CBGT

CBGT simulates spiking neural network dynamics in cortico-basal ganglia-thalamus circuits to analyze how corticostriatal STDP and dopaminergic feedback influence adaptive decision-making and evidence-accumulation processes.

Key Features:

• Spike-Timing Dependent Plasticity (STDP): Incorporates STDP mechanisms to simulate dopaminergic plasticity at corticostriatal synapses and its effects on competition between direct and indirect striatal pathways.
• Dopaminergic Feedback: Integrates dopaminergic feedback based on reward signals to adjust the balance of direct and indirect corticostriatal pathway weights.
• Full Spiking Network Model: Employs a comprehensive spiking network model of cortico-basal ganglia-thalamus pathways to capture interactions among circuit components during decision-making tasks.
• Drift Diffusion Modeling: Aligns simulated neural dynamics with drift diffusion models to quantify changes in evidence accumulation rates and decision boundaries.

Scientific Applications:

• Adaptive Decision-Making: Simulates reward-driven decision tasks to investigate how corticostriatal plasticity produces adaptive choices.
• Cognitive Algorithm Analysis: Analyzes how circuit-level changes affect evidence-accumulation algorithms and decision boundary modulation.
• Neural Dynamics Research: Enables study of dynamic interactions within cortico-basal ganglia-thalamus networks and their role in cognitive functions.

Methodology:

Performs STDP simulations to modify corticostriatal synaptic weights based on dopaminergic feedback from reward signals, applies the resulting weight ratios to a full spiking CBGT network, and analyzes the resulting neural dynamics using drift diffusion models.