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Connection Science, Volume 11
Volume 11, Number 1, March 1999
- Paul Rodríguez:
A Recurrent Neural Network that Learns to Count. 5-40 - Martin J. Adamson:
B-RAAM: A Connectionist Model which Develops Holistic Internal Representations of Symbolic Structures. 41-71 - Chang N. Zhang:
Single Neuron Rational Model of Arithmetic and Logic Operations. 73-90 - Michael J. Healy:
A Topological Semantics for Rule Extraction with Neural Networks. 91-113
Volume 11, Number 2, June 1999
- Stefano Nolfi, Jun Tani:
Extracting Regularities in Space and Time Through a Cascade of Prediction Networks: The Case of a Mobile Robot Navigating in a Structured Environment. 125-148 - Denis Mareschal, Thomas R. Shultz:
Development of Children's Seriation: A Connectionist Approach. 149-186 - Michael Gasser, Douglas Eck, Robert F. Port:
Meter as Mechanism: A Neural Network Model that Learns Metrical Patterns. 187-216
Volume 11, Numbers 3-4, December 1999
- Carme Torras:
Guest Editorial: On Adaptive Robots. 221-224 - David Filliat, Jérôme Kodjabachian, Jean-Arcady Meyer:
Evolution of Neural Controllers for Locomotion and Obstacle Avoidance in a Six-legged Robot. 225-242 - Edmondo Trentin, Roldano Cattoni:
Learning Perception for Indoor Robot Navigation with a Hybrid Hidden Markov Model/Recurrent Neural Networks Approach. 243-265 - Juan Miguel Santos, Claude F. Touzet:
Dynamic Update of the Reinforcement Function During Learning. 267-289 - Carlos M. O. Valente, Aluízio F. R. Araújo, Glauco Augusto de Paula Caurin, A. Schammass:
A Neural Gripper for Arbitrary Object Grasping. 291-316 - Giorgio M. Grasso, Michael Recce:
Towards Genetically Evolved Dynamic Control for Quadruped Locomotion. 317-330 - M. Anthony Lewis, Lucia S. Simo:
Elegant Stepping: A Model of Visually Triggered Gait Adaptation. 331-344 - Dean K. McNeill, Howard C. Card:
Competitive Learning and its Application in Adaptive Vision for Autonomous Mobile Robots. 345-357 - Aude Billard, Auke Jan Ijspeert, Alcherio Martinoli:
A Multi-robot System for Adaptive Exploration of a Fast-changing Environment: Probabilistic Modeling and Experimental Study. 359-379
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