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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 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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Citations

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Summary Dimensions citations
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Chapter title
What Can Trial-to-Trial Variability Tell Us? A Distribution-Based Approach to Spike Train Decoding in the Rat Hippocampus and Entorhinal Cortex
Chapter number 4
Book title
Dynamic Neuroscience
Published by
Springer, Cham, January 2018
DOI 10.1007/978-3-319-71976-4_4
Book ISBNs
978-3-31-971975-7, 978-3-31-971976-4
Authors

Michael J. Prerau, Uri T. Eden

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 22%
Student > Doctoral Student 2 22%
Other 1 11%
Lecturer 1 11%
Researcher 1 11%
Other 1 11%
Unknown 1 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 22%
Mathematics 1 11%
Biochemistry, Genetics and Molecular Biology 1 11%
Physics and Astronomy 1 11%
Neuroscience 1 11%
Other 0 0%
Unknown 3 33%

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