CBGTPy: An extensible cortico-basal ganglia-thalamic framework for modeling biological decision making.
| Title: | CBGTPy: An extensible cortico-basal ganglia-thalamic framework for modeling biological decision making. |
|---|---|
| Authors: | Clapp M; Department of Psychology & Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.; Bahuguna J; Department of Psychology & Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.; Giossi C; Departament de Ciències Matemàtiques i Informàtica, Universitat de les Illes Balears, Palma, Spain.; Institute of Applied Computing and Community Code, Palma, Spain.; Rubin JE; Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America.; Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.; Verstynen T; Department of Psychology & Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.; Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America.; Vich C; Departament de Ciències Matemàtiques i Informàtica, Universitat de les Illes Balears, Palma, Spain.; Institute of Applied Computing and Community Code, Palma, Spain. |
| Source: | BioRxiv : the preprint server for biology [bioRxiv] 2024 Aug 04. Date of Electronic Publication: 2024 Aug 04. |
| Publication Type: | Journal Article; Preprint |
| Language: | English |
| Journal Info: | Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet ISSN: 2692-8205 (Electronic) Linking ISSN: 26928205 NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE |
| 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. |
| Competing Interests: | Conflict of interest The authors declare no conflict of interest. |
| Comments: | Update in: PLoS One. 2025 Jan 14;20(1):e0310367. doi: 10.1371/journal.pone.0310367.. (PMID: 39808625) |
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| Grant Information: | R01 DA053014 United States DA NIDA NIH HHS; R01 DA059993 United States DA NIDA NIH HHS |
| Entry Date(s): | Date Created: 20230921 Latest Revision: 20250430 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC10508778 |
| DOI: | 10.1101/2023.09.05.556301 |
| PMID: | 37732280 |
| Database: | MEDLINE |
Journal Article; Preprint