Since the beginning of the 1980's, a lot of news approaches of biomimetic inspiration have been defined and developed for imitating the brain behavior, for modeling non linear phenomenon, for providing new hardware architectures, for solving hard problems. They are named Neural Networks, Multilayer Perceptrons, Genetic algorithms, Cellular Automates, Self-Organizing maps, Fuzzy Logic, etc. They can be summarized by the word of Connectionism, and consist of an interdisciplinary domain between neuroscience, cognitive science and engineering. First they were applied in computer sciences, engineering, biological models, pattern recognition, motor control, learning algorithms, etc. But rapidly, it appeared that these methods could be of great in...
Computational models are useful tools for exploring the nature of human cognitive processes. In part...
This thesis explores the use of artificial neural networks for modelling cognitive processes. It pre...
International audienceConnectionist Models of Learning, Development and Evolution..
Since the beginning of the 1980's, a lot of news approaches of biomimetic inspiration have been defi...
Connectionist approaches to cognitive modeling make use of large networks of simple computational u...
This article considers how connectionist modeling can contrib-ute to understanding ofhuman cognition...
Abstract – This presentation gives a brief introduction to Evolving Connectionist Systems (ECOS) and...
Abstract: "Connectionism is a method of modeling cognition as the interaction of neuron-like units. ...
The aim of this book is to describe the types of computation that can be performed by biologically p...
One of the fundamental methodological categories is the notion of theory. However, it is hard to det...
The paper describes what evolving processes are and presents a computational model called evolving c...
The employment of a particular class of computer programs known as "connectionist networks" to model...
This chapter provides an introduction and motivates the leading thread of the following ten chapters...
The neural computational approach to cognitive and psychological processes is relatively new. Howeve...
This paper follows the 25 years of development of methods and systems for knowledge-based neural net...
Computational models are useful tools for exploring the nature of human cognitive processes. In part...
This thesis explores the use of artificial neural networks for modelling cognitive processes. It pre...
International audienceConnectionist Models of Learning, Development and Evolution..
Since the beginning of the 1980's, a lot of news approaches of biomimetic inspiration have been defi...
Connectionist approaches to cognitive modeling make use of large networks of simple computational u...
This article considers how connectionist modeling can contrib-ute to understanding ofhuman cognition...
Abstract – This presentation gives a brief introduction to Evolving Connectionist Systems (ECOS) and...
Abstract: "Connectionism is a method of modeling cognition as the interaction of neuron-like units. ...
The aim of this book is to describe the types of computation that can be performed by biologically p...
One of the fundamental methodological categories is the notion of theory. However, it is hard to det...
The paper describes what evolving processes are and presents a computational model called evolving c...
The employment of a particular class of computer programs known as "connectionist networks" to model...
This chapter provides an introduction and motivates the leading thread of the following ten chapters...
The neural computational approach to cognitive and psychological processes is relatively new. Howeve...
This paper follows the 25 years of development of methods and systems for knowledge-based neural net...
Computational models are useful tools for exploring the nature of human cognitive processes. In part...
This thesis explores the use of artificial neural networks for modelling cognitive processes. It pre...
International audienceConnectionist Models of Learning, Development and Evolution..