The code provided is simulating neuronal network dynamics, likely in the context of a cortical sheet—a simplified model of a patch of neural tissue used in computational neuroscience to understand the complex spatiotemporal dynamics of neural activity. Here are the key biological aspects of this model: ### Neural Network Dynamics 1. **Population of Neurons:** - The model considers a network of 150x150 neurons, yielding a total of 22,500 (as given by `n^2`). This discrete representation captures the local interactions among neurons in a neural tissue like the cortex. 2. **Connectivity:** - The code relies on previously loaded data (e.g., `CeRem`, `CeLoc`, `CeLocI` from `'Conns_n150.mat'`) to define the connectivity between neurons. These might represent recurrent and local connections critical for simulating how neuronal activities propagate through the network. 3. **Inhibitory and Excitatory Neurons:** - The parameter `Py2Inh` represents the probabilistic strength of synaptic connections between pyramidal (Py) and inhibitory (Inh) neurons. In the brain, pyramidal neurons are the primary excitatory neurons, while inhibitory neurons modulate the activity of pyramidal neurons, maintaining the balance essential for stable network dynamics. 4. **Noise and Variability:** - Realistically, biological systems exhibit variability. `parameters.NValue` would introduce stochasticity to the model, reflecting the intrinsic noise in biological neural networks that can play a critical role in network dynamics and information processing. ### Simulation Procedure 5. **Initial Conditions and Pre-Simulation:** - Initial conditions for the network activity are first randomized, and a pre-simulation run (`runSheet` function) is executed to stabilize the system before the main simulation. This mirrors biological preparation of neural tissue to reach an operational equilibrium. 6. **Cluster Activation:** - A cell cluster is activated at initialization (`makeCellCluster`), which could simulate localized stimulus application or the activation of a specific neural region, often used to study how particular stimuli influence global network behavior. 7. **Temporal Dynamics:** - The simulation spans from 0 to 3 arbitrary time units, capturing temporal dynamics typical in neural simulations. The selected time span and integration steps (`parameters.h`) help observe the evolution of neural states over time. ### Visualization 8. **Activity Visualization:** - Post-simulation, the network activity (`Py`) at specific time points is visualized. This helps to analyze spatiotemporal patterns of neural activity propagation and spreading, akin to observing cortical wave patterns. 9. **Use of Color Maps:** - The code uses a custom colormap (`MayColourMap`) for clear visualization of neuronal activity, aiding in identifying spatial patterns that may reflect biologically relevant features like waves or bursts of neural activity. Overall, this code provides a framework that mirrors the biological intricacies of a neural network, focusing on the interaction and dynamics of excitatory and inhibitory neurons in a simplified cortical model.