Computational Neuroscience : A Comprehensive Approach (Chapman & Hall/crc Mathematical Biology & Medicine Series)

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Computational Neuroscience : A Comprehensive Approach (Chapman & Hall/crc Mathematical Biology & Medicine Series)

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  • 製本 Hardcover:ハードカバー版/ページ数 688 p.
  • 言語 ENG
  • 商品コード 9781584883623
  • DDC分類 573.81

Full Description


How does the brain work? After a century of research, we still lack a coherent view of how neurons process signals and control our activities. But as the field of computational neuroscience continues to evolve, we find that it provides a theoretical foundation and a set of technological approaches that can significantly enhance our understanding.Computational Neuroscience: A Comprehensive Approach provides a unified treatment of the mathematical theory of the nervous system and presents concrete examples demonstrating how computational techniques can illuminate difficult neuroscience problems. In chapters contributed by top researchers, the book introduces the basic mathematical concepts, then examines modeling at all levels, from single-channel and single neuron modeling to neuronal networks and system-level modeling. The emphasis is on models with close ties to experimental observations and data, and the authors review application of the models to systems such as olfactory bulbs, fly vision, and sensorymotor systems.Understanding the nature and limits of the strategies neural systems employ to process and transmit sensory information stands among the most exciting and difficult challenges faced by modern science. This book clearly shows how computational neuroscience has and will continue to help meet that challenge.

