# Biological Basis of the Computational Neuroscience Model The provided code is a part of a computational neuroscience model that simulates large-scale brain activity, focusing on the integration of The Virtual Brain (TVB) and Hybrid TVB/LSNM simulations. The model leverages empirical data from Hagmann's brain network for understanding neuronal dynamics. The key biological aspects reflected in this model include: ## Biological Areas of Interest 1. **Cortical Regions and Nodes**: - The code focuses on specific cortical regions represented as nodes within TVB. These regions include V1 (primary visual cortex), V4, IT (inferotemporal cortex), FS (likely frontal or sensory-related), D1 and D2 (dopaminergic areas, related to the basal ganglia), and FR (frontal regions). These areas correspond to regions involved in visual processing, high-level sensory processing, and cognitive functions. - Host nodes and their connections are derived from Hagmann's brain connectivity data, suggesting that the model emphasizes structural connectivity and functional dynamics. 2. **Connectivity**: - The connectivity between nodes (i.e., `v1_cxn`, `v4_cxn`, etc.) highlights the focus on network-level interactions. These connections are chosen based on empirical data, where weights above 0.5 indicate stronger connections. This reflects the innate architecture of synaptic networks and efficient communication pathways in the brain. 3. **Neuronal Activity Simulation**: - The code loads neuronal activity data (`tvb_neuronal.npy`) from simulations. This data mimics electrical activity recordings and reflects fluctuations in membrane potentials across different brain regions over time, akin to EEG signals. - The distinction between `tvb` and `hybrid_tvb_lsnm` indicates comparisons between different simulation models or methodologies. 4. **Temporal Dynamics**: - Simulations are time-resolved, capturing dynamics over 198 milliseconds in real time. This temporal dimension allows for the examination of fast neuronal signaling, analogous to the real-time processing of information in the brain. 5. **Neurophysiological Relevance**: - By plotting and comparing the electrical activities in different brain regions, the model aids in understanding how regional interactions might manifest in observable brain activity and potential emergent behaviors or functionalities. Overall, this code forms part of a broader effort to mimic and understand the complex interactions and dynamics within the brain at a neuronal level, utilizing computational methods to draw parallels and provide insights into biological neural networks. These insights could help decipher the roles of various brain regions and their interactions in processing sensory inputs and generating cognitive functions.