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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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