The provided code is part of a computational model simulating the basal ganglia, a group of nuclei in the brain involved in movement control, as indicated by the references to specific nuclei and neurotransmitters. The model is inspired by the conditions reported in Magill et al. (2001), examining cortical and dopamine (DA) interactions. ### Biological Basis of the Model #### Basal Ganglia Circuitry - **Nuclei Involved**: The code includes five key nuclei of the basal ganglia: - **SD1 and SD2**: Representing neurons in the dorsal striatum, differentiated by their dopamine receptor types (D1 and D2). - **STN**: The subthalamic nucleus, which is involved in excitatory projections within the basal ganglia. - **GPe and GPi**: The external and internal segments of the globus pallidus, respectively, which play critical roles in inhibitory control of movement. - **Extrinsic Input (EXT)**: Represents inputs to the basal ganglia, possibly from cortical or thalamic sources. #### Neurotransmitters and Receptor Dynamics - **Dopamine (DA)**: Modeled at tonic levels, influencing synaptic weights and input dynamics. This reflects the modulatory role of dopamine in basal ganglia pathways, particularly affecting SD1 and SD2 neurons. - **Glutamatergic and GABAergic Transmission**: - **Glutamate**: The primary excitatory neurotransmitter, its synaptic influence is manipulated (e.g., via the glut_scale for urethane anaesthesia conditions). - **GABA (Gamma-Aminobutyric Acid)**: Represents inhibitory transmission, crucial for the feedback and feedforward loops in the basal ganglia circuitry, manipulated via gaba_scale. #### Synaptic and Membrane Properties - **Membrane Dynamics**: - **Time Constants and Noise**: Different for each nucleus, reflecting varied excitability and synaptic integration characteristics. - **Resistance and Thresholds**: Customized for each neuronal type, reflecting intrinsic neuronal properties. - **Synaptic Weights and Connections**: - **Connection Probability (p_connect)**: Mimics the sparsity of biological neural connectivity. - **Weighted Interactions**: Synaptic weights (e.g., SD1_w, STN_GPiw) simulate the strength and nature (excitatory or inhibitory) of projections between different nuclei. #### Experimental Conditions - **Input Types and Conditions**: Variations like 'tonic' for ongoing background activity and 'slow' for conditions simulating anesthetic-like correlated activity aim to replicate different experimental states. - **Urethane Anaesthesia Simulation**: Adjusts synaptic scaling to model the effects of anesthesia, which is known to alter neurotransmitter dynamics and neuronal excitability in vivo. #### Intrinsic and Extrinsic Modulation - **Intrinsic Currents**: Spontaneous input currents and burst-current parameters model inherent neuronal properties and potential for bursting activity, particularly in STN neurons known for burst firing. This computational model aims to replicate and analyze the complex interactions among different basal ganglia components under varying conditions of neurotransmitter levels and external inputs, giving insights into how these factors influence neural activity patterns within this critical brain region involved in controlling movement.