Chen, Zhe
Dynamic Neuroscience
1. Introduction
Zhe Chen, Sridevi V. Sarma
Part I. Statistics & Signal Processing
2. Characterizing Complex, Multi-Scale Neural Phenomena Using State-Space Models
Uri T. Eden, Loren M. Frank, Long Tao
3. Latent Variable Modeling of Neural Population Dynamics
Zhe Chen
4. What Can Trial-to-Trial Variability Tell Us? A Distribution-Based Approach to Spike Train Decoding in the Rat Hippocampus and Entorhinal Cortex
Michael J. Prerau, Uri T. Eden
5. Sparsity Meets Dynamics: Robust Solutions to Neuronal Identification and Inverse Problems
Behtash Babadi
6. Artifact Rejection for Concurrent TMS-EEG Data
Wei Wu, Corey Keller, Amit Etkin
Part II. Modeling and Control Theory
7. Characterizing Complex Human Behaviors and Neural Responses Using Dynamic Models
Sridevi V. Sarma, Pierre Sacré
8. Brain–Machine Interfaces
Maryam M. Shanechi
9. Control-Theoretic Approaches for Modeling, Analyzing, and Manipulating Neuronal (In)activity
ShiNung Ching
10. From Physiological Signals to Pulsatile Dynamics: A Sparse System Identification Approach
Rose T. Faghih
11. Neural Engine Hypothesis
Hideaki Shimazaki
12. Inferring Neuronal Network Mechanisms Underlying Anesthesia-Induced Oscillations Using Mathematical Models
Sujith Vijayan, Michelle McCarthy
Avainsanat: Engineering, Biomedical Engineering, Signal, Image and Speech Processing, Computational Biology/Bioinformatics, Neurosciences, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Mathematical Models of Cognitive Processes and Neural Networks
- Toimittaja
- Chen, Zhe
- Sarma, Sridevi V.
- Julkaisija
- Springer
- Julkaisuvuosi
- 2018
- Kieli
- en
- Painos
- 1
- Sivumäärä
- 21 sivua
- Kategoria
- Tekniikka, energia, liikenne
- Tiedostomuoto
- E-kirja
- eISBN (PDF)
- 9783319719764
- Painetun ISBN
- 978-3-319-71975-7