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From Synapses to Rules

Discovering Symbolic Rules from Neural Processed Data

  • Book
  • © 2002

Overview

Editors:
  1. Bruno Apolloni

    1. Department of Information Science, University of Milan, Milan, Italy

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  2. Franz Kurfess

    1. Department of Computer Science, California Polytechnic State University, San Luis Obispo, USA

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    You can also search for this editor in PubMed   Google Scholar

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Table of contents (18 chapters)

  1. Front Matter

    Pages i-xxiii
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  2. The Theoretical Bases of Learning

    1. Front Matter

      Pages 1-4
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    2. The Statistical Bases of Learning

      • Bruno Apolloni, Simone Bassis, Sabrina Gaito, Dario Malchiodi
      Pages 5-40

    3. PAC Meditation on Boolean Formulas

      • Bruno Apolloni, Stefano Baraghini, Giorgio Palmas
      Pages 41-60

    4. Learning Regression Functions

      • Bruno Apolloni, Sabrina Gaito, Domenico Iannizzi, Dario Malchiodi
      Pages 61-73

    5. Cooperative Games in a Stochastic Environment

      • Bruno Apolloni, Simone Bassis, Sabrina Gaito, Dario Malchiodi
      Pages 75-86

    6. If-Then-Else and Rule Extraction from Two Sets of Rules

      • Daniele Mundici
      Pages 87-108

    7. Extracting Interpretable Fuzzy Knowledge from Data

      • Corrado Mencar
      Pages 109-116

    8. Fuzzy Methods for Simplifying a Boolean Formula Inferred from Examples

      • Bruno Apolloni, Dario Malchiodi, Christos Orovas, Anna Maria Zanaboni
      Pages 117-128

  3. Physical Aspects of Learning

    1. Front Matter

      Pages 135-137
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    2. On Mapping and Maps in the Central Nervous System

      • Gabriele E. M. Biella
      Pages 139-164

    3. Molecular Basis of Learning and Memory: Modelling Based on Receptor Mosaics

      • L. F. Agnati, L. M. Santarossa, F. Benfenati, M. Ferri, A. Morpurgo, B. Apolloni et al.
      Pages 165-195

    4. Physiological and Logical Brain Functionalities: A Hypothesis for a Self-Referential Brain Activity

      • Bruno Apolloni, Anna Morpurgo, Luigi F. Agnati
      Pages 197-218

    5. Modeling of Spontaneous Bursting Activity Observed in In-Vitro Neural Networks

      • Maria Marinaro, Silvia Scarpetta
      Pages 219-228

    6. The Importance of Data for Training Intelligent Devices

      • Anna Esposito
      Pages 229-250

    7. Learning and Checking Confidence Regions for the Hazard Function of Biomedical Data

      • Bruno Apolloni, Sabrina Gaito, Dario Malchiodi
      Pages 251-260

  4. Systems that Bridge the Gap

    1. Front Matter

      Pages 273-274
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    2. Integrating Symbol-Oriented and Sub-Symbolic Reasoning Methods into Hybrid Systems

      • Franz J. Kurfess
      Pages 275-292

    3. From the Unconscious to the Conscious

      • Ron Sun
      Pages 293-313

    4. On Neural Networks, Connectionism and Brain-Like Learning

      • Asim Roy
      Pages 315-342
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Keywords

About this book

One high-level ability of the human brain is to understand what it has learned. This seems to be the crucial advantage in comparison to the brain activity of other primates. At present we are technologically almost ready to artificially reproduce human brain tissue, but we still do not fully understand the information processing and the related biological mechanisms underlying this ability. Thus an electronic clone of the human brain is still far from being realizable. At the same time, around twenty years after the revival of the connectionist paradigm, we are not yet satisfied with the typical subsymbolic attitude of devices like neural networks: we can make them learn to solve even difficult problems, but without a clear explanation of why a solution works. Indeed, to widely use these devices in a reliable and non elementary way we need formal and understandable expressions of the learnt functions. of being tested, manipulated and composed with These must be susceptible other similar expressions to build more structured functions as a solution of complex problems via the usual deductive methods of the Artificial Intelligence. Many effort have been steered in this directions in the last years, constructing artificial hybrid systems where a cooperation between the sub symbolic processing of the neural networks merges in various modes with symbolic algorithms. In parallel, neurobiology research keeps on supplying more and more detailed explanations of the low-level phenomena responsible for mental processes.

Editors and Affiliations

  • Department of Information Science, University of Milan, Milan, Italy

    Bruno Apolloni

  • Department of Computer Science, California Polytechnic State University, San Luis Obispo, USA

    Franz Kurfess

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