Deterministic and Stochastic Dynamics of Multi-Variable Neuron Models: Resonance, Filtered Fluctuations and Sodium-Current Inactivation

 
9783659217746: Deterministic and Stochastic Dynamics of Multi-Variable Neuron Models: Resonance, Filtered Fluctuations and Sodium-Current Inactivation

Neurons are the basic elements of the networks that constitute the computational units of the brain. They dynamically transform input information into sequences of electrical pulses. Therefore it is crucial to understand this transformation and identify simple neuron models which accurately reproduce the known features of biological neurons. This book addresses three different features of neurons. We start by exploring the effect of subthreshold resonance on the response of a periodically forced neuron and show qualitatively distinct responses including mode locking and chaos. Then we will consider an experimentally verified model with realistic spike-generating mechanism and study the effect of filtered synaptic fluctuations on the firing-rate response of the neuron. Finally, a model is studied that incorporates threshold variability of neurons. We determine the modulation of the input-output properties of the model due to oscillatory inputs and in the presence of synaptic fluctuations. This book would be useful to understand the above properties of neurons and to learn some mathematical methods in analyzing deterministic and stochastic neuron models.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

About the Author :

Obtained her PhD in Mathematics in 2011 at the University of Warwick, UK. She is interested in Computational Neuroscience and is currently a postdoctoral researcher at University of Bern, Switzerland.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

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Description du livre LAP Lambert Academic Publishing, United States, 2012. Paperback. État : New. Aufl.. Language: English . Brand New Book ***** Print on Demand *****.Neurons are the basic elements of the networks that constitute the computational units of the brain. They dynamically transform input information into sequences of electrical pulses. Therefore it is crucial to understand this transformation and identify simple neuron models which accurately reproduce the known features of biological neurons. This book addresses three different features of neurons. We start by exploring the effect of subthreshold resonance on the response of a periodically forced neuron and show qualitatively distinct responses including mode locking and chaos. Then we will consider an experimentally verified model with realistic spike-generating mechanism and study the effect of filtered synaptic fluctuations on the firing-rate response of the neuron. Finally, a model is studied that incorporates threshold variability of neurons. We determine the modulation of the input-output properties of the model due to oscillatory inputs and in the presence of synaptic fluctuations. This book would be useful to understand the above properties of neurons and to learn some mathematical methods in analyzing deterministic and stochastic neuron models. N° de réf. du libraire AAV9783659217746

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Description du livre LAP LAMBERT Academic Publishing. Paperback. État : New. Paperback. 188 pages. Dimensions: 8.7in. x 5.9in. x 0.4in.Neurons are the basic elements of the networks that constitute the computational units of the brain. They dynamically transform input information into sequences of electrical pulses. Therefore it is crucial to understand this transformation and identify simple neuron models which accurately reproduce the known features of biological neurons. This book addresses three different features of neurons. We start by exploring the effect of subthreshold resonance on the response of a periodically forced neuron and show qualitatively distinct responses including mode locking and chaos. Then we will consider an experimentally verified model with realistic spike-generating mechanism and study the effect of filtered synaptic fluctuations on the firing-rate response of the neuron. Finally, a model is studied that incorporates threshold variability of neurons. We determine the modulation of the input-output properties of the model due to oscillatory inputs and in the presence of synaptic fluctuations. This book would be useful to understand the above properties of neurons and to learn some mathematical methods in analyzing deterministic and stochastic neuron models. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. N° de réf. du libraire 9783659217746

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