ArticlePloS one2025
CBGTPy: An extensible cortico-basal ganglia-thalamic framework for modeling biological decision making.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- How the dynamic interplay of cortico-basal ganglia-thalamic pathways shapes the time course of deliberation and commitment.PLoS computational biology · 2026Article
- Learning reweights the decision dynamics of cortico-basal ganglia-thalamic pathways from deliberation to commitment.bioRxiv : the preprint server for biology · 2026Article
- How cortico-basal ganglia-thalamic subnetworks can shift decision policies to increase reward rate.PLoS computational biology · 2025Article
- Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles.Research square · 2025Article
- Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles.bioRxiv : the preprint server for biology · 2025Article
- Arkypallidal neurons in the external globus pallidus can mediate inhibitory control by altering competition in the striatum.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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6 authors.
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Abstract
Here we introduce CBGTPy, a virtual environment for designing and testing goal-directed agents with internal dynamics that are modeled on the cortico-basal-ganglia-thalamic (CBGT) pathways in the mammalian brain. CBGTPy enables researchers to investigate the internal dynamics of the CBGT system during a variety of tasks, allowing for the formation of testable predictions about animal behavior and neural activity. The framework has been designed around the principle of flexibility, such that many experimental parameters in a decision making paradigm can be easily defined and modified. Here we demonstrate the capabilities of CBGTPy across a range of single and multi-choice tasks, highlighting the ease of set up and the biologically realistic behavior that it produces. We show that CBGTPy is extensible enough to apply to a range of experimental protocols and to allow for the implementation of model extensions with minimal developmental effort.
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