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CDC
2008
IEEE
147views Control Systems» more  CDC 2008»
14 years 2 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
NN
2006
Springer
13 years 7 months ago
Neural systems implicated in delayed and probabilistic reinforcement
This review considers the theoretical problems facing agents that must learn and choose on the basis of reward or reinforcement that is uncertain or delayed, in implicit or proced...
Rudolf N. Cardinal
IJCNN
2008
IEEE
14 years 2 months ago
Biologically realizable reward-modulated hebbian training for spiking neural networks
— Spiking neural networks have been shown capable of simulating sigmoidal artificial neural networks providing promising evidence that they too are universal function approximat...
Silvia Ferrari, Bhavesh Mehta, Gianluca Di Muro, A...
EAAI
2006
123views more  EAAI 2006»
13 years 7 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
AINA
2010
IEEE
13 years 6 months ago
Compensation of Sensors Nonlinearity with Neural Networks
—This paper describes a method of linearizing the nonlinear characteristics of many sensors using an embedded neural network. The proposed method allows for complex neural networ...
Nicholas J. Cotton, Bogdan M. Wilamowski