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Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence

6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13-15, 2001, Proceedings, Part I

  • Conference proceedings
  • © 2001

Overview

Editors:
  1. José Mira

    1. Departamento de Inteligencia Artificial Sanda del Rey, Universidad Nacional de Educación a Distancia, Madrid, Spain

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    You can also search for this editor in PubMed   Google Scholar

  2. Alberto Prieto

    1. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Granada, Spain

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2084)

Included in the following conference series:

Conference proceedings info: IWANN 2001.

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Table of contents (99 papers)

  1. Front Matter

    Pages I-XXVII
    Download chapter PDF

  2. Foundations of Connectionism and Biophysical Models of Neurons

    1. Dendrites: The Last-Generation Computers

      • O. Herreras, J. M. Ibarz, L. López-Aguado, P. Varona
      Pages 1-13

    2. Homogeneity in the Electrical Activity Pattern as a Function of Intercellular Coupling in Cell Networks

      • E. Andreu, R. Pomares, B. Soria, J. V. Sanchez-Andres
      Pages 14-20

    3. A Realistic Computational Model of the Local Circuitry of the Cuneate Nucleus

      • Eduardo Sroseanchez, Senrosesen Barro, Jorge Mariño, Antonio Canedo
      Pages 21-29

    4. Algorithmic Extraction of Morphological Statistics from Electronic Archives of Neuroanatomy

      • Ruggero Scorcioni, Giorgio A. Ascoli
      Pages 30-37

    5. What Can We Compute with Lateral Inhibition Circuits?

      • Josrosee Mira, Ana E. Delgado
      Pages 38-46

    6. Neuronal Models with Current Inputs

      • Jianfeng Fent
      Pages 47-54

    7. Decoding the Population Responses of Retinal Ganglions Cells Using Information Theory

      • J. M. Ferrández, M. Bongard, F. García de Quiros, J. A. Bolea, J. Ammermü, R. A. Normann et al.
      Pages 55-62

    8. Numerical Study of Effects of Co-transmission by Substance P and Acetylcholine on Synaptic Plasticity in Myenteric Neurons

      • Roustem Miftakov, James Christensen
      Pages 63-71

    9. Neurobiological Modeling of Bursting Response During Visual Attention

      • Reza Rajimehr, Leila Montaser Kouhsari
      Pages 72-80

    10. Sensitivity of Simulated Striate Neurons to Cross-Like Stimuli Based on Disinhibitory Mechanism

      • Konstantin A. Saltykov, Igor A. Shevelev
      Pages 81-86

    11. Synchronisation Mechanisms in Neuronal Networks

      • Santi Chillemi, Michele Barbi, Angelo Di Garbo
      Pages 87-94

    12. Detection of Oriented Repetitive Alternating Patterns in color Images

      • Tino Lourens, Hiroshi G. Okuno, Hiroaki Kitano
      Pages 95-107

    13. Synchronization in Brain — Assessment by Electroencephalographic Signals

      • Ernesto Pereda, Joydeep Bhattacharya
      Pages 108-116

    14. Strategies for the Optimization of Large Scale Networks of Integrate and Fire Neurons

      • Manuel A. Sánchez-Montañás
      Pages 117-125

  3. Structural and Functional Models of Neurons

    1. A Neural Network Model of Working Memory (Processing of “What” and “Where” Information)

      • Tetsuto Minami, Toshio Inui
      Pages 126-133

    2. Orientation Selectivity of Intracortical Inhibitory Cells in the Striate Visual Cortex: A Computational Theory and a Neural Circuitry

      • Medhi N. Shirazi
      Pages 134-141

    3. Interpreting Neural Networks in the Frame of the Logic of Lukasiewicz

      • Claudio Moraga, Luis Salinas
      Pages 142-149

    4. Time-Dispersive Effects in the J. Gonzalo’s Research on Cerebral Dynamics

      • Isabel Gonzalo, Miguel A. Porras
      Pages 150-157

    5. Verifying Properties of Neural Networks

      • Pedro Rodrigues, J. Félix Costa, Hava T. Siegelmann
      Pages 158-165
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Other volumes

  1. Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence

  2. Bio-Inspired Applications of Connectionism

Keywords

About this book

Underlying most of the IWANN calls for papers is the aim to reassume some of the motivations of the groundwork stages of biocybernetics and the later bionics formulations and to try to reconsider the present value of two basic questions. The?rstoneis:“Whatdoesneurosciencebringintocomputation(thenew bionics)?” That is to say, how can we seek inspiration in biology? Titles such as “computational intelligence”, “arti?cial neural nets”, “genetic algorithms”, “evolutionary hardware”, “evolutive architectures”, “embryonics”, “sensory n- romorphic systems”, and “emotional robotics” are representatives of the present interest in “biological electronics” (bionics). Thesecondquestionis:“Whatcanreturncomputationtoneuroscience(the new neurocybernetics)?” That is to say, how can mathematics, electronics, c- puter science, and arti?cial intelligence help the neurobiologists to improve their experimental data modeling and to move a step forward towards the understa- ing of the nervous system? Relevant here are the general philosophy of the IWANN conferences, the sustained interdisciplinary approach, and the global strategy, again and again to bring together physiologists and computer experts to consider the common and pertinent questions and the shared methods to answer these questions.

Editors and Affiliations

  • Departamento de Inteligencia Artificial Sanda del Rey, Universidad Nacional de Educación a Distancia, Madrid, Spain

    José Mira

  • Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Granada, Spain

    Alberto Prieto

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