Adequate input for learning in attractor neuralnetworksDaniel

In the context of learning in attractor neural networks (ANN) we discuss the issue of the constraints imposed by the requirement that the aaerents arriving at the neurons in the attractor network from the stimulus, compete successfully with the aaerents generated by the recurrent activity inside the network, in a situation in which the both sets of synaptic eecacies are weak and approximately equal. We simulate and analyze a two component network: one representing the stimulus, the other an ANN. We show that if stimuli are correlated with the receptive elds of neurons in the ANN, and are of suucient contrast, the stimulus can provide the necessary information to the recurrent network to allow learning new stimuli, even in very disfavored situation of synaptic predominance in the recurrent part. Stimuli which are insuuciently correlated with the receptive elds, or are of insuucient contrast, are submerged by the recurrent activity.