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ویرایش: [2 ed.]
نویسندگان: John Milton. Toru Ohira
سری:
ISBN (شابک) : 9783030695781
ناشر: Springer Science+Business Media
سال نشر: 2021
تعداد صفحات: 500
[516]
زبان: English
فرمت فایل : DJVU (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 6 Mb
در صورت تبدیل فایل کتاب Mathematics as a Laboratory Tool - Dynamics, Delays and Noise به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب ریاضیات به عنوان یک ابزار آزمایشگاهی - دینامیک، تاخیر و نویز نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
ویرایش دوم ریاضیات بهعنوان ابزار آزمایشگاهی، تأثیر فزایندهای را که علوم محاسباتی بر انتخابهای شغلی دانشجویان رشتههای علوم و مهندسی در مقطع کارشناسی انجام میدهند، منعکس میکند. تمرکز بر دینامیک و اثرات تاخیرهای زمانی و اغتشاشات تصادفی ("نویز") بر مقررات ارائه شده توسط سیستم های کنترل بازخورد است. این مفاهیم با کاربردهایی در شبکه های تنظیم کننده ژن، کنترل حرکتی، علوم اعصاب و زیست شناسی جمعیت نشان داده شده است. ارائه در ویرایش اول به بحث در مورد تحریک پذیری و انفجار عصبی، چندپایداری، ریز آشوب، استنتاج بیزی، معادلات دیفرانسیل تاخیر مرتبه دوم و روش نیمه گسسته سازی برای ادغام عددی معادلات دیفرانسیل تاخیری بسط داده شده است. هر تلاشی انجام شده است تا اطمینان حاصل شود که مطالب برای کسانی که پیشینه حسابداری دارند در دسترس باشد. این متن مفاهیم ریاضی پیشرفته ای مانند تبدیل های انتگرال لاپلاس و فوریه را در قالب ابزار ارائه می دهد. استنتاج بیزی با استفاده از تعدادی سناریو از نوع کارآگاهی از جمله مسئله مونتی هال معرفی شده است.
The second edition of Mathematics as a Laboratory Tool reflects the growing impact that computational science is having on the career choices made by undergraduate science and engineering students. The focus is on dynamics and the effects of time delays and stochastic perturbations (“noise”) on the regulation provided by feedback control systems. The concepts are illustrated with applications to gene regulatory networks, motor control, neuroscience and population biology. The presentation in the first edition has been extended to include discussions of neuronal excitability and bursting, multistability, microchaos, Bayesian inference, second-order delay differential equations, and the semi-discretization method for the numerical integration of delay differential equations. Every effort has been made to ensure that the material is accessible to those with a background in calculus. The text provides advanced mathematical concepts such as the Laplace and Fourier integral transforms in the form of Tools. Bayesian inference is introduced using a number of detective-type scenarios including the Monty Hall problem.
Preface Laboratory Exercises and Projects Acknowledgments Notation Tools Contents 1 Science and the Mathematics of Black Boxes 1.1 The Scientific Method 1.2 Dynamical Systems 1.2.1 Variables 1.2.2 Measurements 1.2.3 Units 1.3 Input–Output Relationships 1.3.1 Linear Versus Nonlinear Black Boxes 1.3.2 The Neuron as a Dynamical System 1.4 Interactions Between System and Surroundings 1.5 What Have We Learned? 1.6 Exercises for Practice and Insight 2 The Mathematics of Change 2.1 Differentiation 2.2 Differential Equations 2.2.1 Population Growth 2.2.2 Time Scale of Change 2.2.3 Linear ODEs with Constant Coefficients 2.3 Black Boxes 2.3.1 Nonlinear Differential Equations 2.4 Existence and Uniqueness 2.5 What Have We Learned? 