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E-grāmata: BioInformation Processing: A Primer on Computational Cognitive Science

  • Formāts: PDF+DRM
  • Sērija : Cognitive Science and Technology
  • Izdošanas datums: 10-Feb-2016
  • Izdevniecība: Springer Verlag, Singapore
  • Valoda: eng
  • ISBN-13: 9789812878717
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BioInformation Processing: A Primer on Computational Cognitive SciencePalielināt
  • Formāts: PDF+DRM
  • Sērija : Cognitive Science and Technology
  • Izdošanas datums: 10-Feb-2016
  • Izdevniecība: Springer Verlag, Singapore
  • Valoda: eng
  • ISBN-13: 9789812878717
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This book shows how mathematics, computer science and science can be usefully and seamlessly intertwined. It begins with a general model of cognitive processes in a network of computational nodes, such as neurons, using a variety of tools from mathematics, computational science and neurobiology. It then moves on to solve the diffusion model from a low-level random walk point of view. It also demonstrates how this idea can be used in a new approach to solving the cable equation, in order to better understand the neural computation approximations. It introduces specialized data for emotional content, which allows a brain model to be built using MatLab tools, and also highlights a simple model of cognitive dysfunction.
Part I Introductory Matter
1 BioInformation Processing
3(16)
1.1 The Proper Level of Abstraction
4(1)
1.2 The Threads of Our Tapestry
5(2)
1.3
Chapter Guide
7(4)
1.4 Theoretical Modeling Issues
11(3)
1.5 Code
14(5)
References
15(4)
Part II Diffusion Models
2 The Diffusion Equation
19(20)
2.1 The Microscopic Space-Time Evolution of a Particle
19(3)
2.2 The Random Walk and the Binomial Distribution
22(2)
2.3 Rightward Movement Has Probability 0.5 or Less
24(4)
2.3.1 Finding the Average of the Particles Distribution in Space and Time
25(1)
2.3.2 Finding the Standard Deviation of the Particles Distribution in Space and Time
26(2)
2.3.3 Specializing to an Equal Probability Left and Right Random Walk
28(1)
2.4 Macroscopic Scale
28(2)
2.5 Obtaining the Probability Density Function
30(3)
2.5.1 P Less Than 0.5
31(1)
2.5.2 P and q Arc Equal
32(1)
2.6 Understanding the Probability Distribution of the Particle
33(1)
2.7 The General Diffusion Equation
34(5)
References
37(2)
3 Integral Transforms
39(6)
3.1 The Laplace Transform
39(2)
3.1.1 Homework
40(1)
3.2 The Fourier Transform
41(4)
3.2.1 Homework
44(1)
4 The Time Dependent Cable Solution
45(16)
4.1 The Solution for a Current Impulse
46(9)
4.1.1 Modeling the Current Pulses
46(1)
4.1.2 Scaling the Cable Equation
47(2)
4.1.3 Applying the Laplace Transform in Time
49(1)
4.1.4 Applying the Fourier Transform in Space
50(1)
4.1.5 The J Transform of the Pulse
50(1)
4.1.6 The Idealized Impulse 3T Transform Solution
51(1)
4.1.7 Inverting the ST Transform Solution
51(2)
4.1.8 A Few Computed Results
53(1)
4.1.9 Reinterpretation in Terms of Charge
54(1)
4.2 The Solution to a Constant Current
55(2)
4.3 Time Dependent Solutions
57(4)
Reference
58(3)
Part III Neural Systems
5 Mammalian Neural Structure
61(22)
5.1 The Basic Model
61(8)
5.2 Brain Structure
69(1)
5.3 The Brain Stem
69(5)
5.4 Cortical Structure
74(9)
5.4.1 Cortical Processing
76(1)
5.4.2 Isocortex Modeling
77(4)
References
81(2)
6 Abstracting Principles of Computation
83(24)
6.1 Cellular Triggers
83(3)
6.2 Dynamical Loop Details
86(4)
