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Neuronal Noise [electronic resource] /by Alain Destexhe, Michelle Rudolph-Lilith.

by Destexhe, Alain [author.]; Rudolph-Lilith, Michelle [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Springer Series in Computational Neuroscience: 8Publisher: Boston, MA : Springer US, 2012.Description: XVIII, 458p. 203 illus., 1 illus. in color. online resource.ISBN: 9780387790206.Subject(s): Medicine | Neurosciences | Neurobiology | Biomedicine | Neurosciences | NeurobiologyDDC classification: 612.8 Online resources: Click here to access online
Contents:
1 Introduction -- 2 Basics -- 3 Synaptic noise -- 4 Models of synaptic noise -- 5 Integrative properties in the presence of noise6 Recreating synaptic noise using dynamic-clamp -- 7 The mathematics of synaptic noise -- 8 Analyzing synaptic noise -- 9 Case studies -- 10 Conclusions and perspectives A Numerical integration of stochastic differential equations -- B Distributed Generator Algorithm -- C The Fokker-Planck formalism -- D The RT-NEURON interface for dynamic-clamp -- References -- Index.
In: Springer eBooksSummary: Neuronal Noise combines experimental, theoretical and computational results to show how noise is inherent to neuronal activity, and how noise can be important for neuronal computations.  The book covers many aspects of noise in neurons, with an emphasis on the largest source of noise: synaptic noise. It provides students and young researchers with an overview of the important methods and concepts that have emerged from research in this area. It also provides the specialist with a summary of the large body of sometimes contrasting experimental data, and different theories proposed to explore the computational power that various forms of "noise" can confer to neurons.
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1 Introduction -- 2 Basics -- 3 Synaptic noise -- 4 Models of synaptic noise -- 5 Integrative properties in the presence of noise6 Recreating synaptic noise using dynamic-clamp -- 7 The mathematics of synaptic noise -- 8 Analyzing synaptic noise -- 9 Case studies -- 10 Conclusions and perspectives A Numerical integration of stochastic differential equations -- B Distributed Generator Algorithm -- C The Fokker-Planck formalism -- D The RT-NEURON interface for dynamic-clamp -- References -- Index.

Neuronal Noise combines experimental, theoretical and computational results to show how noise is inherent to neuronal activity, and how noise can be important for neuronal computations.  The book covers many aspects of noise in neurons, with an emphasis on the largest source of noise: synaptic noise. It provides students and young researchers with an overview of the important methods and concepts that have emerged from research in this area. It also provides the specialist with a summary of the large body of sometimes contrasting experimental data, and different theories proposed to explore the computational power that various forms of "noise" can confer to neurons.

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