Table of Contents

1 A Theoretical Overview                           1  (30)
Henry C Tuckwell 1, and Jianfeng Feng 2 (1
Epidemiology and Information Science, Faculty
of Medicine St Antoine, University of Paris
6, 27 rue Chaligny, 75012 Paris, France), (2
Department of Informatics, University of
Sussex, Brighton BN1 9QH, U.K.)
1.1 Introduction 1 (1)
1.2 Deterministic dynamical systems 2 (5)
1.2.1 Basic notation and techniques 2 (2)
1.2.2 Single neuron modelling 4 (2)
1.2.3 Phase model 6 (1)
1.3 Stochastic dynamical systems 7 (9)
1.3.1 Jump processes 7 (3)
1.3.2 Diffusion processes 10 (3)
1.3.3 Jump-diffusion models 13 (1)
1.3.4 Perturbation of deterministic 13 (3)
dynamical systems
1.4 Information theory 16 (3)
1.4.1 Shannon information 16 (1)
1.4.2 Mutual information 17 (1)
1.4.3 Fisher information 17 (1)
1.4.4 Relationship between the various 18 (1)
measurements of information
1.5 Optimal control 19 (12)
1.5.1 Optimal control of movement 19 (2)
1.5.2 Optimal control of single neuron 21 (10)
2 Atomistic Simulations of Ion Channels 31 (44)
Peter D. Tieleman Dept. of Biological N4
Sciences, University of Calgary, Alberta,
Canada T2N 1N
2.1 Introduction 31 (6)
2.1.1 Scope of this chapter 31 (1)
2.1.2 Ion channels 32 (5)
2.2 Simulation methods 37 (10)
2.2.1 Molecular dynamics 39 (4)
2.2.2 Continuum electrostatics 43 (2)
2.2.3 Brownian dynamics 45 (2)
2.2.4 Other methods 47 (1)
2.3 Selected applications 47 (16)
2.3.1 Simplified systems 47 (2)
2.3.2 Gramicidin A 49 (1)
2.3.3 Alamethicin 50 (3)
2.3.4 OmpF 53 (4)
2.3.5 The potassium channel KcsA 57 (6)
2.4 Outlook 63 (12)
3 Modelling Neuronal Calcium Dynamics 75 (22)
Saleet M. Jafri 1, and Keun-Hang Yang 2 (1
Program in Bioinformatics and Computational
Biology, School of Computational Sciences,
George Mason University, 10900 University
Blvd., Manassas, VA 20110, U.S.), (2
Department of Neurology, The Johns Hopkins
University, School of Medicine, 600 North
Wolfe Street, Meyer 2-147 Baltimore, MD
21287, U.S.)
3.1 Introduction 75 (2)
3.2 Basic principles 77 (7)
3.2.1 Intracellular calcium stores 79 (2)
3.2.2 Calcium channels 81 (1)
3.2.3 Calcium pumps and exchangers 81 (1)
3.2.4 Mitochondrial calcium 82 (2)
3.3 Special calcium signaling for neurons 84 (7)
3.3.1 Local domain calcium 85 (2)
3.3.2 Cross-talk between channels 87 (2)
3.3.3 Control of gene expression 89 (2)
3.4 Conclusions 91 (6)
4 Structure-Based Models of NO Diffusion in the 97 (34)
Nervous System
Andrew Philippides 1, Phil Husbands 2, Tom
Smiths, and Michael O'Shea (1 Centre for
Computational Neuroscience and Robotics
(CCNR), 'Department of Biology), (2
Department of Informatics, University of
Sussex, Brighton, U.K.)
4.1 Introduction 97 (3)
4.2 Methods 100(14)
4.2.1 Equations governing NO diffusion in 100(2)
the brain
4.2.2 Analytic solutions to the diffusion 102(6)
equation
4.2.3 Modelling diffusion of NO from an 108(4)
irregular 3D structure
4.2.4 Parameter values 112(2)
4.3 Results 114(10)
4.3.1 Diffusion from a typical neuron 115(3)
4.3.2 Effect of neuron size 118(1)
4.3.3 Small sources 119(5)
4.4 Exploring functional roles with more 124(3)
abstract models
4.4.1 The GasNet model 125(1)
4.4.2 Gas diffusion in the networks 125(1)
4.4.3 Modulation 126(1)
4.5 Conclusions 127(4)
5 Stochastic Modelling of Single Ion Channels 131(28)
Alan G. Hawkes European Business Management
School, University of Wales, Swansea, SA2
BPP, U.K.