6.3 An Implication for Biological Computation
90(1)
6.4 Transport Mechanisms and Switches
91(3)
6.5 Control of a Substance via Creation/Destruction Patterns
94(2)
6.6 Calcium Ion Signaling
96(5)
6.7 Modulation Pathways
101(6)
6.7.1 Ligand---Receptor Response Strategies
102(3)
References
105(2)
7 Second Messenger Diffusion Pathways
107(10)
7.1 Calcium Diffusion in the Cytosol
107(7)
7.1.1 Assumption One: Calcium Binding Is Fast
110(2)
7.1.2 Assumption Two: Binding Rate Is Much Less Than Disassociation Rate
112(2)
7.2 Transcriptional Control of Free Calcium
114(3)
References
116(1)
8 Second Messenger Models
117(20)
8.1 Generic Second Messenger Triggers
117(5)
8.1.1 Concatenated Sigmoid Transitions
121(1)
8.2 A Graphic Model Computation Model
122(3)
8.3 Ca++ Triggers
125(1)
8.4 Spatially Dependent Calcium Triggers
126(2)
8.5 Calcium Second Messenger Pathways
128(2)
8.6 General Pharmacological Inputs
130(3)
8.6.1 7 Transmembrane Regions
131(1)
8.6.2 4 Transmembrane Regions
132(1)
8.6.3 Family Two: The Agonist Spectrum
132(1)
8.6.4 Allosteric Modulation of Output
133(1)
8.7 Neurotransmitter Effects
133(4)
Reference
136(1)
9 The Abstract Neuron Model
137(38)
9.1 Neuron Inputs
137(2)
9.2 Neuron Outputs
139(2)
9.3 Abstract Neuron Design
141(10)
9.3.1 Toxin Recognition
143(8)
9.4 Feature Vector Abstraction
151(18)
9.4.1 The BFV Functional Form
152(3)
9.4.2 Modulation of the BFV Parameters
155(1)
9.4.3 Modulation via the BFV Ball and Stick Model
156(13)
9.5 The Full Abstract Neuron Model
169(6)
References
171(4)
Part IV Models of Emotion and Cognition
10 Emotional Models
175(8)
10.1 The Sloman Emotional Model
179(1)
10.2 PsychoPhysiological Data
180(3)
References
182(1)
11 Generation of Music Data: J. Peterson and L. Dzuris
183(22)
11.1 A Musical Grammar
183(3)
11.2 The Wurfelspiel Approach
186(1)
11.3 Neutral Music Data Design
187(4)
11.3.1 Neutral Musical Alphabet Design
187(2)
11.3.2 The Generated Musical Phrases
189(2)
11.4 Emotional Musical Data Design
191(14)
11.4.1 Emotion and Music
191(4)
11.4.2 Emotional Music Data Design
195(1)
11.4.3 Happy Musical Data
196(1)
11.4.4 Sad Musical Data
197(2)
11.4.5 Angry Musical Data
199(2)
11.4.6 Emotional Musical Alphabet Selection
201(2)
References
203(2)
12 Generation of Painting Data: J. Peterson, L. Dzuris and Q. Peterson
205(22)
12.1 Developing a Painting Model
205(5)
12.2 Neutral Painting Data
210(2)
12.2.1 The Neutral Kunsterisches Wurfelspiel Approach
211(1)
12.3 Encoding the Painting Data
212(4)
12.4 Emotionally Labeled Painting Data
216(11)
12.4.1 Painting and Emotion in the Literature
216(4)
12.4.2 The Emotional Kunsterisches Wurfelspiel Approach
220(5)
References
225(2)
13 Modeling Compositional Design
227(24)
13.1 The Cognitive Dysfunction Model Review
228(3)
13.2 Connectionist Based Compositional Design
231(6)
13.2.1 Preprocessing
231(3)
13.2.2 Noun to Verb Processing
234(2)
13.2.3 Sentence Construction
236(1)
13.3 Neurobiologically Based Compositional Design
237(5)
13.3.1 Recalling Data Generation
237(2)
13.3.2 Training the Isocortex Model
239(2)
13.3.3 Sensor Fusion in Area 37
241(1)
13.4 Integration of the Models
242(2)
13.5 Depression Models
244(1)
13.6 Integration into a Virtual World
245(2)
13.7 Lesion Studies
247(1)
13.8 The Complete Cognitive Model
248(1)
13.9 Virtual World Constructions
249(2)
References
250(1)
14 Networks of Excitable Neurons
251(26)
14.1 The Basic Neurotransmitters
251(3)
14.2 Modeling Issues
254(2)
14.3 Software Implementation Thoughts
256(3)
