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IJCNN
2006
IEEE
13 years 11 months ago
Backpropagation for Population-Temporal Coded Spiking Neural Networks
Abstract— Supervised learning rules for spiking neural networks are currently only able to use time-to-first-spike coding and are plagued by very irregular learning curves due t...
Benjamin Schrauwen, Jan M. Van Campenhout
IJON
2002
83views more  IJON 2002»
13 years 5 months ago
Population coding with unreliable spikes
The need for a neuronal coding scheme that is robust against the corruption of action potentials seems to support the idea of population rate coding, where the relevance of a sing...
Matthias Bethge, Klaus Pawelzik
ISCAS
2006
IEEE
90views Hardware» more  ISCAS 2006»
13 years 11 months ago
Feature competition in a spike-based winner-take-all VLSI network
— Recurrent networks and hardware analogs that perform a winner-take-all computation have been studied extensively. This computation is rarely demonstrated in a spiking network o...
Shih-Chii Liu, Matthias Oster
NIPS
2003
13 years 7 months ago
Prediction on Spike Data Using Kernel Algorithms
We report and compare the performance of different learning algorithms based on data from cortical recordings. The task is to predict the orientation of visual stimuli from the ac...
Jan Eichhorn, Andreas S. Tolias, Alexander Zien, M...