Neural network models successfully simulate Pavlovian learning processes
Neural network models effectively simulate the behavioral and neural dynamics of Pavlovian associative learning, capturing phenomena like dopamine signaling and contingency degradation.
The provided papers demonstrate that neural network models, particularly recurrent neural networks and temporal difference learning frameworks, successfully account for behavioral and neural data during Pavlovian conditioning and associative learning tasks.
M. Perich, Charlotte Arlt, Sofia Soares, M. E. Young, Clayton P. Mosher, Juri Minxha, Eugene Carter, Ueli Rutishauser, P. Rudebeck, C. Harvey, Kanaka Rajan. Inferring brain-wide interactions using data-constrained recurrent neural network models. 2020. https://doi.org/10.1101/2020.12.18.423348
Recurrent neural network models are shown to successfully reproduce and untangle neural dynamics and behaviors underlying Pavlovian conditioning.
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Qian L, Burrell M, Hennig JA, Matias S, Murthy VN, Gershman SJ, Uchida N. Prospective contingency explains behavior and dopamine signals during associative learning.. 2025. https://doi.org/10.1038/s41593-025-01915-4
Recurrent neural networks trained within a temporal difference framework successfully develop state representations that explain dopamine signals and behavior during associative Pavlovian learning.
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