AIP Conference Proceedings 151 on Neural Networks for Computing
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Dopamine-dependent plasticity of corticostriatal synapses
Neural Networks - Computational models of neuromodulation
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Isotropic sequence order learning
Neural Computation
Second Order Conditioning in the Sub-cortical Nuclei of the Limbic System
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Learning and Reversal Learning in the Subcortical Limbic System: A Computational Model
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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When neurons fire together they wire together This is Donald Hebb's famous postulate However, Hebbian learning is inherently unstable because synaptic weights will self amplify themselves: the more a synapse is able to drive a postsynaptic cell the more the synaptic weight will grow We present a new biologically realistic way how to stabilise synaptic weights by introducing a third factor which switches on or off learning so that self amplification is minimised The third factor can be identified by the activity of dopaminergic neurons in VTA which fire when a reward has been encountered This leads to a new interpretation of the dopamine signal which goes beyond the classical prediction error hypothesis The model is tested by a real world task where a robot has to find “food disks” in an environment.