An Introduction to Neurocomputing

An Introduction to Neurocomputing

Juan A. Barceló (Universitat Autònoma de Barcelona, Spain)
Copyright: © 2009 |Pages: 48
DOI: 10.4018/978-1-59904-489-7.ch004
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Abstract

Let’s build an automated archaeologist! It is not an easy task. We need a highly complex, nonlinear, and parallel information-processing “cognitive core” able to explain what the robot sees, in terms of causal factors, which not always have an observable nature. Of course, such a “cognitive core” should not run like a human brain. After all, automated archaeologists do the same tasks as “human archaeologists,” but not necessary in the same way. Nevertheless, there is some similitude in the basic mechanism. My suggestion is that an archaeologist, human or “artificial,” will perceive archaeological data and, using some basic principles of learning, as those presented in previous chapter, will develop ways of encoding these data to make sense of perceived world. Consequently, we may try to build our artificial archaeologist based on the idea of learning and the ability to adapt flexibly epistemic actions to different archaeological problems waiting for a solution.
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Simulating The Brain

Let’s build an automated archaeologist!

It is not an easy task. We need a highly complex, nonlinear, and parallel information-processing “cognitive core” able to explain what the robot sees, in terms of causal factors, which not always have an observable nature.

Of course, such a “cognitive core” should not run like a human brain. After all, automated archaeologists do the same tasks as “human archaeologists,” but not necessary in the same way. Nevertheless, there is some similitude in the basic mechanism. My suggestion is that an archaeologist, human or “artificial,” will perceive archaeological data and, using some basic principles of learning, as those presented in previous chapter, will develop ways of encoding these data to make sense of perceived world. Consequently, we may try to build our artificial archaeologist based on the idea of learning and the ability to adapt flexibly epistemic actions to different archaeological problems waiting for a solution.

How much should be programmed in its final form into such a cognitive core and how much will have to be learnt by interacting with some environment, including teachers and other agents? Projects aiming to develop intelligent systems on the basis of powerful and general learning mechanisms start from something close to a “Tabula rasa,” however, they risk being defeated by explosive search spaces requiring evolutionary time-scales for success. Biological evolution enables animals to avoid this problem by providing large amounts of “innate“ information in the genomes of all species. In the case of humans, this seems to include meta-level information about what kinds of things are good to learn, helping to drive the learning processes as well as specific mechanisms, forms of representation, and architectures to enable them to work. Is it possible to use these ideas for building an “intelligent” machine?

Like its human counterpart, the cognitive core of our automated archaeologist should be made of specialized cells called neurons (Figure 1). Artificial and biological neurons are relatively similar, and both have the same parts, also called the cell body, axon, synapse, and dendrite (Bechtel & Abrahamson, 1991; Dawson, 2004; Ellis & Humphreys, 1999; O’Reilly & Munakata, 2000; Quinlan, 1991).

Figure 1.

Schematic representation of a neuron

Each neuron connects as well as accepts connections from many other neurons, configuring a network of neurons. Those connections are implemented by means of dendrites, while synapses are a gateway linked to dendrites coming from other neurons.

Complete Chapter List

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Table of Contents
Foreword
Jean-Claude Gardin
Acknowledgment
Chapter 1
Juan A. Barceló
The task of this automated archaeologist will be to assign to any artifact, represented by some features, visual or not, some meaning or explanatory... Sample PDF
"Automated" Archaeology: A Useless Endeavor, an Impossible Dream, or Reality?
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Chapter 2
Juan A. Barceló
When a specific goal is blocked, we have a problem. When we know ways round the block or how to remove it, we have less a problem. In our case, the... Sample PDF
Problem Solving in the Brain and by the Machine
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Chapter 3
Juan A. Barceló
Inverse problems are among the most challenging in computational and applied science and have been studied extensively (Bunge, 2006; Hensel, 1991;... Sample PDF
Computer Systems that Learn
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Chapter 4
Juan A. Barceló
Let’s build an automated archaeologist! It is not an easy task. We need a highly complex, nonlinear, and parallel information-processing “cognitive... Sample PDF
An Introduction to Neurocomputing
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Chapter 5
Juan A. Barceló
As we have discussed in previous chapters, an artificial neural network is an information-processing system that maps a descriptive feature vector... Sample PDF
Visual and Non-Visual Analysis in Archaeology
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Chapter 6
Juan A. Barceló
In order to be able to acquire visual information, our automated “observer” is equipped with range and intensity sensors. The former acquire range... Sample PDF
Shape Analysis in Archaeology
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Chapter 7
Juan A. Barceló
In this section, we will consider archaeological textures as the archaeological element’s surface attributes having either tactile or visual... Sample PDF
Texture and Compositional Analysis in Archaeology
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Chapter 8
Spatiotemporal Analysis  (pages 256-296)
Juan A. Barceló
As we have suggested many times throughout the book, the general form of an archaeological problem seems to be “why an archaeological site is the... Sample PDF
Spatiotemporal Analysis
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Chapter 9
Juan A. Barceló
It is obvious that answering the first question is a condition to solve the second. In the same way as human archaeologists, the automated... Sample PDF
An Automated Approach to Historical and Social Explanation
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Chapter 10
Juan A. Barceló
We have already argued that an automated archaeologist cannot understand past social actions by enumerating every possible outcome of every possible... Sample PDF
Beyond Science Fiction Tales
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