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» Explanation-Based Neural Network Learning for Robot Control
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HIS
2008
15 years 4 months ago
Neural Plasticity and Minimal Topologies for Reward-Based Learning
Artificial Neural Networks for online learning problems are often implemented with synaptic plasticity to achieve adaptive behaviour. A common problem is that the overall learning...
Andrea Soltoggio
152
Voted
IJON
2008
152views more  IJON 2008»
15 years 1 months ago
Topos: Spiking neural networks for temporal pattern recognition in complex real sounds
This article depicts the approach used to build the Topos application, a simulation of two-wheel robots able to discern real complex sounds. Topos is framed in the nouvelle concep...
Pablo González-Nalda, Blanca Cases
CDC
2008
IEEE
147views Control Systems» more  CDC 2008»
15 years 10 months ago
Clustering neural spike trains with transient responses
— The detection of transient responses, i.e. non– stationarities, that arise in a varying and small fraction of the total number of neural spike trains recorded from chronicall...
John D. Hunter, Jianhong Wu, John G. Milton
ECAL
2005
Springer
15 years 9 months ago
(Co)Evolution of (De)Centralized Neural Control for a Gravitationally Driven Machine
Using decentralized control structures for robot control can offer a lot of advantages, such as less complexity, better fault tolerance and more flexibility. In this paper the ev...
Steffen Wischmann, Martin Hülse, Frank Pasema...
136
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KI
2008
Springer
15 years 3 months ago
Learning Dance Movements by Imitation: A Multiple Model Approach
Abstract. Imitation learning is an intuitive and easy way of programming robots. Instead of specifying motor commands, you simply show the robot what to do. This paper presents a m...
Axel Tidemann, Pinar Öztürk