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1.

図書
図書
1. Theoretical neuroscience : computational and mathematical modeling of neural systems (: hc ; : pbk)
Peter Dayan and L.F. Abbott
出版情報: Cambridge, Mass. ; London : MIT Press, c2001  xv, 460 p. ; 26 cm
シリーズ名: Computational neuroscience
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目次情報: 続きを見る
Preface
Neural Encoding and Decoding / I：
Neural Encoding I: Firing Rates and Spike Statistics / 1：
Introduction / 1.1：
Spike Trains and Firing Rates / 1.2：
What Makes a Neuron Fire? / 1.3：
Spike-Train Statistics / 1.4：
The Neural Code / 1.5：
Chapter Summary / 1.6：
Appendices / 1.7：
Annotated Bibliography / 1.8：
Neural Encoding II: Reverse Correlation and Visual Receptive Fields / 2：
Estimating Firing Rates / 2.1：
Introduction to the Early Visual System / 2.3：
Reverse-Correlation Methods: Simple Cells / 2.4：
Static Nonlinearities: Complex Cells / 2.5：
Receptive Fields in the Retina and LGN / 2.6：
Constructing V1 Receptive Fields / 2.7：
Neural Decoding / 2.8：
Encoding and Decoding / 3.1：
Discrimination / 3.2：
Population Decoding / 3.3：
Spike-Train Decoding / 3.4：
Information Theory / 3.5：
Entropy and Mutual Information / 4.1：
Information and Entropy Maximization / 4.2：
Entropy and Information for Spike Trains / 4.3：
Appendix / 4.4：
Neurons and Neural Circuits / 4.6：
Model Neurons I: Neuroelectronics / 5：
Electrical Properties of Neurons / 5.1：
Single-Compartment Models / 5.3：
Integrate-and-Fire Models / 5.4：
Voltage-Dependent Conductances / 5.5：
The Hodgkin-Huxley Model / 5.6：
Modeling Channels / 5.7：
Synaptic Conductances / 5.8：
Synapses on Integrate-and-Fire Neurons / 5.9：
Model Neurons II: Conductances and Morphology / 5.10：
Levels of Neuron Modeling / 6.1：
Conductance-Based Models / 6.2：
The Cable Equation / 6.3：
Multi-compartment Models / 6.4：
Network Models / 6.5：
Firing-Rate Models / 7.1：
Feedforward Networks / 7.3：
Recurrent Networks / 7.4：
Excitatory-Inhibitory Networks / 7.5：
Stochastic Networks / 7.6：
Adaptation and Learning / 7.7：
Plasticity and Learning / 8：
Synaptic Plasticity Rules / 8.1：
Unsupervised Learning / 8.3：
Supervised Learning / 8.4：
Classical Conditioning and Reinforcement Learning / 8.5：
Classical Conditioning / 9.1：
Static Action Choice / 9.3：
Sequential Action Choice / 9.4：
Representational Learning / 9.5：
Density Estimation / 10.1：
Causal Models for Density Estimation / 10.3：
Discussion / 10.4：
Mathematical Appendix / 10.5：
Linear Algebra / A.1：
Finding Extrema and Lagrange Multipliers / A.2：
Differential Equations / A.3：
Electrical Circuits / A.4：
Probability Theory / A.5：
References / A.6：
Preface
Neural Encoding and Decoding / I：
Neural Encoding I: Firing Rates and Spike Statistics / 1：
2.

図書
図書
2. Brain dynamics : synchronization and activity patterns in pulse-coupled neural nets with delays and noise
Hermann Haken
出版情報: Berlin : Springer, c2002  xii, 245 p. ; 24 cm
シリーズ名: Springer series in synergetics
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3.

