Josic / Rubin / Matias Coherent Behavior in Neuronal Networks

1. Auflage 2009
ISBN: 978-1-4419-0389-1
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 3, 304 Seiten

Reihe: Springer Series in Computational Neuroscience

Recent experimental research advances have led to increasingly detailed descriptions of how networks of interacting neurons process information. With these developments, it has become clear that dynamic network behaviors underlie information processing, and that the observed activity patterns cannot be fully explained by simple concepts such as synchrony and phase locking. These new insights raise significant challenges and offer exciting opportunities for experimental and theoretical neuroscientists. Coherent Behavior in Neuronal Networks features a review of recent research in this area from some of the world's foremost experts on systems neuroscience. The book presents novel methodologies and interdisciplinary perspectives, and will serve as an invaluable resource to the research community. Highlights include the results of interdisciplinary collaborations and approaches as well as topics, such as the interplay of intrinsic and synaptic dynamics in producing coherent neuronal network activity and the roles of globally coherent rhythms and oscillations in the coordination of distributed processing, that are of significant research interest but have been underrepresented in the review literature. With its cutting-edge mathematical, statistical, and computational techniques, this volume will be of interest to all researchers and students in the field of systems neuroscience.

Autoren/Hrsg.

Josic, Kresimir

Rubin, Jonathan

Matias, Manuel

Romo, Ranulfo

Josic, Kre#imir

Inhaltsverzeichnis

1;Coherent Behaviorin Neuronal Networks;5
2;On the Dynamics of Synaptic Inputs During Ongoing Activity in the Cortex;12
2.1;Introduction;12
2.2;Synchrony in Spontaneous Activity;15
2.3;Excitation and Inhibition During Spontaneous Activity;16
2.4;Repeating Patterns in the Spontaneous Subthreshold Membrane Potential Fluctuations of Cortical Neurons;19
2.5;Conclusions;24
2.6;References;25
3;Timing Excitation and Inhibition in the Cortical Network;28
3.1;Excitation and Inhibition During Cortical Up and Down States;28
3.2;Experimental Procedures and Detection of Synaptic Events;32
3.2.1;Intracellular and Extracellular Recordings In Vitro and In Vivo;32
3.2.2;Data Analysis;33
3.2.3;A Short Discussion on the Method;33
3.3;Experimental Results;35
3.3.1;Excitatory and Inhibitory Events During Risetime of Up States In Vitro;37
3.3.2;Excitatory and Inhibitory Events During the End of Up States In Vitro;39
3.3.3;Excitatory and Inhibitory Events During Risetime of Up States In Vivo;41
3.3.4;Excitatory and Inhibitory Events During the End of Up States In Vivo;42
3.4;Excitation and Inhibition in Up and Down states Generated in a Cortical Model;43
3.4.1;Modeling the Cortex;44
3.4.2;Excitatory and Inhibitory Events During Up States In Computo;46
3.5;Timing of Excitation and Inhibition in Cortical Activity;49
3.6;Appendix;53
3.7;Intracellular and Population Recordings In Vitro and In Vivo;53
3.7.1;In Vitro Recordings;53
3.7.2;In Vivo Recordings;53
3.7.3;Recordings and Stimulation;53
3.8;Data Analysis and Detection of Synaptic Events;54
3.9;References;54
4;Finding Repeating Synaptic Inputs in a Single Neocortical Neuron;58
4.1;Introduction;58
4.2;Repeat Detection;59
4.3;Significance Testing;63
4.4;Implanted, Artificial Repeats;65
4.5;An Improved Repeat Detector;65
4.6;Recording Conditions and Effects on Synaptic Repeat Detection;68
4.7;References;70
5;Reverberatory Activity in Neuronal Networks;72
5.1;Background;72
5.2;Reverberatory Activity in Cultured Neuronal Networks;74
5.3;Biophysical Mechanisms Underlying Persistent In Vitro Reverberation;77
5.3.1;Intrinsic Bistability vs. Recurrent Excitation;77
5.3.2;Asynchronous Synaptic Transmission;78
5.3.3;Short-Term Synaptic Dynamics;81
5.4;Summary and Outlook;82
5.5;References;83
6;Gap Junctions and Emergent Rhythms;87
6.1;Introduction;87
6.2;The Absolute Integrate-and-Fire Model;89
6.2.1;Spike Adaptation;90
6.2.2;Phase Response Curve;93
6.3;Gap-Junction Coupling;94
6.3.1;Existence of the Asynchronous State;95
6.3.2;Stability of the Asynchronous State;96
6.4;Discussion;101
6.5;References;102
7;The Feed-Forward Chain as a Filter-Amplifier Motif;105
7.1;Introduction;105
7.2;Synchrony-Breaking Hopf Bifurcations;108
7.3;Periodic Forcing of Feed-Forward Chains;110
7.3.1;Simulations;111
7.3.2;Experiments;114
7.4;Periodic Forcing near Hopf Bifurcation;115
