The code provided is a model of electrical synapses, often referred to as "gap junctions," in a computational neuroscience setting. Gap junctions are specialized intercellular connections that directly connect the cytoplasm of two cells, allowing certain ions and molecules to pass freely between cells. This electrical coupling enables rapid and direct transmission of electrical signals, differing from the chemical synapses that rely on neurotransmitter release.
Point Process: The use of POINT_PROCESS indicates a mechanism that can be attached to specific points in a neural network model, similar to synaptic connections. Here, it represents a gap junction responsible for transferring ionic current between cells.
Nonspecific Current (i): Gap junctions allow the flow of ions like K(^+), Na(^+), and Cl(^-), constituting a nonspecific ionic current. This current is computed in the model as the difference between the membrane voltage of connected cells, modulated by the resistance of the junction.
Voltage Difference: The differential voltage (v - vgap) represents the driving force for ionic flow via the junction. In biological terms, this reflects the differences in membrane potential between two neurons that promote the passive flow of ions.
Resistance (r): The parameter r symbolizes the resistance across the gap junction, influenced by the conductivity of connexin proteins forming the junction. High resistance implies less conductance, affecting the speed and amplitude of signal transmission.
Rapid Communication: In biological systems, gap junctions facilitate rapid and reliable communication between neurons, playing critical roles in synchronizing neuronal activity, such as in the oscillatory behavior observed in certain neural circuits involved in rhythmic activities like breathing.
By modeling these dynamics, computational neuroscientists can study how electrical coupling via gap junctions impacts neuronal network activities, synchronization, and information processing in the brain.