter space in which the mitral cell model best fit the data. This region of parameter space was also very robust to parameter variations. Specifically, optimum performance was obtained when calcium and slow K channels were concentrated in the glomeruli, with a lower density in the soma and proximal secondary dendrites. The distribution of sodium and fast potassium channels, on the other hand, was highest at the soma and axon, with a much lighter distribution throughout the secondary dendrites. The KA and KCa channels were also concentrated near the soma. 6. The parameter search of the granule cell model was much less restrained by experimental data. Several parameter regimes were found that gave a good match to the data.(ABSTRACT TRUNCATED AT 400 WORDS)

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Year:  1993        PMID: 7688798     DOI: 10.1152/jn.1993.69.6.1948

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  63 in total

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Authors:  M C Vanier; J M Bower
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Authors:  L E Moore; N Chub; J Tabak; M O'Donovan
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3.  The intrinsic electrophysiological characteristics of fly lobula plate tangential cells: III. Visual response properties.

Authors:  J Haag; A Vermeulen; A Borst
Journal:  J Comput Neurosci       Date:  1999 Nov-Dec       Impact factor: 1.621

4.  Parameter estimation methods for single neuron models.

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5.  Synaptic control of spiking in cerebellar Purkinje cells: dynamic current clamp based on model conductances.

Authors:  D Jaeger; J M Bower
Journal:  J Neurosci       Date:  1999-07-15       Impact factor: 6.167

6.  Morphometric modeling of olfactory circuits in the insect antennal lobe: I. Simulations of spiking local interneurons.

Authors:  T A Christensen; G D'Alessandro; J Lega; J G Hildebrand
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7.  Simulations of the role of the muscarinic-activated calcium-sensitive nonspecific cation current INCM in entorhinal neuronal activity during delayed matching tasks.

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8.  Multiple models to capture the variability in biological neurons and networks.

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9.  A database of computational models of a half-center oscillator for analyzing how neuronal parameters influence network activity.

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10.  Parameter estimation for bursting neural models.

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Journal:  J Comput Neurosci       Date:  2007-11-13       Impact factor: 1.621

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