2.6 Exercises for Practice and Insight 3 Equilibria and Steady States 3.1 Law of Mass Action 3.2 Closed Dynamical Systems 3.2.1 Equilibria: Drug Binding 3.2.2 Transient Steady States: Enzyme Kinetics 3.3 Open Dynamical Systems 3.3.1 Water Fountains 3.4 The ``Steady-State Approximation'' 3.4.1 Steady State: Enzyme–Substrate Reactions 3.4.2 Steady State: Consecutive Reactions 3.5 Existence of Fixed Points 3.6 What Have We learned? 3.7 Exercises for Practice and Insight 4 Stability 4.1 Landscapes in Stability 4.1.1 Postural Stability 4.1.2 Perception of Ambiguous Figures 4.1.3 Stopping Epileptic Seizures 4.2 Fixed-Point Stability 4.3 Stability of Second-Order ODEs 4.3.1 Real Eigenvalues 4.3.2 Complex Eigenvalues 4.3.3 Phase-Plane Representation 4.4 Illustrative Examples 4.4.1 The Lotka–Volterra Equation 4.4.2 The van der Pol Oscillator 4.4.3 Computer: Friend or Foe? 4.5 Lyapunov's Insight 4.5.1 Conservative Dynamical Systems 4.5.2 Lyapunov's Direct Method 4.6 What Have We Learned? 4.7 Exercises for Practice and Insight 5 Fixed Points: Creation and Destruction 5.1 Saddle-Node Bifurcation 5.1.1 Neuron Bistability 5.2 Transcritical Bifurcation 5.2.1 Postponement of Instability 5.3 Pitchfork Bifurcation 5.3.1 Finger-Spring Compressions 5.4 Near the Bifurcation Point 5.4.1 The Slowing-Down Phenomenon 5.4.2 Critical Phenomena 5.5 Bifurcations at the Benchtop 5.6 What Have We Learned? 5.7 Exercises for Practice and Insight 6 Transient Dynamics 6.1 Step Functions 6.2 Ramp Functions 6.3 Impulse Responses 6.3.1 Measuring the Impulse Response 6.4 The Convolution Integral 6.4.1 Summing Neuronal Inputs 6.5 Transients in Nonlinear Dynamical Systems 6.5.1 Excitability 6.5.2 Bounded Time-Dependent States 6.6 What Have We Learned? 6.7 Exercises for Practice and Insight 7 Frequency Domain I: Bode Plots and Transfer Functions 7.1 Low-Pass Filters 7.2 Laplace Transform Toolbox 7.2.1 Tool 8: Euler's Formula 7.2.2 Tool 9: The Laplace Transform 7.3 Transfer Functions 7.4 Biological Filters 7.4.1 Crayfish Photoreceptor 7.4.2 Osmoregulation in Yeast 7.5 Bode-Plot Cookbook 7.5.1 Constant Term (Gain) 7.5.2 Integral or Derivative Term 7.5.3 Lags and Leads 7.5.4 Time Delays 7.5.5 Complex Pair: Amplification 7.6 Interpreting Biological Bode Plots 7.6.1 Crayfish Photoreceptor Transfer Function 7.6.2 Yeast Osmotic Stress Transfer Function 7.7 What Have We Learned? 7.8 Exercises for Practice and Insight 8 Frequency Domain II: Fourier Analysis and Power Spectra 8.1 Laboratory Applications 8.1.1 Creating Sinusoidal Inputs 8.1.2 Electrical Interference 8.1.3 The Electrical World 8.2 Fourier Analysis Toolbox 8.2.1 Tool 10: Fourier Series 8.2.2 Tool 11: The Fourier Transform 8.3 Applications of Fourier Analysis 8.3.1 Uncertainties in Fourier Analysis 8.3.2 Approximating a Square Wave 8.3.3 Power Spectrum 8.3.4 Fractal Heartbeats 8.4 Digital Signals 8.4.1 Low-Pass Filtering 8.4.2 Introducing the FFT 8.4.3 Power Spectrum: Discrete Time Signals 8.4.4 Using FFTs in the Laboratory 8.4.5 Power Spectral Density Recipe 8.5 What Have We Learned? 8.6 Exercises for Practice and Insight 9 Feedback and Control Systems 9.1 Feedback Control 9.2 Pupil Light Reflex (PLR) 9.3 ``Linear'' Feedback 9.3.1 Proportional-Integral-Derivative (PID) Controllers 9.4 Delayed Feedback 9.4.1 Example: Wright's Equation 9.5 Production–Destruction 9.5.1 Negative Feedback: PLR 9.5.2 Negative Feedback: Gene Regulatory Systems 9.5.3 Stability: General Forms 9.5.4 Positive Feedback: Cheyne–Stokes Respiration 9.6 Electronic Feedback 9.6.1 The Clamped PLR 9.7 Intermittent Control 9.7.1 Virtual Balancing 9.8 Integrating DDEs 9.9 What Have We Learned? 