## Biological Basis of the Code The provided code models the complex network interactions within the mammalian visual cortex. This model is structured as a **multi-area network** capturing the dynamics and connectivity patterns across different cortical areas. Key biological aspects modeled in the code include: ### 1. **Cortical Architecture:** - **Area List and Connectivity:** The model includes various cortical areas (`M.area_list`) and their interactions, emphasizing hierarchical and feedforward-feedback dynamics within the visual cortex. Each area contains a structure (`M.structure[area]`) that presumably represents different types of neuronal populations or layers. - **Architectural Types:** The use of `arch_types = proc['architecture_completed']` suggests consideration of different cortical architectures. This could include differentiation between hierarchical (HL), horizontal (HZ), and local (same-area) connections as part of modeling cortical columnar organization. ### 2. **Connectivity Parameters:** - **Connection Parameters:** The code uses specific parameters (`conn_params`) that influence synaptic transmission, including `g` (potentially the inhibitory synaptic weight or balance) and various `fac_nu_ext` values, which may modulate external input rates to cortical layers. - **Indegree Scaling:** The parameter `av_indegree_V1` (average indegree for area V1), highlights the focus on connectivity density and synaptic input distribution to different areas. ### 3. **Granger Causality and Significance:** - **Granger Causality Analysis:** The data processing includes analysis of Granger causality, a statistical method assessing directed functional connectivity, which is used to infer causal relationships between different cortical areas or populations. - **Significance Testing:** The model evaluates the proportion of significant connections (p-values less than 0.05) between different populations or areas, capturing meaningful neural interactions, and potentially correlating them to functional relevance. ### 4. **Excitatory vs. Inhibitory Dynamics:** - **Balance of E/I Connections:** The code explicitly assesses the balance between excitatory and inhibitory influences in network interactions. This reflects the biological concept of excitation-inhibition balance, critical for stable neural computation and preventing pathological states like epilepsy. ### 5. **Indegree and Connection Strength:** - **Relative Indegree:** The use of `K`, which represents relative indegree, reflects on how the strength and density of synaptic inputs vary across the network. This is a key aspect of neural function, affecting how effectively neurons can integrate incoming signals. - **Connection Strength Analysis:** The investigation of the relationship between Granger causality and connection strength demonstrates an appreciation for how the biophysical properties of synaptic connections can influence functional connectivity patterns. ### 6. **Visualization:** - The code aims to visualize meaningful patterns of interactions (`Fig9`) through different layers and areas of the cortex, helping to illustrate the spatial distribution and organization of neural interactions across the modeled cortical structure. Overall, the code aims to simulate and visualize the distributed and hierarchical nature of cortical processing in the visual system, focusing on the intricate interplay between different areas and neuronal populations via their connectivity patterns, while also addressing statistical significance and excitation-inhibition balance.