The provided code is part of a computational neuroscience model that aims to simulate and analyze the hierarchical organization of functional brain networks. Here, I will describe the biological basis of key elements present in the code: --- ### Functional Hierarchies in the Brain: 1. **Functional Hierarchy:** The code is designed to generate functional hierarchies within neural networks, which reflect the organization of different brain regions based on their connections and roles. Hierarchies in the brain allow for efficient information processing and integration across various cortical and subcortical areas. 2. **FLN and SLN Matrices:** - **FLN (Feedforward Likelihood Network):** In this context, FLN refers to connectivity data that describes the likelihood or propensity for neural information to flow from one brain region to another in a feedforward manner. This is key in understanding bottom-up processing within the hierarchical structure. - **SLN (Superficial Layer Network):** SLN likely represents connectivity patterns involving superficial cortical layers, which are essential for horizontal or within-layer communication. This might contribute to lateral information processing and integration. 3. **Area-to-Area Distances:** - The hierarchy considers spatial distances between different cortical areas, which influence the connectivity and communication patterns. Distance data helps model how structural connectivity can impact the functional hierarchies due to conduction delays and synaptic strength attenuation. ### Specific Brain Regions and Networks: - **ROI (Regions of Interest):** These are specific brain regions selected for detailed analysis based on previous research (e.g., Kennedy-Fries Neuron 2015 selection). The selected regions likely have distinctive roles in cognitive and sensory processing as part of the brain’s hierarchical structure. ### Neural Oscillations: 1. **Gamma and Alpha Power:** - **Gamma Power (30 Hz):** Gamma oscillations are linked with high-level cognitive functions, such as attention and memory. The code analyzes gamma power across the cortex to understand how different hierarchical levels contribute to or are modulated by these fast oscillations. - **Alpha Power (3 Hz):** Alpha oscillations are often associated with resting state and inhibitory processes. It provides insights into modulating attention and sensory processing at different hierarchical levels. ### Modeling Approaches: - **Parameters and Simulations:** The function `hierarchy` appears to simulate the dynamics of neural connections based on different input parameters, potentially representing biological characteristics like synaptic strengths or neurotransmitter concentrations. ### Data Sources: - **Core-Nets.org Data Sources:** The code utilizes empirical data from a known neuroinformatics repository that provides anatomical and functional connectivity information, ensuring that the model's assumptions and simulations are grounded in biological reality. --- In summary, the code's biological basis lies in modeling the hierarchical organization of brain networks using empirically derived connectivity data (FLN and SLN), spatial arrangements, and neural oscillations (gamma and alpha) to simulate and understand the functional architectures of neural processes.