The provided code is simulating the Local Field Potential (LFP) generated by a network of neurons. The LFP is a measure of electric potentials generated by the action of multiple neurons, commonly used in neuroscience to infer neuronal activity and network dynamics. Here's an exploration of the biological elements modeled by the code:
Total Neurons: The code simulates the activity of 440 neurons (total_no_neurons = 440). This number can relate to a small cortical column or another localized region of the brain, aiming to study synaptic activities and network interactions.
Trial and Neuron Assignment: Each neuron is randomly assigned to a trial number, which corresponds to different experimental conditions or initial states using randi. This can reflect variability in neuronal response or different synaptic input conditions.
Recording Nodes: The code uses 8 recording points (n=8), which simulate electrodes strategically placed to capture the electrical activity generated by neurons. The contacts on the electrode (pt_coord) specify positions probably along or around a brain slice or other neural structure.
Resistivity and Conductivity: The extracellular resistivity (resistivity= 3.33 Ω m) affects signal propagation of potentials. Sigma (sigma = 1/resistivity) represents the conductivity of the extracellular medium.
Line Currents and Voltage Calculation: The code reads line currents from text files which correspond to the currents flowing along neuronal processes like axons and dendrites. The method get_phi_line computes the potential at recording sites across these lines. This calculation incorporates parameters h, R, and ds, representing the geometry and orientation of the neurons in space, important for modeling their bioelectrical properties.
Voltage Segmentation: The LFPs are calculated over a considerable temporal resolution (tcount= ((tstop) *40)) over a time period (tstop=1545). During the simulation, voltages from individual lines are summed to form a total potential at each recording node, reflecting cumulative neuronal activity.
The model captures the dynamics of endogenous electric fields resulting from synaptic and axonal currents. This includes the influence of:
Synaptic Activity: The synaptic influences are indirect but critical; the currents are also driven by synaptic inputs applied to these neurons, making it possible to infer network dynamics and connectivity.
Spatially Resolved Intracellular Dynamics: Each neuron's contribution is informed by its spatial geometry (h, R), which directly affects extracellular field potentials measurable around neural structures.
In conclusion, the code aims to model and understand how the aggregated synaptic and axonal currents from a network of neurons generate LFPs, offering insights into the underlying biophysical mechanisms of neuronal interactions and community activity within a defined biological substrate.