5.1 Introduction 131(3)
5.2 Some basic probability 134(1)
5.3 Single channel models 134(5)
5.3.1 A three-state mechanism 135(1)
5.3.2 A simple channel-block mechanism 136(1)
5.3.3 A five-state model 137(2)
5.4 Transition probabilities, macroscopic 139(3)
currents and noise
5.4.1 Transition probabilities 140(1)
5.4.2 Macroscopic currents and noise 141(1)
5.5 Behaviour of single channels under 142(7)
equilibrium conditions
5.5.1 The duration of stay in an 142(2)
individual state
5.5.2 The distribution of open times and 144(2)
shut times
5.5.3 Joint distributions 146(1)
5.5.4 Correlations between intervals 147(1)
5.5.5 Bursting behaviour 148(1)
5.6 Time interval omission 149(2)
5.7 Some miscellaneous topics 151(8)
5.7.1 Multiple levels 151(1)
5.7.2 Hidden Markov Methods of analysis 151(8)
6 The Biophysical Basis of Firing Variability 159(26)
in Cortical Neurons
Hugh P.C. Robinson Department of Physiology,
University of Cambridge, Downing Street,
Cambridge, CB2 3EG, U.K.
6.1 Introduction 159(1)
6.2 Typical input is correlated and 160(1)
irregular
6.3 Synaptic unreliability 161(2)
6.4 Postsynaptic ion channel noise 163(2)
6.5 Integration of a transient input by 165(3)
cortical neurons
6.6 Noisy spike generation dynamics 168(3)
6.7 Dynamics of NMDA receptors 171(2)
6.8 Class 1 and class 2 neurons show 173(1)
different noise sensitivities
6.9 Cortical cell dynamical classes 174(2)
6.10 Implications for synchronous firing 176(2)
6.11 Conclusions 178(7)
7 Generating Quantitatively Accurate, but 185(30)
Computationally Concise, Models of Single
Neurons
Gareth Leng, Arieta Reiff Marganiec, Mike
Ludwig, and Nancy Sabatier College of Medical
and Veterinary Sciences, University of
Edinburgh, EH9 8XD, U.K.
7.1 Introduction 186(4)
7.1.1 The scale of the problem 186(3)
7.1.2 Strategies for developing 189(1)
computationally concise models
7.2 The hypothalamo-hypophysial system 190(7)
7.2.1 Firing patterns of vasopressin neurons 191(2)
7.2.2 Implications of membrane bistability 193(1)
for responsiveness to afferent input
7.2.3 Firing patterns of oxytocin cells 194(1)
7.2.4 Intrinsic properties 194(2)
7.2.5 Intracellular Cat+ concentration 196(1)
7.2.6 Implications 197(1)
7.3 Statistical methods to investigate the 197(10)
intrinsic mechanisms underlying spike
patterning
7.3.1 Selecting recordings for analysis 197(1)
7.3.2 Interspike interval distributions 198(1)
7.3.3 Modelling 198(1)
7.3.4 Simulating oxytocin cell activity 199(2)
7.3.5 Experimental testing of the model 201(2)
7.3.6 Firing rate analysis 203(2)
7.3.7 Index of dispersion 205(1)
7.3.8 Autocorrelation analysis 206(1)
7.4 Summary and conclusions 207(8)
8 Bursting Activity in Weakly Electric Fish 215(38)
R iger Krahe 1 and Fabrizio Gabbiani 2 (1
Beckman Institute for Advanced Science and
Technology, Department of Molecular and
Integrative Physiology, University of
Illinois at Urbana/Champaign, 405 N. Mathews
Ave. Urbana, IL 61801, U.S.) (2 Division of
Neuroscience, Baylor College of Medicine, One
Baylor Plaza, Houston, TX 77030, U.S.)
8.1 Introduction 216(1)
8.1.1 What is a burst? 216(1)
8.1.2 Why bursts? 217(1)
8.2 Overview of the electrosensory system 217(6)
8.2.1 Behavioral significance of 220(1)
electrosensation
8.2.2 Neuroanatomy of the electrosensory 220(2)
system
8.2.3 Electrophysiology and encoding of 222(1)
amplitude modulations
8.3 Feature extraction by spike bursts 223(6)
