# Biological Basis of the Code The provided code is part of a computational model that simulates specific aspects of the basal ganglia, a group of nuclei in the brain that are deeply involved in coordinating movement, as well as in other cognitive and emotional functions. This model specifically emphasizes the interplay between various types of neurons within the basal ganglia and their synaptic connections. ## Neuronal Types The code models several neuron types within the basal ganglia network, each represented with specific parameters: - **MSN (Medium Spiny Neurons):** Predominantly found in the striatum, MSNs are GABAergic inhibitory neurons. They are central to basal ganglia output as they modulate signals passing through the striatum. - **FSI (Fast-Spiking Interneurons):** These are inhibitory neurons critical for controlling the output of MSNs through local circuit interactions. - **STN (Subthalamic Nucleus Neurons):** Excitatory neurons that play a significant role in regulating the movements through connections with other basal ganglia components. - **GPe (Globus Pallidus Externa Neurons):** Inhibitory neurons that interact with the subthalamic nucleus as well as other basal ganglia nuclei, playing a crucial role in the regulation of movement. - **SNr (Substantia Nigra Pars Reticulata Neurons):** These act as the final output nuclei of the basal ganglia to the thalamus and are inhibitory. ## Synaptic Dynamics - **AMPA and NMDA Receptors:** The code manipulates synaptic efficacy through AMPA and NMDA receptor-mediated currents. These receptors are ionotropic glutamate receptors which play crucial roles in synaptic plasticity and transmission in the CNS. The code specifically modifies the synaptic dynamics by adjusting these receptor properties via the `resetSynapticParametersWithRatios` method to reflect different neurotransmission scenarios. - **GABA Receptors:** As MSNs and other inhibitory neurons in the basal ganglia are GABAergic, the model incorporates GABA-A mediated inhibition, critical for balancing excitatory input from glutamatergic receptors. ## Network Connectivity The code captures network connectivity based on probabilities derived from known biological data. - **Connectivity Probabilities:** The model uses connection probabilities (e.g., `P_MSN_MSN_in`) that reflect the likelihood of synaptic connections between different neuron types based on experimental and theoretical findings. This approach captures how neurons communicate within the basal ganglia, affecting computational outcomes like signal integration and inhibition. ## Dopaminergic Modulation - **Dopamine Level (DOPAMINE):** The model incorporates a parameter representing dopamine levels, which significantly affect basal ganglia function. Dopamine modulates the activity and synaptic plasticity of basal ganglia circuits, influencing motor and reward-related behaviors. ## Biological Relevance The model described in the code is designed to mimic the complex interactions within the basal ganglia circuitry in a simplified mathematical framework. This involves integrating the complex anatomy of neuron types, synaptic interactions, and modulatory systems into a coherent structure that attempts to replicate observed biological phenomena. By doing so, the model provides insights into how alterations in dynamics and connectivity within the basal ganglia can lead to motor dysfunctions, such as those seen in Parkinson’s disease and other movement disorders.