From Synapses to Rules

Discovering Symbolic Rules from Neural Processed Data

By: Bruno Apolloni (Editor), Franz Kurfess (Editor)

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Hardcover | 31 October 2002

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416 Pages

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25.4 x 16.51 x 3.18

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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.

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Non-Fiction Computing & I.T.Computer Science Mathematical Theory of Computation Artificial Intelligence Neural Networks & Fuzzy Systems Science Physics Mathematical Physics

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Paperback

Published: 9th November 2012

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The Theoretical Bases of Learning	p. 1
The Statistical Bases on Learning	p. 5
PAC Meditation on Boolean Formulas	p. 41
Learning Regression Functions	p. 61
Cooperative Games in a Stochastic Environment	p. 75
If-Then-Else and Rule Extraction from Two Sets of Rules	p. 87
Extracting Interpretable Fuzzy Knowledge from Data	p. 109
Fuzzy Methods for Simplifying a Boolean Formula Inferred from Examples	p. 117
Physical Aspects of Learning	p. 135
On Mapping and Maps in the Central Nervous System	p. 139
Molecular Basis of Learning and Memory: Modelling Based on Receptor Mosaics	p. 165
Physiological and Logical Brain Functionalities: A Hypothesis for a Self-Referential Brain Activity	p. 197
Modeling of Spontaneous Bursting Activity Observed in In-Vitro Neural Networks	p. 219
The Importance of Data for Training Intelligent Devices	p. 229
Learning and Checking Confidence Regions for the Hazard Function of Biomedical Data	p. 251
Systems that Bridge the Gap	p. 273
Integrating Symbol-Oriented and Sub-Symbolic Reasoning Methods into Hybrid Systems	p. 275
From the Unconscious to the Conscious	p. 293
On Neural Networks, Connectionism and Brain-Like Learning	p. 315
Adaptive Computation in Data Structures and Webs	p. 343
IUANT: An Updating Method for Supervised Neural Structures	p. 363
Conclusions	p. 371
References	p. 372
Index	p. 385
Table of Contents provided by Blackwell. All Rights Reserved.

From Synapses to Rules

Discovering Symbolic Rules from Neural Processed Data

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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