8.3.1 Bursts reliably indicate relevant 223(2)
stimulus features
8.3.2 Feature extraction analysis 225(4)
8.4 Factors shaping burst firing in vivo 229(2)
8.5 Conditional action potential 231(7)
backpropagation controls burst firing in
vitro
8.5.1 Experimental evidence for 231(2)
conditional backpropagation
8.5.2 Multicompartmental model of 233(2)
pyramidal cell bursts
8.5.3 Reduced models of burst firing 235(3)
8.6 Comparison with other bursting neurons 238(3)
8.6.1 Ping-pong between soma and dendrite 239(1)
8.6.2 Dynamical properties of burst 240(1)
oscillations
8.6.3 Intra-burst ISI sequences 241(1)
8.7 Conclusions 241(12)
9 Likelihood Methods for Neural Spike Train 253(34)
Data Analysis
Emery N. Brown, Riccardo Barbieri, Uri T.
Eden, and Loren M. Frank Neuroscience
Statistics Research Laboratory, Department of
Anesthesia and Critical Care, Massachusetts
General Hospital, U.S., Division of Health
Sciences and Technology, Harvard Medical
School, Massachusetts Institute of
Technology, U.S.
9.1 Introduction 253(2)
9.2 Theory 255(8)
9.2.1 The conditional intensity function 255(3)
and interspike interval probability
density
9.2.2 The likelihood function of a point 258(2)
process model
9.2.3 Summarizing the likelihood 260(1)
function: maximum likelihood estimation
and Fisher information
9.2.4 Properties of maximum likelihood 261(1)
estimates
9.2.5 Model selection and model 262(1)
goodness-of-fit
9.3 Applications 263(19)
9.3.1 An analysis of the spiking activity 263(7)
of a retinal neuron
9.3.2 An analysis of hippocampal 270(6)
place-specific firing activity
9.3.3 An analysis of the spatial 276(6)
receptive field dynamics of a hippocampal
neuron
9.4 Conclusion 282(1)
9.5 Appendix 283(4)
10 Biologically-Detailed Network Modelling 287(18)
Andrew Davison Yale University School of
Medicine, Section of Neurobiology, P.O. Box
208001, New Haven, CT 06520-8001, U.S.
10.1 Introduction 287(2)
10.2 Cells 289(2)
10.2.1 Modelling olfactory bulb neurons 290(1)
10.2.2 Modelling cerebellum neurons 290(1)
10.3 Synapses 291(1)
10.4 Connections 292(5)
10.4.1 Network topology 292(1)
10.4.2 Number of connections 293(1)
10.4.3 Distribution of connections 294(3)
10.5 Inputs 297(3)
10.5.1 Spatiotemporal pattern of inputs 297(1)
10.5.2 Inputs to individual cells 298(2)
10.6 Implementation 300(1)
10.7 Validation 300(1)
10.8 Conclusions 301(4)
11 Hebbian Learning and Spike-Timing-Dependent 305(36)
Plasticity
Sen Song Freeman Building, Cold Spring Harbor
Laboratory, 1 Bungtown Rd., Cold Spring
Harbor, NY 22734, U.S.
11.1 Hebbian models of plasticity 305(2)
11.2 Spike-timing dependent plasticity 307(2)
11.3 Role of constraints in Hebbian learning 309(4)
11.3.1 Covariance rule 310(1)
11.3.2 Constraints based on postsynaptic 310(1)
rate
11.3.3 Constraints on total synaptic 311(2)
weights
11.4 Competitive Hebbian learning through 313(13)
STDP
11.4.1 STDP is stable and competitive by 313(1)
itself
11.4.2 Temporal correlation between 314(1)
inputs and output neuron
11.4.3 Mean rate of change in synaptic 315(2)
strength
11.4.4 Equilibrium synaptic strengths 317(2)
11.4.5 Three common scenarios and 319(7)
comparison to simulations
11.5 Temporal aspects of STDP 326(1)
11.6 STDP in a network 327(5)
11.6.1 Hebbian models of map development 327(4)
and plasticity
11.6.2 Distributed synchrony in a 331(1)
recurrent network
11.7 Conclusion 332(9)
12 Correlated Neuronal Activity: High- and 341(34)
