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Neurons as an Information-theoretic Engine

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Cover of 'Neurons as an Information-theoretic Engine'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Characterizing Complex, Multi-Scale Neural Phenomena Using State-Space Models
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    Chapter 3 Latent Variable Modeling of Neural Population Dynamics
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    Chapter 4 What Can Trial-to-Trial Variability Tell Us? A Distribution-Based Approach to Spike Train Decoding in the Rat Hippocampus and Entorhinal Cortex
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    Chapter 5 Sparsity Meets Dynamics: Robust Solutions to Neuronal Identification and Inverse Problems
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    Chapter 6 Artifact Rejection for Concurrent TMS-EEG Data
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    Chapter 7 Characterizing Complex Human Behaviors and Neural Responses Using Dynamic Models
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    Chapter 8 Brain–Machine Interfaces
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    Chapter 9 Control-Theoretic Approaches for Modeling, Analyzing, and Manipulating Neuronal (In)activity
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    Chapter 10 From Physiological Signals to Pulsatile Dynamics: A Sparse System Identification Approach
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    Chapter 11 Neural Engine Hypothesis
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    Chapter 12 Inferring Neuronal Network Mechanisms Underlying Anesthesia-Induced Oscillations Using Mathematical Models
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    Chapter 13 Retraction Note to: Artifact Rejection for Concurrent TMS-EEG Data
Attention for Chapter 7: Characterizing Complex Human Behaviors and Neural Responses Using Dynamic Models
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Citations

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Chapter title
Characterizing Complex Human Behaviors and Neural Responses Using Dynamic Models
Chapter number 7
Book title
Dynamic Neuroscience
Published by
Springer, Cham, January 2018
DOI 10.1007/978-3-319-71976-4_7
Book ISBNs
978-3-31-971975-7, 978-3-31-971976-4
Authors

Sridevi V. Sarma, Pierre Sacré

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 7%
Other 1 7%
Unknown 12 86%
Readers by discipline Count As %
Computer Science 1 7%
Unknown 13 93%

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