Escape, avoidance, and imitation: a neural network approach
Adaptive Behavior
Dopamine-dependent plasticity of corticostriatal synapses
Neural Networks - Computational models of neuromodulation
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Learning obstacle avoidance with an operant behavior model
Artificial Life
Classification of linearly nonseparable patterns by linear threshold elements
IEEE Transactions on Neural Networks
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Neurons in the basal ganglia (BG) of monkeys learning a simple visual discrimination (VD) task show faster changes in activity than those in the prefrontal cortex (PFC). This motivated the hypothesis that changes in the BG activity can ''lead'' those in the PFC. Given that the PFC is a key player in the learning of complex tasks, we tested the former hypothesis by using a neural network model that learns simple and complex contingencies as VD and delayed matching to sample (DMTS) tasks. Even though the model accounted for the results in the VD task no such ''lead'' was observed in the DMTS task. We propose that when the task requires learning high-order contingencies, such as in the DMTS case, motor structures quickly select the subset of responses allowing the subject to obtain reward, but learning in the cortico-BG loop progresses in a concurrent way in order to maximize reward.