New Trends in Neural Computation

1. Front Matter

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2. Biophysics of neural computation

   • K. N. Leibovic

   Pages 1-11

3. Integrated learning in rana computatrix

   • Fernando J. Corbacho, Michael A. Arbib

   Pages 12-19

4. A model for centering visual stimuli through adaptive value learning

   • Murciano A., Zamora J., Reviriego M.

   Pages 20-23

5. A model for the development of neurons selective to visual stimulus size

   • M. A. Andrade, F. Morán

   Pages 24-29

6. An invariant representation mechanism after presynaptic inhibition

   • Roberto Moreno-Díaz Jr., Olga Bolívar Toledo

   Pages 30-36

7. The pancreatic B-cell as a voltage-controlled oscillator

   • J. V. Sánchez-Andrés, B. Soria

   Pages 37-42

8. Approximation of the solution of the dendritic cable equation by a small series of coupled differential equations

   • Jaap Hoekstra

   Pages 43-48

9. A neural network model inspired in global appreciations about the thalamic reticular nucleus and cerebral cortex connectivity

   • Javier Ropero Peláez

   Pages 49-54

10. Towards more realistic self contained models of neurons: High-order, recurrence and local learning

    • J. Mira, A. E. Delgado, J. R. Alvarez, A. P. de Madrid, M. Santos

    Pages 55-62

11. McCulloch's neurons revisited

    • Robert J. Scott

    Pages 63-67

12. Biologically motivated approach to face recognition

    • N. Petkov, P. Kruizinga, T. Lourens

    Pages 68-77

13. Learning by reinforcement: A psychobiological model

    • Francisco J. Vico, F. Sandoval, J. Almaraz

    Pages 78-83

14. A neural state machine for iconic language representation

    • Igor Aleksander, Helen Morton

    Pages 84-89

15. Variable binding using serial order in recurrent neural networks

    • Joan Lòpez-Moliner, Josep Ma Sopena

    Pages 90-95

16. Region of influence (ROI) networks. Model and implementation

    • F. Castillo, J. Cabestany, J. M. Moreno

    Pages 96-101

17. A node splitting algorithm that reduces the number of connections in a Hamming distance classifying network

    • H. Hüning

    Pages 102-107

18. A high order neural model

    • Lopez Aligué Francisco J., Acevedo Sotoca Ma Isabel, Jaramillo Moran Miguel A.

    Pages 108-113

19. Higher-order networks for the optimization of block designs

    • Pau Bofill, Carme Torras

    Pages 114-118

20. Neural Bayesian classifier

    • Christian Jutten, Pierre Comon

    Pages 119-124

Neural computation arises from the capacity of nervous tissue to process information and accumulate knowledge in an intelligent manner. Conventional computational machines have encountered enormous difficulties in duplicatingsuch functionalities. This has given rise to the development of Artificial Neural Networks where computation is distributed over a great number of local processing elements with a high degree of connectivityand in which external programming is replaced with supervised and unsupervised learning. The papers presented in this volume are carefully reviewed versions of the talks delivered at the International Workshop on Artificial Neural Networks (IWANN '93) organized by the Universities of Catalonia and the Spanish Open University at Madrid and held at Barcelona, Spain, in June 1993. The 111 papers are organized in seven sections: biological perspectives, mathematical models, learning, self-organizing networks, neural software, hardware implementation, and applications (in five subsections: signal processing and pattern recognition, communications, artificial vision, control and robotics, and other applications).

• Artificial Neural Networks
• C++ programming language
• Fuzzy
• Künstliche Neuronale Netze
• Neural Architectures
• Neural Computation
• Neural Software
• Neuronale Architekturen
• Neuronale Software
• Selbstorganisierende Neuronale Netze
• classification
• modeling
• object recognition
• robot
• robotics

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