Advances in Neural Computation, Machine Learning, and Cognitive Research: Selected Papers from the XIX International Conference on Neuroinformatics, October 2-6, 2017, Moscow, Russia 9783319666037, 9783319666044, 3319666037

Table of contents :
Preface......Page 6
Advisory Board......Page 7
Program Committee......Page 9
Contents......Page 12
Neural Network Theory......Page 15
1 Introduction......Page 16
3 Regularization of Deep Neural Networks......Page 17
4 Regularization Representation Using Metagraph Approach......Page 18
5 Experiments......Page 20
References......Page 21
1 Introduction......Page 22
3 Description of the Noise......Page 24
5 Results......Page 25
6 Conclusion......Page 27
References......Page 28
1 Introduction......Page 30
2 Model Problem......Page 32
3 Calculations......Page 33
References......Page 34
1 Introduction......Page 36
2 Model of the Gate Neural Network......Page 37
3 Network Learning Algorithm......Page 41
4 Analysis of the Results......Page 42
5 Conclusion......Page 43
References......Page 44
1 Introduction......Page 46
2 Neuron Model......Page 47
3 Gateway Model......Page 49
References......Page 51
1 Introduction......Page 52
2.2 Neural Network Architectures......Page 53
3.2 Factoid Answer Selection from Alternatives......Page 54
3.3 Common Sense Questions......Page 55
4 Conclusions......Page 56
References......Page 57
Applications of Neural Networks......Page 58
1 Introduction......Page 59
2 Overview of Deep Neural Network Architectures......Page 60
3 Neuron Models......Page 61
References......Page 62
1 Introduction......Page 65
2 Problem Formulation......Page 66
4 Computer Simulation......Page 67
5 Conclusions......Page 69
References......Page 70
1 Introduction......Page 71
2 Analysis of the Structure of a Digital Neural Network Control System Based on the Universal Computer......Page 73
3 Implementation of Phases of the Control Cycle of the Neural Network System......Page 74
4 A Coherent Information Environment Model for Neural-Network Control System......Page 75
References......Page 76
1 Introduction......Page 77
2 Mathematical Model of Longitudinal Motion for Maneuverable Aircraft......Page 78
3 Generation of a Representative Set of Training Data......Page 79
4 Semi-empirical Neural Network Model of Aircraft Longitudinal Motion......Page 80
References......Page 82
1 Introduction......Page 84
2 The Multi-agent Adaptive Fuzzy Neuronet for Dump Truck Fault's Short-Term Forecasts......Page 85
2.1 The Training Algorithms of the Multi-agent Adaptive Fuzzy Neuronet......Page 86
2.2 The Multi-agent Adaptive Fuzzy Neuronet......Page 88
3 Results......Page 89
References......Page 90
1.1 Relevance of the Problem......Page 91
1.2 Statement of the Problem......Page 92
2.1 Structure of the Faster R-CNN......Page 93
3 Experimental Researches......Page 94
4 Conclusions......Page 95
References......Page 96
1 Introduction......Page 97
3 Results......Page 99
References......Page 101
1 Introduction......Page 103
2 The Topological Data Analysis......Page 104
3 Setting the Problem......Page 105
4 Topological Invariants Calculated for a Traffic Intensity Sequence......Page 106
5 Building the Neural-Net Model of the Data......Page 108
References......Page 109
1 Introduction......Page 110
2 Using TAP in Image Recognition......Page 111
3 Formation of Feature Description of a Textured Image......Page 112
4 Computational Experiment......Page 113
References......Page 115
1 Introduction......Page 116
3 Stability of the Control System......Page 117
4 Upper Bound of Learning Rate Calculation......Page 118
5 Experimental Results......Page 119
References......Page 121
1 Introduction......Page 122
2 The Organization of the Intelligent Diagnostics of Mechatronic Complex Components......Page 123
3 Neural Network for Data Processing......Page 124
5 Conclusion......Page 127
References......Page 128
2 Materials and Methods......Page 129
3 Examined Approach......Page 132
References......Page 135
1 Introduction......Page 137
2.2 Initial Stages of Information Transformation......Page 138
2.3 Compression of Information by Granulation......Page 139
2.4 Classification of Objects as a Prototype Search......Page 140
3 Properties of the Model......Page 141
References......Page 142
1 Introduction......Page 144
3 Representation of the EEG Signals as Images......Page 145
4 Quality of Features Generated by the Convolutional Neural Network......Page 147
5 Conclusions......Page 148
References......Page 149
1 Introduction......Page 150
2 Semi-empirical Model of a Sagging Thread. Methods......Page 151
3 Calculation......Page 153
4 Conclusions......Page 154
References......Page 155
Cognitive Sciences and Adaptive Behavior......Page 157
1 Introduction......Page 158
2 Contrast of a Color Image......Page 159
3 Color Analog of Rayleigh Criterion......Page 165
References......Page 166
1 Introduction......Page 168
3 Results......Page 169
References......Page 172
1 Introduction......Page 174
2.1 General Scheme of the Model......Page 175
2.2 Description of the Iterative Process......Page 176
3 Results of Computer Simulation......Page 177
4 Conclusion......Page 179
References......Page 180
Neurobiology......Page 181
1 Introduction......Page 182
2 Materials and Methods......Page 184
3 Results......Page 185
4 Conclusion......Page 186
References......Page 187
1 Introduction......Page 189
2.2 Data Processing......Page 190
3 Conclusion......Page 193
References......Page 194
1 Introduction......Page 195
2 The Own Goals of the Individual Neuron......Page 196
3 The Functional Systems of the Neuron Involved in Synaptic Modulations in the Early Phase of LTP......Page 197
Acknowledgements......Page 199
References......Page 200
1 Introduction......Page 202
2.3 Hodgkin-Huxley-like Model of a Neuron......Page 203
3.1 Dynamic-Clamp Study of the Influence of NaP Current......Page 204
3.2 Effect of Persistent-Sodium Current in a Modeled Neuron......Page 205
References......Page 207

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