Low-Level Views
Emilio Salinas, and Terrence J. Sejnowski 2,3
(1 Department of Neurobiology and Anatomy,
Wake Forest University School of Medicine,
Medical Center Boulevard, Winston-Salem, NC
27157-1010, U.S.), (2 Computational
Neurobiology Laboratory, Howard Hughes
Medical Institute, The Salk Institute for
Biological Studies, 10010 North Torrey Pines
Road, La Jolla, CA 92037, U.S.), (3
Department of Biology, University of
California at San Diego, La Jolla, CA 92093,
U.S.)
12.1 Introduction: the timing game 341(2)
12.2 Functional roles for spike timing 343(4)
12.2.1 Stimulus representation 343(2)
12.2.2 Information flow 345(2)
12.3 Correlations arising from common input 347(3)
12.4 Correlations arising from local 350(3)
network interactions
12.5 When are neurons sensitive to 353(4)
correlated input?
12.5.1 Coincidence detection 354(1)
12.5.2 Fluctuations and integrator models 354(3)
12.6 A simple, quantitative model 357(7)
12.6.1 Parameterizing the input 357(2)
12.6.2 A random walk in voltage 359(3)
12.6.3 Quantitative relationships between 362(2)
input and output
12.7 Correlations and neuronal variability 364(1)
12.8 Conclusion 365(2)
12.9 Appendix 367(8)
13 A Case Study of Population Coding: Stimulus 375(16)
Localisation in the Barrel Cortex
Rasmus S. Petersen 1, and Stefano Panzeri 2
(1 Cognitive Neuroscience Sector,
International School for Advanced Studies,
Via Beirut 2/4, 34014 Trieste, Italy), (2
UMIST, Department of Optometry and
Neuroscience, Manchester M601QD, U.K.)
13.1 Introduction 375(1)
13.2 Series expansion method 376(6)
13.2.1 Quantifying neuronal responses and 376(1)
stimuli
13.2.2 Mutual information and sampling 377(1)
bias
13.2.3 Series expansion approach to 378(3)
information estimation
13.2.4 Generalised series expansion 381(1)
13.2.5 Sampling bias of the series 382(1)
expansion
13.3 The whisker system 382(2)
13.3.1 Whisking behaviour 382(1)
13.3.2 Anatomy of the whisker system 383(1)
13.3.3 Whisker physiology 384(1)
13.4 Coding in the whisker system 384(5)
13.4.1 Introduction 384(1)
13.4.2 Role of spike timing 385(4)
13.5 Discussion 389(3)
13.5.1 Decoding first spike times 389(1)
13.5.2 Role of cross-correlations in 390(2)
population codes
13.6 Conclusions 392
14 Modelling Fly Motion Vision 391(40)
Alexander Borst Max-Planck-Institute of
Neurobiology, Department of Systems and
Computational Neurobiology, Am Klopferspitz
18a D-82152 Martinsried, Germany
14.1 The fly motion vision system: an 397(3)
overview
14.2 Mechanisms of local motion detection: 400(11)
the correlation detector
14.2.1 Steady-state response properties 401(5)
14.2.2 Dynamic response properties 406(2)
14.2.3 Additional filters and adaptive 408(3)
properties
14.3 Spatial processing of local motion 411(10)
signals by Lobula plate tangential cells
14.3.1 Compartmental models of tangential 411(3)
cells
14.3.2 Dendritic integration and gain 414(3)
control
14.3.3 Binocular interactions 417(1)
14.3.4 Dendro-dendritic interactions 418(3)
14.4 Conclusions 421(10)
15 Mean-Field Theory of Irregularly Spiking 431(60)
Neuronal Populations and Working Memory in
Recurrent Cortical Networks
Alfonso Renart 1, Nicolas Brunel 2, and
Xiao-Jing Wang 1 (1 Volen Center for Complex
Systems, Brandeis University, Waltham, MA
02254, U.S.), (2 CNRS, Neurophysique et
Physiologie du Syst鑪e Moteur, Universit 
Paris Ren Descartes, 45 rue des Saints

