The code provided is a computational model simulating neural network dynamics in multiple brain areas, based on the well-known Wong-Wang model. It is structured to mimic certain biological properties and interactions between excitatory and inhibitory neurons in the brain, and it simulates how these subdivisions respond to inputs over time. Here are the key biological components modeled by the code: ### Brain Area Network - **Multiple Areas**: The code simulates a network of 30 areas, reflecting the interconnected nature of brain regions, often modeling specific visual or associative areas such as V2, V4, and MT. The reference to areas like LIP, TEpd, and others hints at attempts to model parts of the macaque visual and frontal cortices. - **Lesion Simulation**: The ability to selectively lesion an area (e.g., V4 or LIP), by setting its neural activity to zero, allows the simulation of experimental scenarios where certain brain regions are ablated, reflecting studies on the functional architecture and connectivity of the brain. ### Neural Dynamics - **Neural Populations**: The model includes three types of populations per area — typically representing two excitatory (pyramidal) populations and one inhibitory (interneurons) population, which form the basic building blocks of cortical circuits. - **Rate Coding**: The neural activity is represented as firing rates (`rate` arrays), corresponding to biological neurons’ spike rates, a common abstraction in computational neuroscience models. - **Synaptic Interactions**: The code computes inputs to neurons as a combination of local interactions (excitatory/inhibitory balance) and long-range projections from other areas. This models both local synaptic connections and distributed processing across brain regions. ### Synaptic and Neural Processes - **NMDA and GABA Dynamics**: The code evolves NMDA receptor-mediated excitatory currents and GABA receptor-mediated inhibitory currents through time constants (`taun` and `taug`) and conductance levels. NMDA receptors contribute to synaptic plasticity and excitatory transmission, while GABA receptors mediate inhibition. - **Transfer Functions**: The model uses nonlinear transfer functions to convert synaptic input into neural firing rates, deriving from the biophysics of synaptic transmission and neuron firing (e.g., threshold-linear curves for inhibitory neurons). - **Noise Integration**: An Ornstein-Uhlenbeck (OU) process simulates synaptic noise and variability in neural firing, reflecting the stochastic nature of neural activity in the brain. ### Input and Interaction Patterns - **External Inputs and Stimulation Protocols**: `Iext` represents external stimulation to mimic sensory inputs or experimental manipulations, with variable periods of application (`Tpulse`). - **Hierarchical Connectivity**: The model includes gradients and area-specific connections, meant to reflect biologically observed differences in cortical connectivity strength and patterns across different cortical regions. The code provides a high-level abstraction of cortical network dynamics, integrating various neuronal and synaptic mechanisms to simulate complex brain function and test hypotheses about neural activity patterns across structured neural circuits.