Neural network models effectively simulate the behavioral and neural dynamics of Pavlovian associative learning, capturing phenomena like dopamine signaling and contingency degradation.
Evidence for · 2
Inferring brain-wide interactions using data-constrained recurrent neural network models
2020 · cited by 87
Recurrent neural network models are shown to successfully reproduce and untangle neural dynamics and behaviors underlying Pavlovian conditioning.
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More for · 1
Prospective contingency explains behavior and dopamine signals during associative learning.
2025 · cited by 14
Recurrent neural networks trained within a temporal difference framework successfully develop state representations that explain dopamine signals and behavior during associative Pavlovian learning.