Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Dynamics of a winner-take-all neural network
Neural Networks
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Simple model of spiking neurons
IEEE Transactions on Neural Networks
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A biologically plausible winner-takes-all architecture
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
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Most artificial neural network architectures learn either via unsupervised or reinforcement learning but rarely via both. However, the brain effectively integrates both types of learning. We describe which prerequisites are necessary in a spiking network architecture in order to integrate both learning mechanisms and present a network which meets these requirements. In a nut shell, the network has a winner-take-all type output layer resembling the motor output and an excitatory feedback layer which extends the firing of the input layer until after the end of external stimulation resembling the function of the hippocampus.