The provided code aims to model aspects of neural connections within the globus pallidus externus (GPe), a critical structure in the basal ganglia of the brain. The basal ganglia are involved in various functions, including motor control, learning, and behavior modification, primarily through complex neural circuits. Here, the focus is on the lateral connections within the GPe. ### Biological Basis #### Globus Pallidus Externus (GPe) - The GPe is part of the basal ganglia network and plays an essential role in regulating movement and processing information within this neural circuit. - It communicates with other parts of the basal ganglia network through inhibitory neurotransmitter signals, mainly involving GABA (gamma-aminobutyric acid). #### Lateral Connections in GPe - **Lateral Connectivity:** These are connections between neurons that are at the same hierarchical level within the GPe. They allow for integration and processing of information across similar neural populations. - **Strength and Interaction:** The strength of these connections influences the overall output from the GPe to targets, such as the subthalamic nucleus (STN), and modulates motor control and other basal ganglia-associated functions. #### Dopamine (DA) - **Role of Dopamine:** Dopamine is a crucial neuromodulator in the basal ganglia. It adjusts the functioning of neural circuits in response to changes in the level of dopamine, which happens in various states such as different phases of motor activity or in disorders like Parkinson's disease. - The parameter `DA` in the code likely represents dopamine concentration, which affects the lateral connection strengths (`ssmax`) in the GPe. Biologically, this reflects changes in synaptic efficacy tied to dopaminergic modulation. #### Computational Representation - **Lateral Connectivity Parameters:** The code sets specific parameters—`smax` and `rs`—representing the maximum strength and radius influencing lateral interactions among neurons in the GPe. - **Modulation of Synaptic Weights:** Changes in dopamine levels alter synaptic weights, which is modeled by adjusting the maximum lateral connection strength (`ssmax`) in response to dopamine (`DA`). This reflects the real-life plasticity observed in synaptic circuits due to neuromodulation. By modeling lateral connectivity in the GPe, this computational approach allows researchers to simulate how variations in dopamine levels can modulate neural activity within the basal ganglia, ultimately contributing to motor control and potentially other higher-order functions.