9.10 Exercises for Practice and Insight 10 Oscillations 10.1 Neural Oscillations 10.1.1 Hodgkin–Huxley Neuron Model 10.2 Hopf Bifurcations 10.2.1 PLR: Supercritical Hopf Bifurcation 10.2.2 HH Equation: Hopf Bifurcations 10.3 Analyzing Oscillations 10.4 Poincaré Sections 10.4.1 Gait Stride Variability 10.5 Phase Resetting 10.5.1 Stumbling 10.6 Interacting Oscillators 10.6.1 Periodic Forcing 10.6.2 Coupled Oscillators 10.7 What Have We Learned? 10.8 Exercises for Practice and Insight 11 Beyond Limit Cycles 11.1 Chaos and Parameters 11.2 Dynamical Diseases and Parameters 11.3 Chaos in the Laboratory 11.3.1 Flour Beetle Cannibalism 11.3.2 Clamped PLR with ``Mixed Feedback'' 11.4 Complex Dynamics: The Human Brain 11.4.1 Reduced Hodgkin–Huxley Models 11.4.2 Delay-Induced Transient Oscillations 11.5 What Have We Learned? 11.6 Exercises for Practice and Insight 12 Random Perturbations 12.1 Noise and Dynamics 12.1.1 Stick Balancing at the Fingertip 12.1.2 Noise and Thresholds 12.1.3 Stochastic Gene Expression 12.2 Stochastic Processes Toolbox 12.2.1 Random Variables and Their Properties 12.2.2 Stochastic Processes 12.2.3 Statistical Averages 12.3 Laboratory Evaluations 12.3.1 Intensity 12.3.2 Estimation of the Probability Density Function 12.3.3 Estimation of Moments 12.3.4 Broad-Shouldered Probability Density Functions 12.4 Correlation Functions 12.4.1 Estimating Autocorrelation 12.5 Power Spectrum of Stochastic Signals 12.6 Examples of Noise 12.7 Cross-Correlation Function 12.7.1 Impulse Response 12.7.2 Measuring Time Delays 12.7.3 Coherence 12.8 What Have We Learned? 12.9 Problems for Practice and Insight 13 Noisy Dynamical Systems 13.1 The Langevin Equation: Additive Noise 13.1.1 The Retina As a Recording Device 13.1.2 Time-Delayed Langevin Equation 13.1.3 Skill Acquisition 13.1.4 Stochastic Gene Expression 13.1.5 Numerical Integration of Langevin Equations 13.2 Noise and Thresholds 13.2.1 Linearization 13.2.2 Dithering 13.2.3 Stochastic Resonance 13.3 Parametric Noise 13.3.1 On–Off Intermittency: Quadratic Map 13.3.2 Langevin Equation with Parametric Noise 13.3.3 Stick Balancing at the Fingertip 13.4 What Have We Learned? 13.5 Problems for Practice and Insight 14 Random Walks 14.1 A Simple Random Walk 14.1.1 Walking Molecules as Cellular Probes 14.2 Anomalous Random Walks 14.3 Correlated Random Walks 14.3.1 Random Walking While Standing Still 14.3.2 Gait Stride Variability: DFA 14.3.3 Walking on the DNA Sequence 14.4 Delayed Random Walks 14.5 Portraits in Diffusion 14.5.1 Finding a Mate 14.5.2 Facilitated Diffusion on the Double Helix 14.6 Optimal Search Patterns: Lévy Flights 14.7 Probability Density Functions 14.7.1 Master Equation 14.8 Tool 12: Generating Functions 14.8.1 Approaches Using the Characteristic Function 14.9 What Have We Learned? 14.10 Problems for Practice and Insight 15 Thermodynamic Perspectives 15.1 Equilibrium 15.1.1 Mathematical Background 15.1.2 First and Second Laws of Thermodynamics 15.1.3 Spontaneity 15.2 Nonequilibrium 15.3 Dynamics and Temperature 15.3.1 Near Equilibrium 15.3.2 Principle of Detailed Balancing 15.4 Entropy Production 15.4.1 Minimum Entropy Production 15.4.2 Microscopic Entropy Production 15.5 Far from Equilibrium 15.5.1 The Sandpile Paradigm 15.5.2 Power Laws 15.5.3 Measuring Power Laws 15.5.4 Epileptic Quakes 15.6 What Have We Learned? 15.7 Exercises for Practice and Insight 16 Concluding Remarks References Index