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» Extracting Propositions from Trained Neural Networks
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CDC
2008
IEEE
147views Control Systems» more  CDC 2008»
14 years 1 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
IJCNN
2008
IEEE
14 years 1 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...
NIPS
2008
13 years 8 months ago
Extracting State Transition Dynamics from Multiple Spike Trains with Correlated Poisson HMM
Neural activity is non-stationary and varies across time. Hidden Markov Models (HMMs) have been used to track the state transition among quasi-stationary discrete neural states. W...
Kentaro Katahira, Jun Nishikawa, Kazuo Okanoya, Ma...
ENGL
2006
99views more  ENGL 2006»
13 years 7 months ago
A Patient Specific Neural Networks (MLP) for Optimization of Fuzzy Outputs in Classification of Epilepsy Risk Levels from EEG Si
In recent years Neural Networks have been widely used as pattern and statistical classifiers in bio medical engineering. Most research to date using hybrid systems (Fuzzy-Neuro) fo...
R. Sukanesh, R. Harikumar
ICRA
2007
IEEE
117views Robotics» more  ICRA 2007»
14 years 1 months ago
Predicting Object Dynamics from Visual Images through Active Sensing Experiences
Prediction of dynamic features is an important task for determining the manipulation strategies of an object. This paper presents a technique for predicting dynamics of objects re...
Shun Nishide, Tetsuya Ogata, Jun Tani, Kazunori Ko...