Literature DB >> 11521155

Anticipatory responses of dopamine neurons and cortical neurons reproduced by internal model.

R E Suri1.   

Abstract

Animals seem to learn and use internal models; they learn to anticipate predictable events, and their behavior in the sensory preconditioning paradigm reflects formation of novel associative chains. To investigate possible neural correlates, the temporal difference model (TD model) was extended to an internal model approach. The proposed model learns reward prediction error signals that resemble dopamine neuron activity. In contrast to the original TD model, the reward prediction error signals of the proposed model are influenced by the formation of novel associative chains in the sensory preconditioning experiment. This is consistent with experimental findings, as striatal dopamine concentration is influenced by the formation of novel associative chains in this paradigm. Comparison of the model architecture with biological neural networks suggests that chains of neurons with tonic anticipatory activity may underlie the formation of novel associative chains. These findings suggest that dopamine neuron activity may reflect the processing of an internal model.

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Year:  2001        PMID: 11521155     DOI: 10.1007/s002210100814

Source DB:  PubMed          Journal:  Exp Brain Res        ISSN: 0014-4819            Impact factor:   1.972


  6 in total

Review 1.  Internal models in sensorimotor integration: perspectives from adaptive control theory.

Authors:  Chung Tin; Chi-Sang Poon
Journal:  J Neural Eng       Date:  2005-08-31       Impact factor: 5.379

2.  Rethinking dopamine as generalized prediction error.

Authors:  Matthew P H Gardner; Geoffrey Schoenbaum; Samuel J Gershman
Journal:  Proc Biol Sci       Date:  2018-11-21       Impact factor: 5.349

3.  Dopamine cells respond to predicted events during classical conditioning: evidence for eligibility traces in the reward-learning network.

Authors:  Wei-Xing Pan; Robert Schmidt; Jeffery R Wickens; Brian I Hyland
Journal:  J Neurosci       Date:  2005-06-29       Impact factor: 6.167

4.  Speed/accuracy trade-off between the habitual and the goal-directed processes.

Authors:  Mehdi Keramati; Amir Dezfouli; Payam Piray
Journal:  PLoS Comput Biol       Date:  2011-05-26       Impact factor: 4.475

5.  Predictive representations can link model-based reinforcement learning to model-free mechanisms.

Authors:  Evan M Russek; Ida Momennejad; Matthew M Botvinick; Samuel J Gershman; Nathaniel D Daw
Journal:  PLoS Comput Biol       Date:  2017-09-25       Impact factor: 4.475

6.  Seeking motivation and reward: Roles of dopamine, hippocampus, and supramammillo-septal pathway.

Authors:  Andrew J Kesner; Coleman B Calva; Satoshi Ikemoto
Journal:  Prog Neurobiol       Date:  2022-02-25       Impact factor: 11.685

  6 in total

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