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IJCNN
2006
IEEE
14 years 13 days 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
ICANN
2005
Springer
13 years 12 months ago
A Hardware/Software Framework for Real-Time Spiking Systems
Abstract. One focus of recent research in the field of biologically plausible neural networks is the investigation of higher-level functions such as learning, development and modu...
Matthias Oster, Adrian M. Whatley, Shih-Chii Liu, ...
NECO
2007
150views more  NECO 2007»
13 years 6 months ago
Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule
Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is ...
Dorit Baras, Ron Meir
IJCNN
2000
IEEE
13 years 10 months ago
Adding Reinforcement Learning Features to the Neural-Gas Method
M. Winter, Giorgio Metta, Giulio Sandini