図書
図書
3. Analysis of parallel spike trains
Sonja Grün, Stefan Rotter, editors
出版情報: New York : Springer, 2010  xix, 441 p. ; 24 cm
シリーズ名: Springer series in computational neuroscience / series editors, Alain Destexhe, Romain Brette ; v. 7
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Basic Spike Train Statistics: Point Process Models / Part I：
Stochastic Models of Spike Trains / Carl van Vreeswijk1：
Estimating the Firing Rate / Shigeru Shinomoto2：
Analysis and Interpretation of Interval and Count Variability in Neural Spike Trains / Martin Paul Nawrot3：
Processing of Phase-Locked Spikes and Periodic Signals / Go Ashida ; Hermann Wagner ; Catherine E. Carr4：
Pairwise Comparison of Spike Trains / Part II：
Pair-Correlation in the Time and Frequency Domain / Jos J. Eggermont5：
Dependence of Spike-Count Correlations on Spike-Train Statistics and Observation Time Scale / Tom Tetzlaff ; Markus Diesmann6：
Spike Metrics / Jonathan D. Victor ; Keith P. Purpura7：
Gravitational Clustering / George Gerstein8：
Multiple-Neuron Spike Patterns / Part III：
Spatio-Temporal Patterns / Moshe Abeles9：
Unitary Event Analysis / Sonja Grün ; Ad Aertsen10：
Information Geometry of Multiple Spike Trains / Shun-ichi Amari11：
Higher-Order Correlations and Cumulants / Benjamin Staude ; Stefan Rotter12：
Population-Based Approaches / Part IV：
Information Theory and Systems Neuroscience / Don H. Johnson ; Ilan N. Goodman ; Christopher J. Rozell13：
Population Coding / Stefano Panzeri ; Fernando Montani ; Giuseppe Notaro ; Cesare Magri ; Rasmus S. Peterson14：
Stochastic Models for Multivariate Neural Point Processes: Collective Dynamics and Neural Decoding / Wilson Truccolo15：
Practical Issues / Part V：
Simulation of Stochastic Point Processes with Defined Properties / Stefano Cardanobile16：
Generation and Selection of Surrogate Methods for Correlation Analysis / Sebastien Louis ; Christian Borgelt17：
Bootstrap Tests of Hypotheses / Valérie Ventura18：
Generating Random Numbers / Hans Ekkehard Plesser19：
Practically Trivial Parallel Data Processing in a Neuroscience Laboratory / Michael Denker ; Bernd Wiebelt ; Denny Fliegner ; Abigail Morrison20：
Index
Basic Spike Train Statistics: Point Process Models / Part I：
Stochastic Models of Spike Trains / Carl van Vreeswijk1：
Estimating the Firing Rate / Shigeru Shinomoto2：
4.

