Decision-making depends on the coordinated activity of cortico-basal ganglia-thalamic circuits. These circuits help the brain evaluate alternatives, select actions, suppress inappropriate responses, and learn from reward. Yet they are difficult to study because behavior emerges from interactions between many pathways, timescales, and cell populations. CBGTPy was developed to make these interactions easier to model, manipulate, and test.
In this paper, in collaboration with Jonathan Rubin, Timothy Verstynen (Pittsburgh, PA) and Catalina Vich (Palma, Spain), we introduce CBGTPy, an extensible Python framework for simulating goal-directed agents whose internal dynamics are modeled on mammalian cortico-basal ganglia-thalamic pathways. The toolbox allows researchers to design behavioral tasks, run biologically grounded spiking-network simulations, and generate testable predictions about both neural activity and behavior. It is explicitly built around flexibility: experimental parameters can be modified independently from the properties of the simulated agent, making it possible to test many decision-making paradigms within the same framework.
A central strength of CBGTPy is that it bridges two levels that are often studied separately: circuit dynamics and behavior. The model includes key CBGT components such as cortex, striatum, external and internal globus pallidus, subthalamic nucleus, and thalamus, with biologically motivated pathways, spiking dynamics, dopamine-dependent plasticity, and the possibility of targeted stimulation of specific nuclei. This makes it possible to ask not only what decision an agent makes, but how that decision emerges from interacting neural populations.
The framework is designed for both action selection and action inhibition. It can simulate n-choice decision tasks, where the model selects among competing alternatives, as well as stop-signal tasks, where an emerging action must be cancelled. It also allows users to manipulate pathways or apply targeted stimulation, offering a way to explore how changes in basal ganglia circuitry alter decision time, choice behavior, or stopping success.
The broader contribution is methodological. CBGTPy provides a virtual environment in which hypotheses about CBGT function can be tested before, alongside, or after experiments in humans and animal models. As new anatomical and physiological discoveries reshape our understanding of basal ganglia organization, the framework can be extended to include additional pathways or updated circuit assumptions.
A useful way to summarize the paper is: CBGTPy turns cortico-basal ganglia-thalamic theory into an experimental playground. It allows researchers to move from verbal circuit diagrams to executable models that produce both neural dynamics and behavior.
This work contributes to a growing effort to make computational models more useful for experimental neuroscience: not as abstract black boxes, but as biologically interpretable tools for testing how circuit mechanisms give rise to decisions, learning, and inhibitory control.
To know more:
- Clapp, M., Bahuguna, J., Giossi, C., Rubin, J. E., Verstynen, T., & Vich, C. (2025). CBGTPy: An extensible cortico-basal ganglia-thalamic framework for modeling biological decision making. PLOS ONE, 20, e0310367. https://doi.org/10.1371/journal.pone.0310367.
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.
In our preprint, 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.
To know more:
- Matthew Clapp, Jyotika Bahuguna, Cristina Giossi, Jonathan E. Rubin, Timothy Verstynen, Catalina Vich (2023). CBGTPy: An extensible cortico-basal ganglia-thalamic framework for modeling biological decision making. BioRxiv https://doi.org/10.1101/2023.09.05.556301