P鑽es, 75270 Paris Cedex 06, France)

15.1 Introduction 432(2)

15.2 Firing-rate and variability of a 434(18)

spiking neuron with noisy input

15.2.1 The leaky integrate-and-fire neuron 434(1)

15.2.2 Temporal structure of the afferent 435(1)

synaptic current

15.2.3 The diffusion approximation 436(3)

15.2.4 Computation of the mean firing 439(4)

rate and CV

15.2.5 Effect of synaptic time constants 443(2)

15.2.6 Approximate treatment of realistic 445(7)

synaptic dynamics

15.3 Self-consistent theory of recurrent 452(25)

cortical circuits

15.3.1 Self-consistent steady-state 452(7)

solutions in large unstructured networks

15.3.2 Stability and dynamics 459(3)

15.3.3 Bistability in a single population 462(5)

network

15.3.4 Persistent neural activity in an 467(2)

object working memory model

15.3.5 Stability of the persistent 469(3)

activity state

15.3.6 Multistability in balanced networks 472(5)

15.4 Summary and future directions 477(14)

16 The Operation of Memory Systems in the Brain 491(44)

Edmund T. Rolls University of Oxford, Dept.

of Experimental Psychology, South Parks Road,

Oxford 0X13UD, England. www.cns.ox.ac.uk

16.1 Introduction 492(1)

16.2 Functions of the hippocampus in 492(20)

long-term memory

16.2.1 Effects of damage to the 494(1)

hippocampus and connected structures on

object-place and episodic memory

16.2.2 Neurophysiology of the hippocampus 495(3)

and connected areas

16.2.3 Hippocampal models 498(2)

16.2.4 Continuous spatial 500(10)

representations, path integration, and

the use of idiothetic inputs

16.2.5 A unified theory of hippocampal 510(1)

memory: mixed continuous and discrete

attractor networks

16.2.6 The speed of operation of memory 511(1)

networks: the integrate and-fire approach

16.3 Short-term memory systems 512(8)

16.3.1 Prefrontal cortex short-term 512(2)

memory networks, and their relation to

temporal and parietal perceptual networks

16.3.2 Computational details of the model 514(4)

of short-term memory

16.3.3 Computational necessity for a 518(1)

separate, prefrontal cortex, short-term

memory system

16.3.4 Role of prefrontal cortex 519(1)

short-term memory systems in visual

search and attention

16.3.5 Synaptic modification is needed to 519(1)

set up but not to reuse short-term memory

systems

16.4 Invariant visual object recognition 520(1)

16.5 Visual stimulus-reward association, 521(1)

emotion, and motivation

16.6 Effects of mood on memory and visual 522(13)

processing

17 Modelling Motor Control Paradigms 535(40)

Pietro G. Morasso, and Vittorio Sanguineti

University of Genova, DIST (Department of

Informatics Systems and Telecommunications),

Via Opera Pia 13, I-16145 Genova, Italy

17.1 Introduction: the ecological nature of 536(3)

motor control

17.2 The robotic perspective 539(5)

17.2.1 Logical decomposition of motor 539(2)

control into cascaded computational

processes

17.2.2 The Achilles' heel of feedback 541(3)

control

17.3 The biological perspective 544(6)

17.3.1 Motor equivalence and 544(1)

spatiotemporal invariances

17.3.2 The viscous-elastic properties of 545(3)

the human muscles

17.3.3 Dynamic compensation: anticipatory 548(2)

feedforward and feed back control

17.4 The role of cerebellum in the 550(4)

coordination of multiple joints

17.4.1 Abnormal feedforward control in 552(2)

ataxic patients

17.5 Controlling unstable plants 554(8)

17.5.1 Stabilisation of the standing 554(2)

posture: evidence of anticipatory

compensation

17.5.2 Arm trajectory in a divergent 556(2)

force field: evidence of stiffness

modulation

17.5.3 Choosing between stiffness 558(2)

modulation and anticipatory compensation

17.5.4 Implementing anticipatory 560(2)

compensation

17.6 Motor learning paradigms 562(13)

17.6.1 Learning paradigms in neural 562(1)

networks

17.6.2 Adaptive behaviour and motor 563(1)

learning

17.6.3 A distributed computational 564(11)

architecture

18 Computational Models for Generic Cortical 575(32)

Microcircuits

Wolfgang Maass 1, Thomas Natschlaeger 1, and

Henry Markram 2 (1 Institute for Theoretical

Computer Science, Technische Universitaet

Graz, A-8010 Graz, Austria,

maals,tnatschl@igi.tu-graz.ac.at  (2 Brain

Mind Institute, EPFL, Lausanne, Switzerland,

henry.markram@epfl.ch)

18.1 Introduction 575(1)

18.2 A conceptual framework for real-time 576(6)

neural computation

18.3 The generic neural microcircuit model 582(1)

18.4 Towards a non-Turing theory for 582(2)

real-time neural computation

18.5 A generic neural microcircuit on the 584(10)

computational test stand

18.5.1 Speech recognition 584(5)

18.5.2 Predicting movements and solving 589(5)

the aperture problem

18.6 Temporal integration and kernel 594(6)

function of neural microcircuit models

18.6.1 Temporal integration in neural 594(3)

microcircuit models

18.6.2 Kernel function of neural 597(3)

microcircuit models

18.7 Software for evaluating the 600(1)

computational capabilities of neural

microcircuit model

18.8 Discussion 601(6)

19 Modelling Primate Visual Attention 607(20)

Laurent Itti University of Southern

California, Hedco Neuroscience Building

HNB-30A, Los Angeles, CA 90089-2520, U.S.

19.1 Introduction 607(1)

19.2 Brain areas 608(2)

19.3 Bottom-up control 610(4)

19.3.1 Visual search and pop-out 610(1)

19.3.2 Computational models and the 611(3)

saliency map

19.4 Top-down modulation of early vision 614(2)

19.4.1 Are we blind outside of the focus 614(1)

of attention?

19.4.2 Attentional modulation of early 615(1)

vision

19.5 Top-down deployment of attention 616(1)

19.5.1 Attentional facilitation and cuing 616(1)

19.5.2 Influence of task 616(1)

19.6 Attention and scene understanding 617(2)

19.6.1 Is scene understanding purely 618(1)

attentional?

19.6.2 Cooperation between where and what 618(1)

19.6.3 Attention as a component of vision 619(1)

19.7 Discussion 619(8)

Index 627