Dynamic Neuroscience Statistics, Modeling, and Control
This book shows how to develop efficient quantitative methods to characterize neural data and extra information that reveals underlying dynamics and neurophysiological mechanisms. Written by active experts in the field, it contains an exchange of innovative ideas among researchers at both computatio...
| Other Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2018, 2018
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| Edition: | 1st ed. 2018 |
| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Introduction
- Part I Statistics & Signal Processing
- Characterizing Complex, Multi-scale Neural Phenomena Using State-Space Models
- Latent Variable Modeling of Neural Population Dynamics
- What Can Trial-to-Trial Variability Tell Us? A Distribution-Based Approach to Spike Train Decoding in the Rat Hippocampus and Entorhinal Cortex
- Sparsity Meets Dynamics: Robust Solutions to Neuronal Identification and Inverse Problems
- Artifact Rejection for Concurrent TMS-EEG Data
- Part II Modeling & Control Theory
- Characterizing Complex Human Behaviors and Neural Responses Using Dynamic Models
- Brain-Machine Interfaces
- Control-theoretic Approaches for Modeling, Analyzing and Manipulating Neuronal (In)activity
- From Physiological Signals to Pulsatile Dynamics: A Sparse System Identification Approach
- Neural Engine Hypothesis
- Inferring Neuronal Network Mechanisms Underlying Anesthesia induced Oscillations Using Mathematical Models
- Epilogue