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Найдено 10 публикаций
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Статья
Morozova E., Zakharov D., Gutkin B. et al. PLoS Computational Biology. 2016. Vol. 12. No. 12. P. e1005233-1-e1005233-36.
Добавлено: 4 октября 2018
Статья
Morozova E. O., Zakharov D., Gutkin B. et al. PLoS Computational Biology. 2016.

The dynamics of neuronal excitability determine the neuron’s response to stimuli, its synchronization and resonance properties and, ultimately, the computations it performs in the brain. We investigated the dynamical mechanisms underlying the excitability type of dopamine (DA) neurons, using a conductance-based biophysical model, and its regulation by intrinsic and synaptic currents. Calibrating the model to reproduce low frequency tonic firing results in N-methyl-D-aspartate (NMDA) excitation balanced by γ-Aminobutyric acid (GABA)-mediated inhibition and leads to type I excitable behavior characterized by a continuous decrease in firing frequency in response to hyperpolarizing currents. Furthermore, we analyzed how excitability type of the DA neuron model is influenced by changes in the intrinsic current composition. A subthreshold sodium current is necessary for a continuous frequency decrease during application of a negative current, and the low-frequency “balanced” state during simultaneous activation of NMDA and GABA receptors. Blocking this current switches the neuron to type II characterized by the abrupt onset of repetitive firing. Enhancing the anomalous rectifier Ih current also switches the excitability to type II. Key characteristics of synaptic conductances that may be observed in vivo also change the type of excitability: a depolarized γ-Aminobutyric acid receptor (GABAR) reversal potential or co-activation of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) leads to an abrupt frequency drop to zero, which is typical for type II excitability. Coactivation of N-methyl-D-aspartate receptors (NMDARs) together with AMPARs and GABARs shifts the type I/II boundary toward more hyperpolarized GABAR reversal potentials. To better understand how altering each of the aforementioned currents leads to changes in excitability profile of DA neuron, we provide a thorough dynamical analysis. Collectively, these results imply that type I excitability in dopamine neurons might be important for low firing rates and fine-tuning basal dopamine levels, while switching excitability to type II during NMDAR and AMPAR activation may facilitate a transient increase in dopamine concentration, as type II neurons are more amenable to synchronization by mutual excitation.

Добавлено: 9 февраля 2017
Статья
Graupner M., Maex R., Gutkin B. PLoS Computational Biology. 2013. Vol. 9. No. 8.

Nicotine exerts its reinforcing action by stimulating nicotinic acetylcholine receptors (nAChRs) and boosting dopamine (DA) output from the ventral tegmental area (VTA). Recent data have led to a debate about the principal pathway of nicotine action: direct stimulation of the DAergic cells through nAChR activation, or disinhibition mediated through desensitization of nAChRs on GABAergic interneurons. We use a computational model of the VTA circuitry and nAChR function to shed light on this issue. Our model illustrates that the α4β2-containing nAChRs either on DA or GABA cells can mediate the acute effects of nicotine. We account for in vitro as well as in vivo data, and predict the conditions necessary for either direct stimulation or disinhibition to be at the origin of DA activity increases. We propose key experiments to disentangle the contribution of both mechanisms. We show that the rate of endogenous acetylcholine input crucially determines the evoked DA response for both mechanisms. Together our results delineate the mechanisms by which the VTA mediates the acute rewarding properties of nicotine and suggest an acetylcholine dependence hypothesis for nicotine reinforcement.

Добавлено: 23 декабря 2014
Статья
Svetlichnyy D., Imrichova H., Fiers M. et al. PLoS Computational Biology. 2015.
Добавлено: 22 октября 2018
Статья
Gutkin B., Roth A., Buchin A. et al. PLoS Computational Biology. 2016.
Добавлено: 13 октября 2016
Статья
Lobanov M., Roytberg M. A., Galzitskaya O. et al. PLoS Computational Biology. 2010. No. 6(10). P. 1-10.
Добавлено: 17 декабря 2010
Статья
Cazé R. D., Humphries M., Gutkin B. PLoS Computational Biology. 2013. Vol. 9. No. 2.
Добавлено: 10 марта 2015
Статья
Gutkin B., Chalk M., Masset P. et al. PLoS Computational Biology. 2017. Vol. 13. No. 6. P. 1-16.

In order to respond reliably to specific features of their environment, sensory neurons need to integrate multiple incoming noisy signals. Crucially, they also need to compete for the interpretation of those signals with other neurons representing similar features. The form that this competition should take depends critically on the noise corrupting these signals. In this study we show that for the type of noise commonly observed in sensory systems, whose variance scales with the mean signal, sensory neurons should selectively divide their input signals by their predictions, suppressing ambiguous cues while amplifying others. Any change in the stimulus context alters which inputs are suppressed, leading to a deep dynamic reshaping of neural receptive fields going far beyond simple surround suppression. Paradoxically, these highly variable receptive fields go alongside and are in fact required for an invariant representation of external sensory features. In addition to offering a normative account of context-dependent changes in sensory responses, perceptual inference in the presence of signal-dependent noise accounts for ubiquitous features of sensory neurons such as divisive normalization, gain control and contrast dependent temporal dynamics.

Добавлено: 17 октября 2017
Статья
Schaworonkow N., Nikulin V. PLoS Computational Biology. 2019. No. 15. P. 1-22.
Добавлено: 25 октября 2019
Статья
Shugay M., Bagaev D. V., Turchaninova M. A. et al. PLoS Computational Biology. 2015. Vol. 11. No. 11. P. e1004503.
Добавлено: 17 февраля 2016