Competition and Cooperation in Neural Nets

I. An Opening Perspective.- 1. Competitive and Cooperative Aspects in Dynamics of Neural Excitation and Self-Organization.- II. Reaction-Diffusion Equations.- 2. Sigmoidal Systems and Layer Analysis.- 3. Asymptotic Behavior of Stationary Homogeneous Neuronal Nets.- 4. Aggregation and Segregation Phenomena in Reaction-Diffusion Equations.- III. Single-Neuron and Stochastic Models.- 5. Nerve Pulse Interactions.- 6. Micronetworks in Nerve Cells.- 7. Role and Use of Noise in Biological Systems.- 8. Stochastic, Quantal Membrane Conductances and Neuronal Function.- 9. Diffusion Approximations and Computational Problems for Single Neurons' Activity.- 10. Periodic Pulse Sequences Generated by an Analog Neuron Model.- 11. On a Mathematical Neuron Model.- IV. Oscillations in Neural Networks.- 12. Control of Distributed Neural Oscillators.- 13. Characteristics of Neural Network with Uniform Structure.- V. Development and Plasticity of the Visual Systems.- 14. Systems Matching and Topographic Maps: The Branch-Arrow Model (BAM).- 15. Differential Localization of Plastic Synapses in the Visual Cortex of the Young Kitten: Evidence for Guided Development of the Visual Cortical Networks.- 16. Self-Organization of Neural Nets with Competitive and Cooperative Interaction.- 17. A Simple Paradigm for the Self-Organized Formation of Structured Feature Maps.- 18. Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition.- 19. On the Spontaneous Emergence of Neuronal Schemata.- 20. Associative and Competìve Principles of Learning and Development.- VI. Sensori-Motor Transformations and Learning.- 21. Modelling Neural Mechanisms of Visuomotor Coordination in Frog and Toad.- 22. Two-Dimensional Model of Retinal-Tectal-Pretectal Interactions for theControl of Prey-Predator Recognition and Size Preference in Amphibia.- 23. Tensor Theory of Brain Function:The Cerebellum as a Space-Time Metric.- 24. Mechanisms of Motor Learning.- 25. Dynamic and Plastic Properties of the Brain Stem Neuronal Networks as the Possible Neuronal Basis of Learning and Memory.