電子ブック
ＥＢ
4. Brain computation as hierarchical abstraction / ((ebook) :)
Dana H. Ballard
出版情報:   1 online resource (xiv, 440 pages)
シリーズ名: Computational neuroscience ;
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目次情報: 続きを見る
Series Foreword
Preface
Acknowledgments
Setting the Stage / Part 1：
Brain Computation / 1：
Introducing the Brain / 1.1：
Computational Abstraction / 1.2：
Different than Silicon / 1.3：
The Brain's Tricks for Fast Computation / 1.4：
More Powerful than a Computer? / 1.5：
Do Humans Have Non-Turing Abilities? / 1.6：
Summary / 1.7：
Brain Overview / 2：
Spinal Cord and Brainstem / 2.1：
The Forebrain: An Overview / 2.2：
Cortex: Long-Term Memory / 2.3：
Basal Ganglia: The Program Sequencer / 2.4：
Thalamus: Input and Output / 2.5：
Hippocampus: Program Modifications / 2.6：
Amygdal: Rating what's Important / 2.7：
How the Brain Programs itself / 2.8：
Neurons, Circuits, and Subsystems / 2.9：
Neurons and Circuits / 3：
Signaling Strategies / 3.1：
Receptive Fields / 3.2：
Modeling Receptive Field Formation / 3.3：
Spike Codes for Cortical Neurons / 3.4：
Reflexive Behaviors / 3.5：
Appendix: Neuron Behaviors / 3.6：
Cortical Memory / 4：
Table Lookup Strategies / 4.1：
The Cortical Map Concept / 4.2：
Hierarchies of Maps / 4.3：
What Does the Cortex Represent? / 4.4：
Computational Models / 4.5：
Programs via Reinforcement / 4.6：
Evaluating a Program / 5.1：
Reinforcement Learning Algorithms / 5.2：
Learning in the Basal Ganglia / 5.3：
Learning to Set Cortical Synapses / 5.4：
Learning to Play Backgammon / 5.5：
Backgammon as an Abstract Model / 5.6：
Embodiment of Behavior / 5.7：
Sensory-Motor Routines / 6：
Human Vision Is Specialized / 6.1：
Routines / 6.2：
Human Embodiment Overview / 6.3：
Evidence for Visual Routines / 6.4：
Changing the Agenda / 6.5：
Discussion and Summary / 6.6：
Motor Routines / 7：
Motor Computation Basics / 7.1：
Biological Movement Organization / 7.2：
Cortex: Movement Plans / 7.3：
Cerebellum: Checking Expectations / 7.4：
Spinal Cord: Coding the Movement Library / 7.5：
Reading Human Movement Data / 7.6：
Operating System / 7.7：
A Hierarchical Cognitive Architecture / 8.1：
Program Execution / 8.2：
Humanoid Avatar Models / 8.3：
Module Multiplexing / 8.4：
Program Arbitration / 8.5：
Alerting / 8.6：
Program Indexing / 8.7：
Credit Assignment / 8.8：
Implications of a Modular Architecture / 8.9：
Awareness / 8.10：
Decision Making / 9：
The Coding of Decisions / 9.1：
Deciding in Noisy Environments / 9.2：
Social Decision Making / 9.3：
Populations of Game Players / 9.4：
Emotions / 9.5：
Triune Phylogeny / 10.1：
Emotions and the Body / 10.2：
Somatic Marker Theory / 10.3：
The Amygdala's Special Role / 10.4：
Computational Perspectives / 10.5：
Consciousness / 10.6：
Being a Model / 11.1：
Simulation / 11.2：
What Is Consciousness For? / 11.3：
Notes / 11.4：
References
Index
Series Foreword
Preface
Acknowledgments
5.

電子ブック
ＥＢ
5. Computational modeling methods for neuroscientists ((ebook) :)
edited by Erik De Schutter
出版情報: Cambridge, Mass. ; London : MIT Press, c2010  1 online resource (xii, 419 p.)
シリーズ名: Computational neuroscience ;
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目次情報: 続きを見る
Series Foreword
Introduction
Differential Equations / Bard Ermentrout ; John Rinzel1：
Parameter Searching / Pablo Achard ; Werner Van Geit ; Gwendal LeMasson2：
Reaction-Diffusion Modeling / Upinder S. Bhalla ; Stefan Wils3：
Modeling Intracellular Calcium Dynamics / Erik De Schutter4：
Modeling Voltage-Dependent Channels / Alain Destexhe ; John R. Huguenard5：
Modeling Synapses / Arnd Roth ; Mark C. W. van Rossum6：
Modeling Point Neurons / 7：
From Hodgkin-Huxley to Integrate-and-Fire / Nicolas Brunel
Reconstruction of Neuronal Morphology / Gwen Jacobs ; Brenda Claiborne ; Kristen Harris8：
An Approach to Capturing Neuron Morphological Diversity / Haroon Anwar ; Imad Riachi ; Sean Hill ; Felix Schurmann ; Henry Markram9：
Passive Cable Modeling / William R. Holmes10：
Modeling Complex Neurons / 11：
Realistic Modeling of Small Neuronal Networks / Ronald L. Calabrese ; Astrid A. Prinz12：
Large-Scale Network Simulations in Systems Neuroscience / Reinoud Maex ; Michiel Berends ; Hugo Cornelis13：
Software Appendix
References
Contributors
Index
Series Foreword
Introduction
Differential Equations / Bard Ermentrout ; John Rinzel1：
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