Neurotechnologies in Systems Neuroscience
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More About This Title Neurotechnologies in Systems Neuroscience
English
The key to developing a comprehensive theory of brain function is understanding the brain on a circuit or systems level. New technologies that have been developed over the last two decades now offer researchers unprecedented tools for studying the brain and have led to the rapid development of the field of Systems Neuroscience. Neurotechnologies in Systems Neuroscience: New Technologies for Understanding and Manipulating Brain Circuits and Computations is a timely collection of work that seeks to provide a valuable overview of technological advances and breakthroughs now being applied in research.
English
Part I:Understanding the Problem
Chapter 1: New Style Anatomy and Related Changes (D. Chklovski -- Janelia Farm)
Chapter 2: The Multplicity of Cell Types: Interneurons and All That (T. Klausberger -- Vienna)
Chapter 3: Current Problems in Circuit Neuroscience: Where We Need Technology (F. Battaglia -- Raboud University)
Chapter 4: Neurodynamics and Missing Data: Questions from a Theoretician's Point of View (P. Tiesinga -- Raboud University)
Part II: Technologies and Applications
Chapter 5: MEMS and IC Technologies for Electrophysiology (P. Ruther and O. Paul -- Freiburg University)
Chapter 6: Surfaceand Probe Arrays (M. Schuttler -- Freiburg University)
Chapter 7: Genetic Methods for Cell Connection Tagging (E. Calloway -- Salk Institute)
Chapter 8: Ospins and Other Molecules for Activity Manipulation (E. Bamberg --Frankfurt)
Chapter 9: Light Delivery to the Brain (C. Chestek and E. Yoon -- University of Michigan)
Chapter 10: Optical Imaging: 2P and Calcium Imaging (A Kampff -- Lisbon)
Chapter 11: Voltage Sensitive Dyes Imaging (A. Grinvald --Weizmann Institute)
Chapter 12: Real Time Data Analysis and Closed Loop Simulation (R. Butera -- Emory)
Chapter 13: Virtual Reality Set-ups for Small Animals (M. Mehta -- UCLA)
Chapter 14: The Future of Nanotechnology (P. Alivisatos -- UC Berkeley)
Part III: Neuroinformatics
Chapter 15: Organizing Data: Challenges and Methodologies (D. Kennedy -- Harvard)
Chapter 16: Role of Models in Bridging the Gap Across Experiments (I. Bojak -- University of Reading)
Chapter 17: Interpreting Single Units: LFP and Neuroimaging Data (A. Destexhe -- UNIC)
Chapter 18: Time Series Analysis of High Dimensional Data (M. Vinck -- Yale)
Chapter 19: Automatic Segmentation of Neurons and Synapses (G . Ascoli -- George Mason Univeristy)
Chapter 20: Describing and Modeling Connectivity Using Graph Theory and Bayesian Approaches (P. Tiesinga and M. van Gerven -- Raboud University)
Part IV: Manipulating Brain Activity in Humans
Chapter 21: High-end Electrophysiology at the Bedside (P. Boon -- Ghent University)
Chapter 22: Deep Brain Stimulation: New Methods for Parkinsons, Depression, Epilepsy (V. Coenen Freiburg)
Chapter 23: Neuro-ensemble based BCI (C. Mehring -- London)
Chapter 1: New Style Anatomy and Related Changes (D. Chklovski -- Janelia Farm)
Chapter 2: The Multplicity of Cell Types: Interneurons and All That (T. Klausberger -- Vienna)
Chapter 3: Current Problems in Circuit Neuroscience: Where We Need Technology (F. Battaglia -- Raboud University)
Chapter 4: Neurodynamics and Missing Data: Questions from a Theoretician's Point of View (P. Tiesinga -- Raboud University)
Part II: Technologies and Applications
Chapter 5: MEMS and IC Technologies for Electrophysiology (P. Ruther and O. Paul -- Freiburg University)
Chapter 6: Surfaceand Probe Arrays (M. Schuttler -- Freiburg University)
Chapter 7: Genetic Methods for Cell Connection Tagging (E. Calloway -- Salk Institute)
Chapter 8: Ospins and Other Molecules for Activity Manipulation (E. Bamberg --Frankfurt)
Chapter 9: Light Delivery to the Brain (C. Chestek and E. Yoon -- University of Michigan)
Chapter 10: Optical Imaging: 2P and Calcium Imaging (A Kampff -- Lisbon)
Chapter 11: Voltage Sensitive Dyes Imaging (A. Grinvald --Weizmann Institute)
Chapter 12: Real Time Data Analysis and Closed Loop Simulation (R. Butera -- Emory)
Chapter 13: Virtual Reality Set-ups for Small Animals (M. Mehta -- UCLA)
Chapter 14: The Future of Nanotechnology (P. Alivisatos -- UC Berkeley)
Part III: Neuroinformatics
Chapter 15: Organizing Data: Challenges and Methodologies (D. Kennedy -- Harvard)
Chapter 16: Role of Models in Bridging the Gap Across Experiments (I. Bojak -- University of Reading)
Chapter 17: Interpreting Single Units: LFP and Neuroimaging Data (A. Destexhe -- UNIC)
Chapter 18: Time Series Analysis of High Dimensional Data (M. Vinck -- Yale)
Chapter 19: Automatic Segmentation of Neurons and Synapses (G . Ascoli -- George Mason Univeristy)
Chapter 20: Describing and Modeling Connectivity Using Graph Theory and Bayesian Approaches (P. Tiesinga and M. van Gerven -- Raboud University)
Part IV: Manipulating Brain Activity in Humans
Chapter 21: High-end Electrophysiology at the Bedside (P. Boon -- Ghent University)
Chapter 22: Deep Brain Stimulation: New Methods for Parkinsons, Depression, Epilepsy (V. Coenen Freiburg)
Chapter 23: Neuro-ensemble based BCI (C. Mehring -- London)
