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Statistical Mechanics of Node-perturbation Learning with Noisy Baseline
The value of the objective function for an unperturbed output is called a baseline.
Effects of Synaptic Weight Diffusion on Learning in Decision Making Networks
Loewenstein & Seung (2006) demonstrated that matching behavior is a steady state of learning in neural networks if the synaptic weights change proportionally to the covariance between reward and neural activities.
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