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Hiroaki Kitano
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Journal Articles
Publisher: Journals Gateway
Artificial Life (1998) 4 (2): 141–156.
Published: 01 April 1998
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The soil nematode Caenorhabditis Elegans (C. elegans) is the most investigated of all multicellular organisms. Since the proposal to use it as a model organism, a series of research projects have been undertaken, investigating various aspects of this organism. As a result, the complete cell lineage, neural circuitry, and various genes and their functions have been identified. The complete C. elegans DNA sequencing and gene expression mapping for each cell at different times during embryogenesis will be identified in a few years. Given the abundance of collected data, we believe that the time is ripe to introduce synthetic models of C. elegans to further enhance our understanding of the underlying principles of its development and behavior. For this reason, we have started the Perfect C. elegans Project, which aims to produce ultimately a complete synthetic model of C. elegans ' cellular structure and function. This article describes the goal, the approach, and the initial results of the project.
Journal Articles
Publisher: Journals Gateway
Artificial Life (1998) 4 (1): iii.
Published: 01 January 1998
Journal Articles
Publisher: Journals Gateway
Artificial Life (1994) 2 (1): 79–99.
Published: 01 October 1994
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This article reports on a simple neurogenesis model that is combined with evolutionary computation. Because the integration of an evolutionary process with neural networks is such an exciting field of study, with the promise of discovering new computational models and, possibly, providing novel biological insights, much research has been conducted in this area. However, only a few studies have incorporated a development stage, and none have modeled metabolism and other chemical reactions in a consistent manner. In this article, we present a simple model of neurogenesis and cell differentiation that combines evolutionary computing, metabolism, development, and neural networks. The model represents an evolutionary large-scale chaos as a mathematical foundation. An evolutionary large-scale chaos is a large-scale chaos whose map functions change through evolutionary computing. Experiments indicate that the model is capable of evolving and growing large neural networks, and exhibits phenomena analogous to cell differentiation.