The code provided is part of a computational model designed to simulate the dynamics of a basal ganglia network under specified conditions. The basal ganglia are a group of interconnected subcortical nuclei in the brain involved in various functions, including motor control, action selection, and reward-based learning. This model appears to simulate specific components of the basal ganglia and their interactions in the absence of cortical (Ctx) and dopamine (DA) inputs, as specified by the condition `"No Ctx, no DA - no STN DA"`. ### Key Biological Components Modeled: 1. **Nuclei of the Basal Ganglia:** - **Striatal D1 and D2 Neurons (SD1 and SD2):** These correspond to different types of neurons in the striatum, a primary input structure of the basal ganglia. D1-type neurons are typically associated with the direct pathway and D2-type neurons with the indirect pathway, each modulating motor activity differently. - **Subthalamic Nucleus (STN):** Part of the indirect pathway, the STN sends excitatory inputs to both the Globus Pallidus internus (GPi) and externus (GPe), playing a critical role in regulating motor control. - **Globus Pallidus Externus (GPe) and Internus (GPi):** The GPe serves as a relay in the indirect pathway, contributing to the inhibition of competing motor programs, while the GPi is part of the direct pathway's output, providing inhibitory control over thalamic activity and thus influencing movement. 2. **Neuronal Dynamics:** - Neuronal activities are determined by parameters such as membrane potentials, synaptic inputs, and intrinsic currents. - **Firing Thresholds and Reset Potentials:** These parameters are set for different nuclei, reflecting their different firing properties. Notably, STN neurons have a lower firing threshold (`theta`), indicating they are more easily activated. - **Refractory Periods and Excitatory/Inhibitory Timings:** Modeled to mimic real biological synaptic interactions, e.g., time constants for AMPA, NMDA, and GABA receptors. 3. **Synaptic Interactions:** - **Connection Proportions:** The model uses a connection probability (`p_connect`) to represent synaptic connectivity, thereby capturing network sparsity. - **Synaptic Weights:** These weights determine the strength of excitatory and inhibitory inputs among these nuclei. For instance, GABAergic (inhibitory) weights are adjusted under urethane to mimic its anesthetic effect by increasing inhibition. 4. **Dopaminergic Influence:** - **Dopamine's Role:** Typically, dopamine modulates the state of the basal ganglia network, influencing motor activity through its effect on striatal neurons; however, in this model, dopamine is set to zero (`dop1 = 0; dop2 = 0`), reflecting its absence and a focus on compensatory network dynamics. 5. **External and Spontaneous Inputs:** - External inputs (`EXT`) are modeled with parameters like synaptic delays and weights, simulating cortical drive and how it would normally influence basal ganglia activity. - Spontaneous activity is provided to certain nuclei, such as the STN and GPe, to simulate baseline firing rates observed in these neurons under specific conditions. 6. **Experimental Conditions:** - **Urethane Effect (`do_urethane`):** This parameter adjusts excitatory and inhibitory synaptic weights to simulate the reduction and enhancement of neurotransmitter effects under anesthesia, a state often used in experimental settings to study brain activity. ### Conclusion: This code forms the basis for a simulation aimed at understanding basal ganglia network dynamics under specific experimental conditions derived from the work of Magill et al. (2001). By focusing on intrinsic neuronal properties, synaptic interactions, and absence of cortical and dopaminergic modulation, the model provides insights into basal ganglia function and dysfunction which could be extrapolated to conditions such as Parkinson's disease or anesthesia.