The provided code appears to be part of a computational model simulating certain aspects of neuronal electrophysiology, specifically aimed at calculating local field potentials (LFPs). Below are key biological features and assumptions that are inferred from the code: ### Biological Basis of the Model 1. **Neuronal Components**: - **Soma**: The code suggests individual cell bodies ("soma") for pyramidal neurons and interneurons. These structures are fundamental to neuronal cell bodies where integration of incoming signals occurs. - **Dendrites**: Presence of dendritic compartments indicates these neurons also have branched processes extending from the soma. Dendrites are crucial for receiving synaptic inputs from other neurons. 2. **Cell Types**: - **Pyramidal cells**: The code specifies several pyramidal cells (`pyCellsNumber = 4`), which are a type of excitatory neuron, typically found in the cerebral cortex, known for their significant role in cognitive function and LFP generation. - **Interneuron**: There is a single interneuron (`inCellsNumber = 1`), which likely provides inhibitory influences and helps regulate network dynamics. 3. **Electrophysiological Currents**: - The code references various ionic currents (`ina`, `ik`, `icl`, `ica`) happening across the cell membrane. These relate to the flow of sodium (Na⁺), potassium (K⁺), chloride (Cl⁻), and calcium (Ca²⁺) ions and form the basis of action potentials and synaptic transmission. - **Injected Current**: The model also uses explicitly injected currents through mechanisms like `LinClamp`, simulating experimental conditions where currents are artificially introduced in a neuron. 4. **Extracellular Electric Fields (LFP)**: - The code's main focus is to compute local field potentials (LFPs), which are extracellular electric fields resulting from summed electrical activity (current flows) within a region of tissue. - It calculates LFP contributions from different neuronal compartments (soma, dendrite) and synapses by considering both the geometry and specific ionic currents. - **Synaptic Components**: Terms like `back_syn`, `mut_syn`, and `in_syn` suggest various synapses contributing to the LFP. These might represent different types of synaptic connections (e.g., recurrent, mutual, inhibitory). 5. **Inverse Distance Dependence**: - The LFP computation includes distance-based attenuation, where potentials decouple with distance from the source (`count_distance()` function). This mirrors the biological reality in which the influence of currents on extracellular potentials decreases with increased distance. 6. **Conductivity of Brain Tissue**: - The parameter `sigma = 0.3 [S/m]` is a measure of brain tissue conductivity based on reported values (Lindén et al., 2014), indicating the medium's ability to transmit electrical signals. Through the integration of these biological elements, the model simulates how neurons contribute to LFPs, providing insights into neural dynamics and potentially aiding in interpretations of electrophysiological recordings.