The provided code models the electrical coupling between fast-spiking (FS) neurons mediated through gap junctions, which are specialized structures allowing direct cytoplasmic connectivity between neurons. Here is an overview of the biological concepts embodied in the code:
Fast-spiking (FS) neurons are a type of inhibitory interneuron characterized by their ability to fire rapid, successive action potentials. They play key roles in neuronal network oscillations and synchronization within neural circuits. FS neurons are known to have distinctive electrophysiological properties allowing them to respond quickly to synaptic inputs, helping to regulate the timing of activity in neural circuits.
Gap junctions are assemblies of intercellular channels that allow direct electrical communication between neurons. Unlike chemical synapses, which use neurotransmitters to relay signals, gap junctions enable ions and small molecules to flow directly between neighboring cells. This coupling allows for rapid and bidirectional transmission of electrical signals, facilitating synchronization among neurons, particularly in the context of oscillatory activity within neural networks.
In the simulation, the FS neurons are driven by direct current injections. This is a common method in computational and experimental studies to control the activity of neurons. By specifying the timing (curStart, curEnd) and amplitude (curAmp) of the current, the model simulates external influences or experimental manipulations, which might be employed to mimic the effect of synaptic input or other neural inputs.
Parameter Reading: The code reads critical simulation parameters like the number of cells (numCells), simulation duration (maxTime), and output specifications from an external file. These parameters set the context for the biological modeling by determining the scale and scope of the network being simulated.
Current Input and Injection: The simulation utilizes data from a file, currentInputInfo.txt, to determine where and how current injections occur, representing the direct stimulation or spontaneous synaptic inputs that FS neurons might experience in a biological system.
Gap Junction Modeling: The code simulates the presence of gap junctions between neurons by reading their configuration from the parameter file. It establishes connections based on detailed specifications of pre- and postsynaptic neurons (gapSrc, gapDest) and the resistance of the gap junction (gapRes), which influences the electrical coupling strength. This allows for the modeling of synchronized activity that is characteristic of gap junction-connected FS neurons.
Simulation Time Steps: The code operates with fine temporal discretization (simDt) to accurately capture the rapid dynamics characteristic of FS neurons, which is crucial for modeling their fast-spiking behavior and temporal synchronization via gap junctions.
The modeled FS neuron network with gap junction connections captures essential biological phenomena of electrical coupling and synchrony in neural circuits. This setup provides insights into how FS neurons interact and synchronize, highlighting the role of gap junctions in neural communication and network dynamics. The use of current injection to drive the neurons further allows the exploration of how FS neurons respond to external stimuli, reflecting real-world experimental manipulations.