# Biological Basis of the Computational Model The code provided is part of a computational neuroscience model focused on understanding **distributed working memory (WM)** across a large-scale network of brain areas. Below are the key biological aspects connected to the code: ## Network Dynamics and Working Memory - **Cortical Areas**: The model simulates activity across 30 cortical areas, which are likely chosen to represent distributed regions involved in cognitive tasks, such as working memory. V1 (primary visual cortex) is specifically influenced by an external input (`Iext`), indicating its role in visual processing and memory encoding. - **External Input (Iext)**: The input to area V1 suggests stimulation to initiate or maintain working memory operations. The input parameters (`Tpulse` and `mu0`) may represent the temporal and intensity characteristics of sensory or task-specific stimuli. - **Global Coupling**: The `Gext` variable symbolizes global neural coupling, which refers to the extent of connectivity or interaction strength among the cortical areas. This is biologically relevant as higher coupling could simulate conditions of enhanced coordination (or synchronization) between areas involved in a working memory task. ## Hierarchical and Connectional Dynamics - **FLN and SLN**: These likely refer to the "Feedforward and Lateral Networks," respectively. They are important for modeling how information flows through different areas and hierarchies in the brain, reflecting the anatomical and functional connectivity among brain regions. - **Wiring and Hierarchical Values**: These parameters (`wiring` and `hierVals`) hint at the hierarchical organization of the cortical areas. Hierarchies in the brain are thought to be critical for information processing and integration, particularly in tasks like working memory which require orchestrating multiple neural circuits. ## Competition and Gating - **CIB (Cross-Inhibitory Balance)**: The mention of 'With CIB' and 'Without CIB' implies a mechanism of inhibitory control or competition among neural populations. CIB can regulate the segregation and integration of information, essential for maintaining discrete memory states without interference. ## Output and Thresholds - **Working Memory Areas**: The output variable `WMareas` counts areas that exceed a threshold rate of activity, indicating those actively participating in storing or manipulating information during a working memory task. The threshold could represent the firing rate necessary for sustained neuronal activation characteristic of memory maintenance. ## Visualization and Insights - **Graphical Plotting**: The plotted results show how the number of areas involved in working memory changes with global coupling and inhibitory dynamics. This reveals how network connectivity and inhibition modulate the brain's capacity for distributed memory tasks, offering insights into normal and potentially pathological conditions of working memory functionality. The model outlined serves as a simulation of the neural underpinnings of distributed working memory processes, emphasizing the roles of cortical areas, hierarchical and connectivity dynamics, and inhibitory control in cognitive function.