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IJCSS
2007
122views more  IJCSS 2007»
13 years 5 months ago
Artificial Neural Network Type Learning with Single Multiplicative Spiking Neuron
In this paper, learning algorithm for a single multiplicative spiking neuron (MSN) is proposed and tested for various applications where a multilayer perceptron (MLP) neural netwo...
Deepak Mishra, Abhishek Yadav, Sudipta Ray, Prem K...
ICTAI
2003
IEEE
13 years 10 months ago
Q-Concept-Learning: Generalization with Concept Lattice Representation in Reinforcement Learning
One of the very interesting properties of Reinforcement Learning algorithms is that they allow learning without prior knowledge of the environment. However, when the agents use al...
Marc Ricordeau
GECCO
2008
Springer
261views Optimization» more  GECCO 2008»
13 years 6 months ago
SSNNS -: a suite of tools to explore spiking neural networks
We are interested in engineering smart machines that enable backtracking of emergent behaviors. Our SSNNS simulator consists of hand-picked tools to explore spiking neural network...
Heike Sichtig, J. David Schaffer, Craig B. Laramee
IJCNN
2007
IEEE
13 years 11 months ago
Spectral Clustering of Synchronous Spike Trains
— In this paper a clustering algorithm that learns the groups of synchronized spike trains directly from data is proposed. Clustering of spike trains based on the presence of syn...
António R. C. Paiva, Sudhir Rao, Il Park, J...
EAAI
2006
123views more  EAAI 2006»
13 years 5 months ago
Imitation learning with spiking neural networks and real-world devices
This article is about a new approach in robotic learning systems. It provides a method to use a real-world device that operates in real-time, controlled through a simulated recurr...
Harald Burgsteiner