14.4 How Would We Code Synapse Interaction?
259(5)
14.4.1 The Catecholamine Abstraction
260(3)
14.4.2 PSD Computation
263(1)
14.5 Networks of Neural Objects
264(8)
14.5.1 Chained Architecture Details
264(6)
14.5.2 Modeling Neurotransmitter Interactions
270(2)
14.6 Neuron Calculations
272(5)
References
276(1)
15 Training the Model
277(10)
15.1 The OCOS DAG
277(4)
15.1.1 Some MatLab Comments
280(1)
15.2 Homework
281(1)
15.3 Final Comments
281(6)
References
284(3)
Part V Simple Abstract Neurons
16 Matrix Feed Forward Networks
287(28)
16.1 Introduction
287(3)
16.2 Minimizing the MFFN Energy
290(1)
16.3 Partial Calculations for the MFFN
291(4)
16.3.1 The Last Hidden Layer
291(1)
16.3.2 The Remaining Hidden Layers
292(3)
16.4 The Full Backpropagation Equations for the MFFN
295(1)
16.5 A Three Layer Example
296(2)
16.5.1 The Output Layer
296(1)
16.5.2 The Hidden Layer
297(1)
16.5.3 The Input Layer
297(1)
16.6 A MatLab Beginning
298(3)
16.7 MatLab Implementations
301(8)
16.7.1 Initialization
301(1)
16.7.2 Evaluation
302(1)
16.7.3 Updating
303(5)
16.7.4 Training
308(1)
16.8 Sample Training Sessions
309(6)
16.8.1 Approximating a Step Function
309(2)
16.8.2 Approximating sin2
311(1)
16.8.3 Approximating sin2 Again: Linear Outputs
312(2)
Reference
314(1)
17 Chained Feed Forward Architectures
315(18)
17.1 Introduction
315(4)
17.2 Minimizing the CFFN Energy
319(1)
17.3 Partial Derivative Calculation in Generalized Chains
319(5)
17.4 Partial Calculations for the CFFN
324(4)
17.4.1 The ∂yj-∂yi Calculation
326(1)
17.4.2 The Internal Parameter Partial Calculations
326(2)
17.5 Simple MatLab Implementations
328(5)
References
330(3)
Part VI Graph Based Modeling In Matlab
18 Graph Models
333(84)
18.1 Building Global Graph Objects One
337(8)
18.1.1 Vertices One
340(2)
18.1.2 Edges One
342(2)
18.1.3 A First Graph Class
344(1)
18.2 Adding Class Methods First Pass
345(15)
18.2.1 Adding Edge Methods
345(1)
18.2.2 Adding Vertices Methods
346(1)
18.2.3 Adding Graph Methods
346(2)
18.2.4 Using the Methods
348(3)
18.2.5 Adding a Graph to an Existing Graph
351(2)
18.2.6 Drawing Graphs
353(3)
18.2.7 Evaluation and Update Strategies
356(4)
18.3 Training
360(11)
18.4 Polishing the Training Code
371(8)
18.5 Comparing the CFFN and MFFN Code
379(2)
18.6 Handling Feedback
381(9)
18.7 Lagged Training
390(11)
18.8 Better Lagged Training!
401(5)
18.9 Improved Gradient Descent
406(11)
References
414(3)
19 Address Based Graphs
417(44)
19.1 Graph Class Two
420(10)
19.1.1 Vertices Two
421(2)
19.1.2 Edges Two
423(2)
19.1.3 Graph Class
425(5)
19.2 Class Methods Two
430(11)
19.2.1 Add Location Methods
430(1)
19.2.2 Add Edge Methods
431(1)
19.2.3 Add Node Methods
431(1)
19.2.4 Finding the Incidence Matrix
432(9)
19.2.5 Get the Laplacian
441(1)
19.3 Evaluation and Update Strategies in Graphs
441(8)
19.4 Adding Inhibition
449(12)
20 Building Brain Models
461(34)
20.1 Build A Cortex Module
461(10)
20.1.1 Build A Cortical Can
462(2)
20.1.2 Build A Cortical Column
464(3)
20.1.3 Build A Cortex Sheet
467(4)
20.2 Build A Thalamus Module
471(3)
20.3 Build A MidBrain Module
474(3)
20.4 Building the Brain Model
477(18)
Reference
491(4)
Part VII Models of Cognition Dysfunction
21 Models of Cognitive Dysfunction
495(24)
21.1 Cognitive Modeling
495(4)
21.1.1 Training Algorithms
499(1)
21.2 Information
499(9)
21.2.1 Laplacian Updates
499(2)
21.2.2 Module Updates
501(3)
21.2.3 Submodule Two Training
504(3)
21.2.4 Submodule One Training
507(1)
21.3 A Normal Brain Model
508(11)
21.3.1 The Cognitive Dysfunction Model
513(2)
References
515(4)
Part VIII Conclusions
22 Conclusions
519(6)
References
521(4)
Part IX Background Reading
23 Background Reading
525(14)
23.1 The Central Nervous System
525(1)
23.2 Information Theory, Biological Complexity and Neural Circuits
526(1)
23.3 Nervous System Evolution and Cognition
526(1)
23.4 Comparative Cognition
527(3)
23.5 Neural Signaling
530(1)
23.6 Gene Regulatory Circuits
530(1)
23.7 Software
531(2)
23.8 Theoretical Robotics
533(6)
References
533(6)
Glossary 539(16)
Index 555
Dr. James Peterson is an Associate Professor in Mathematical Sciences and Biological Sciences at Clemson University, USA. His formal training is in mathematics but he has worked as an aerospace engineer and a software engineer also. He enjoys working on very hard problems that require multiple disciplines to make sense out of and he reads, studies and plays in cutting edge areas a lot as part of his interests.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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