The code provided models a gap junction mechanism that is modulated by GABAergic input through a blockade effect. Below is a breakdown of the biological basis of the elements in this computational model:

Gap Junctions

• Definition: Gap junctions are specialized intercellular connections that allow for direct electrical communication between neurons. They facilitate the passage of ions and small signaling molecules, enabling rapid and bidirectional transmission between cells.
• Electrical Synapse: Unlike chemical synapses where neurotransmitters mediate transmission, gap junctions form channels that allow ionic currents to flow directly between the neurons.

GABAergic Input

• GABA: Gamma-Aminobutyric Acid (GABA) is the primary inhibitory neurotransmitter in the mammalian central nervous system. Its primary function is to reduce neuronal excitability by binding to GABA receptors and causing hyperpolarization.
• Blockade Mechanism: The model simulates a blockade effect through GABAergic input on the gap junction. The blockade reduces the conductance (or permeability) of the gap junction, thereby modulating the electrical coupling between neurons.

Key Biological Parameters

• Tau Parameters: tau1 and tau2 are time constants that might correspond to the dynamics of biological processes that determine how quickly a gap junction can be affected by GABAergic modulation. These could model the kinetic properties of the gating mechanisms that allow or prevent ionic flow through gap junctions.
• Maxblock: This parameter represents the maximum possible blockade level exerted on the gap junction conductivity by GABAergic input.
• Noise: The noise parameter introduces stochastic variations in the current, representing the biological variability and random fluctuations observed in synaptic activity.

Mechanistic Details

• Conductance Modulation: The conductance of the gap junction is modulated by a state variable cc, which is influenced by the degree of GABAergic blockade. The model uses a hyperbolic tangent function (tanh) to simulate the dose-response relationship between the neurotransmitter effect and its blockade level.
• Dynamic States: The states A and B reflect the dynamic processes that influence the opening and closing of the gap junctions under synaptic input, modeled here by the weight factor.

Conclusion

This computational model provides a framework for understanding how gap junctions, fundamental elements of neuronal communication, are regulated by inhibitory synaptic inputs in a time-dependent manner. Through this model, researchers can explore the dynamic interplay between electrical coupling and inhibitory signaling, offering insights into neural network synchronization and modulation.