The provided code models aspects of cortical networks, particularly focusing on neuronal connectivity and activity within cortical sheets. Here, I outline the biological basis of the code: ### Neuronal Connectivity: 1. **Neuronal Type and Connectivity:** - The model distinguishes two types of neurons within the cortical network: pyramidal neurons (Py) and inhibitory interneurons (In). - **Connectivity Matrices:** - `CeLoc` and `CeLocI` represent local connectivity matrices for excitatory connections from pyramidal to other pyramidal neurons and from pyramidal to inhibitory interneurons, respectively. The local connections are determined using a Gaussian function over a toroidal topology, reflecting the spatially-decaying probability of connection, typical in cortical tissue. 2. **Remote Connectivity:** - `CeRem` represents the remote, or long-range, connections between different areas or patches of the simulated network. This reflects the patchy, yet overlapping connections often observed in the cerebral cortex, where distant cortical columns can influence each other. ### Neural Dynamics and Noise: 3. **Parameters of Neuronal Dynamics:** - `parameters.tauPy` and `parameters.tauInh` set the time constants for pyramidal and inhibitory neurons, affecting how fast these neurons respond to input. These parameters modify the temporal dynamics of action potentials in the simulated cortical sheet. 4. **Background Activity/Noise:** - A noise function, `getNoise`, is used to simulate intrinsic neuronal activity fluctuations, capturing the naturally occurring variability and stochastic nature of synaptic input in the brain. ### Simulation of Neuronal Activity: 5. **Initial Conditions and Inputs:** - The code can initialize neuronal activity (all 0 for an initial prescan and all 1 for a subsequent run), reflecting different states of network activation. - `parameters.PyInput` represents external input to the pyramidal neurons, mimicking external stimuli or background cortical drive. 6. **Cluster Formation:** - `makeCellCluster` is used to introduce heterogeneity by forming clusters of neurons with differing activity levels. This simulates the non-uniform activity patterns observed in biological neuronal circuits due to differences in input or connectivity. ### Outputs and Analysis: 7. **Local Field Potentials (LFP):** - The model calculates LFPs, which are an aggregate measure of synaptic activity around the neurons. LFPs are used to approximate the electrophysical outputs one might record with electrodes in the brain. 8. **Visualization:** - The code visualizes various aspects of the model output, including averaged neuronal firing (both pyramidal and inhibitory), in response to simulated input over time. This visualization helps to understand the emergent oscillatory dynamics, typical of cortical activity. This model captures key principles of cortical microcircuitry, using computational methods to simulate connectivity, neuronal dynamics, and emergent network activity. The focus is on understanding how structured connectivity and input patterns generate the complex electrical patterns observed in the brain.