Plausible Neural Networks for Biological Modelling / Edition 1

Plausible Neural Networks for Biological Modelling / Edition 1

by H.A. Mastebroek

This book introduces two recent developments in neural network methodology, namely recurrency in the architecture and the use of spiking or integrate-and-fire neurons. The neuro-anatomical processes of synapse modification during development, training, and memory formation are discussed as realistic bases for weight-adjustment in neural networks. Early chapters lay a… See more details below


This book introduces two recent developments in neural network methodology, namely recurrency in the architecture and the use of spiking or integrate-and-fire neurons. The neuro-anatomical processes of synapse modification during development, training, and memory formation are discussed as realistic bases for weight-adjustment in neural networks. Early chapters lay a foundation for the application of neural networks as models for the various biological phenomena that are treated in later chapters, detailing various neural network models of sensory and motor control tasks. Mastebroek teaches in the Department of Neurobiophysics and Biomedical Engineering at the University of Groningen, The Netherlands. Vos teaches developmental neurology at the same institution. Annotation c. Book News, Inc., Portland, OR (

Product Details

Springer Netherlands
Publication date:
Mathematical Modelling: Theory and Applications Series, #13
Edition description:
Product dimensions:
9.21(w) x 6.14(h) x 0.69(d)

Table of Contents

Part IFundamentals
1Biological Evidence for Synapse Modification Relevant for Neural Network Modelling
2.The Synapse11
3.Long Term Potentiation13
4.Two Characteristic Types of Experiment15
4.1Food Discrimination Learning in Chicks15
4.2Electrical Stimulation of Nervous Cell Cultures18
References and Further Reading20
2What is Different with Spiking Neurons?
1.Spikes and Rates23
1.1Temporal Average-Spike Count24
1.2Spatial Average-Population Activity26
1.3Pulse Coding-Correlations and Synchrony27
2.'Integrate and Fire' Model28
3.Spike Response Model30
4.Rapid Transients33
5.Perfect Synchrony36
6.Coincidence Detection38
7.Spike Time Dependent Hebbian Learning39
8.Temporal Coding in the Auditory System42
3Recurrent Neural Networks: Properties and Models
2.Universality of Recurrent Networks52
2.1Discrete Time Dynamics52
2.2Continuous Time Dynamics54
3.Recurrent Learning Algorithms for Static Tasks56
3.1Hopfield Network56
3.2Boltzmann Machines58
3.3Recurrent Backpropagation Proposed by Fernando Pineda60
4.Recurrent Learning Algorithms for Dynamical Tasks63
4.1Backpropagation Through Time63
4.2Jordan and Elman Networks64
4.3Real Time Recurrent Learning (RTRL)65
4.3.1Continuous Time RTRL65
4.3.2Discrete Time RTRL66
4.3.3Teacher Forced RTRL67
4.3.4Considerations about the Memory Requirements67
4.4Time Dependent Recurrent Backpropagation (TDRBP)68
5.Other Recurrent Algorithms69
4A Derivation of the Learning Rules for Dynamic Recurrent Neural Networks
1.A Look into the Calculus of Variations75
2.Conditions of Constraint77
3.Applications in Physics: Lagrangian and Hamiltonian Dynamics78
4.Generalized Coordinates80
5.Application to Optimal Control Systems82
6.Time Dependent Recurrent Backpropagation: Learning Rules85
Part IIApplications to Biology
5Simulation of the Human Oculomotor Integrator Using a Dynamic Recurrent Neural Network
2.The Different Neural Integrator Models95
3.The Biologically Plausible Improvements99
3.1Fixed Sign Connection Weights100
3.2Artificial Distance between Inter-Neurons101
3.3Numerical Discretization of the Continuous Time Model101
3.4The General Supervisor102
3.5The Modified Network103
4.Emergence of Clusters104
4.2Mathematical Identification of Clusters106
4.3Characterization of the Clustered Structure106
4.4Particular Locations110
5.Discussion and Conclusion110
6Pattern Segmentation in an Associative Network of Spiking Neurons
1.The Binding Problem117
2.Spike Response Model118
3.Simulation Results121
3.1Pattern Retrieval and Synchronization123
3.2Pattern Segmentation124
3.3Context Sensitive Binding in a Layered Network with Feedback126
4.Related Work129
4.1Segmentation with LEGION129
4.2How about Real Brains?130
7Cortical Models for Movement Control
1.Introduction: Constraints on Modeling Biological Neural Networks135
2.Cellular Firing Patterns in Monkey Cortical Areas 4 and 5137
3.Anatomical Links between Areas 4 and 5, Spinal Motoneurons, and Sensory Systems140
4.How Insertion of a Time Delay can Create a Niche for Deliberation141
5.A Volition-Deliberation Nexus and Voluntary Trajectory Generation142
6.Cortical-Subcortical Cooperation for Deliberation and Task-Dependent Configuration146
7.Cortical Layers, Neural Population Codes, and Posture-Dependent Recruitment of Muscle Synergies150
8.Trajectory Generation in Handwriting and Viapoint Movements151
9.Satisfying Constraints of Reaching to Intercept or Grasp155
10.Conclusions: Online Action Composition by Cortical Circuits156
8Implications of Activity Dependent Processes in Spinal Cord Circuits for the Development of Motor Control; a Neural Network Model
2.Sensorimotor Development165
3.Reflex Contributions to Joint Stiffness166
4.The Model167
4.1Neural Model168
4.2Musculo-Skeletal Model170
4.3Muscle Model172
4.4Sensory Model173
4.5Model Dynamics174
5.2Neural Control Properties177
5.3Perturbation Experiments179
9Cortical Maps as Topology-Representing Neural Networks Applied to Motor Control: Articulatory Speech Synthesis
1.Lateral Connections in Cortical Maps190
2.A Neural Network Model191
3.Spatial Maps as Internal Representations for Motor Planning193
3.1Dynamical Behavior of Spatial Maps194
3.2Function Approximation by Interconnected Maps196
3.3Dynamical Inversion199
4.Application of Cortical Maps to Articulatory Speech Synthesis200
4.1Cortical Control of Speech Movements202
4.2An Experimental Study203
4.2.1The Training Procedure204
4.2.2Field Representation of Phonemic Targets208
4.2.3Non-Audible Gestures and Compensation211
4.2.4Generation of VVV ... Sequences211
10Line and Edge Detection by Curvature-Adaptive Neural Networks
2.Biological Constraints223
3.Construction of the Gabor Filters224
4.The One-Dimensional Case224
5.The Two-Dimensional Case225
6.Simple Detection Scheme225
7.An Extended Detection Scheme226
8.Intermezzo: A Multi-Scale Approach230
9.Advanced Detection Scheme231
10.Biological Plausibility of the Adaptive Algorithm233
11.Conclusion and Discussion235
11Path Planning and Obstacle Avoidance Using a Recurrent Neural Network
2.Problem Description242
3.Task Descriptions243
3.2Fusing the Representations into a Neuronal Map245
3.3Path Planning and Heading Decision246

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