7.4.1;Simulations;116
7.4.2;Asymmetry and Multiplicity in Response Curve;116
7.4.3;Scalings of Solution Amplitudes;121
7.4.4;Q-Factor;125
7.5;Cochlear Modeling;127
7.5.1;Hopf Models of the Auditory System;127
7.5.2;Two-Frequency Forcing;128
7.6;References;129
8;Gain Modulation as a Mechanism for Switching Reference Frames, Tasks, and Targets;131
8.1;The Problem of Behavioral Flexibility;131
8.2;What is Gain Modulation?;132
8.2.1;Experimental Evidence for Gain Modulation;134
8.2.1.1;Modulation by Proprioceptive Information;134
8.2.1.2;Attentional Modulation;135
8.2.1.3;Nonlinear Interactions between Multiple Stimuli;135
8.2.1.4;Context- and Task-Dependent Modulation;136
8.3;Computations Based on Gain Modulation;137
8.3.1;Coordinate Transformations;138
8.3.2;Arbitrary Sensory-Motor Remapping;142
8.3.2.1;How the Contextual Switch Works;143
8.3.3;Switching as a Fundamental Operation;145
8.4;Flexible Responses to Complex Stimuli;148
8.5;References;150
9;Far in Space and Yet in Synchrony: Neuronal Mechanisms for Zero-Lag Long-Range Synchronization;153
9.1;Introduction;153
9.2;How can Zero-Lag Long-Range Synchrony Emerge Despite of Conduction Delays?;155
9.3;Zero-Lag Long-Range Neuronal Synchrony via Dynamical Relaying;158
9.3.1;Illustration of Dynamical Relaying in a Module of Three HH Cells;158
9.3.2;Effect of a Broad Distribution of Conduction Delays;161
9.3.3;Dynamical Relaying in Large-Scale Neuronal Networks;163
9.4;General Discussion, Conclusions and Perspectives;167
9.5;Methods;170
9.5.1;Models;170
9.5.2;Simulations;173
9.5.3;Data Analysis;173
9.6;References;174
10;Characterizing Oscillatory Cortical Networks with Granger Causality;178
10.1;Introduction;178
10.2;Granger Causality Analysis;179
10.3;Estimation of Autoregressive Models;184
10.4;Numerical Simulations;186
10.5;Laminar Organization of the Cortical Alpha Rhythm;190
10.6;The Choice of Neural Signals for Neuronal Interaction Analysis;195
10.7;Summary;197
10.8;References;197
11;Neurophysiology of Interceptive Behavior in the Primate: Encoding and Decoding Target Parameters in the Parietofrontal System;199
11.1;Introduction;199
11.2;Behavioral Aspects of an Interceptive Action;200
11.3;Visual Motion Processing;201
11.4;The Interception Task;202
11.5;Sensorimotor Processing During the Interception of Circularly Moving Targets;202
11.6;Encoding of Angular Position and Time-to-Contact During the Interception Task;204
11.7;Decoding of Angular Position and Tau During Interception of Circularly Moving Targets;208
11.8;Concluding Remarks;212
11.9;References;213
12;Noise Correlations and Information Encoding and Decoding;215
12.1;Introduction;215
12.2;Defining Noise Correlations;216
12.3;Theoretical Studies: Noise Correlations and Information Encoding;218
12.4;Theoretical Studies: Noise Correlations and Information Decoding;220
12.5;Empirical Studies: Noise Correlations and Information Encoding and Decoding;220
12.6;Theoretical Analysis of the Effects of Correlations on Encoding and Decoding in Pairs;221
12.7;Empirical Validation;224
12.8;Effects of Noise Correlations on Information Encoding and Decoding;229
12.9;Population Effects of Noise Correlations;230
12.10;Conclusion;235
12.11;References;235
13;Stochastic Synchrony in the Olfactory Bulb;1
13.1;Basic Circuitry of the Olfactory Bulb Mediates Recurrent and Lateral Inhibition;238
13.2;Slow Kinetics of Lateral Inhibition are Incompatible with Synchronization of Fast Oscillations;238
13.3;Gamma Oscillations are Intrinsic to Olfactory Bulb and to Mitral Cells;239
13.4;Mitral Cells are Oscillators with a Preferred Frequency of 40Hz;239
13.5;Noise-Induced Oscillatory Synchrony;240
13.6;Stochastic Synchrony;241
13.7;Phase Reduction and Lyapunov Exponents;243
13.8;Noise Color and Reliability;246
13.9;Input/Output Correlations;249
13.10;Summary;250
13.11;References;250
14;Stochastic Neural Dynamics as a Principle of Perception;254
14.1;Introduction;254
14.2;Brain Dynamics: From Spiking Neurons to Reduced Rate-Models;255
14.3;Perceptual Detection and Stochastic Dynamics;258
14.3.1;Neurophysiology;258
14.3.2;A Computational Model of Probabilistic Detection;263
14.4;References;268
15;Large-Scale Computational Modeling of the Primary Visual Cortex;270
15.1;Introduction;270
15.2;Physiological Background;275
15.3;The Large-Scale Computational Model;278
15.4;Dynamics of the Primary Visual Cortex;281
15.4.1;Patterns of Spontaneous Cortical Activity;282
15.4.2;Line-Motion Illusion;288
15.4.3;Orientation Tuning;293
15.5;Discussion;297
15.6;References;298
16;Index;304