Dynamic Neuroscience Statistics, Modeling, and Control /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Chen, Zhe (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Sarma, Sridevi V. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Θέματα: Biomedical engineering.; Signal processing.; Image processing.; Speech processing systems.; Bioinformatics.; Neurosciences.; Statistics .; Neural networks (Computer science) .; Biomedical Engineering and Bioengineering.; Signal, Image and Speech Processing.; Computational Biology/Bioinformatics.; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.; Mathematical Models of Cognitive Processes and Neural Networks.

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...

Πίνακας περιεχομένων:

• 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.