Temporal Sequence Learning, Prediction, and Control: A Review of Different Models and Their Relation to Biological Mechanisms
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- Florentin Wörgötter
- Department of Psychology, University of Stirling, Stirling FK9 4LA, Scotland,
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- Bernd Porr
- Department of Psychology, University of Stirling, Stirling FK9 4LA, Scotland,
抄録
<jats:p>In this review, we compare methods for temporal sequence learning (TSL) across the disciplines machine-control, classical conditioning, neuronal models for TSL as well as spike-timing-dependent plasticity (STDP). This review introduces the most influential models and focuses on two questions: To what degree are reward-based (e.g., TD learning) and correlation-based (Hebbian) learning related? and How do the different models correspond to possibly underlying biological mechanisms of synaptic plasticity? We first compare the different models in an open-loop condition, where behavioral feedback does not alter the learning. Here we observe that reward-based and correlation-based learning are indeed very similar. Machine control is then used to introduce the problem of closed-loop control (e.g., actor-critic architectures). Here the problem of evaluative (rewards) versus nonevaluative (correlations) feedback from the environment will be discussed, showing that both learning approaches are fundamentally different in the closed-loop condition. In trying to answer the second question, we compare neuronal versions of the different learning architectures to the anatomy of the involved brain structures (basal-ganglia, thalamus, and cortex) and the molecular biophysics of glutamatergic and dopaminergic synapses. Finally, we discuss the different algorithms used to model STDP and compare them to reward-based learning rules. Certain similarities are found in spite of the strongly different timescales. Here we focus on the biophysics of the different calcium-release mechanisms known to be involved in STDP.</jats:p>
収録刊行物
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- Neural Computation
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Neural Computation 17 (2), 245-319, 2005-02-01
MIT Press
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詳細情報 詳細情報について
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- CRID
- 1361981471192921984
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- NII論文ID
- 30022207918
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- ISSN
- 1530888X
- 08997667
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- データソース種別
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