The code provided models the passive leak current in neurons, an essential aspect of the cell's electrical properties. Here, the model is implemented in the NEURON simulation environment, which is widely used for simulating neurons and neural networks.
Leak Currents: In biological neurons, leak currents are non-specific ionic currents that flow through the cell membrane, and are crucial for maintaining the resting membrane potential. They result from the passive diffusion of ions across the membrane through channels that are open at rest.
Membrane Potential: The code models how ions such as sodium (Na+), potassium (K+), and chloride (Cl-) may passively move across the neuron's membrane due to concentration gradients. These movements contribute to the 'leak' of electric charge, influencing the overall membrane potential.
Conductance (gbar): The parameter gbar represents the maximum conductance of the leak channels per unit area (in Siemens per centimeter squared, S/cm²). Conductance measures how easily ions pass through the channels, and in this model, it is set to a specific rate (here, (9 \times 10^{-5}) S/cm²), reflecting the density and configuration of these passive channels.
Reversal Potential (e): The reversal potential e (also known as the equilibrium potential) is the membrane potential at which no net ionic flow occurs through the leak channels, modeled here as -61 mV. This value represents the driving force for ions moving through the leak channels, balancing electrical and chemical gradients.
Homeostasis: Leak currents help maintain and stabilize the resting membrane potential, a crucial aspect of the neuron's ability to respond to synaptic input and generate action potentials.
Signal Integration: While action potentials are the signals of active neuronal communication, leak currents continuously influence how neurons respond to inputs by affecting the baseline electrical environment.
Neuronal Excitability: By affecting the resting membrane potential, leak currents indirectly influence neuronal excitability, determining how readily a neuron can fire an action potential in response to synaptic inputs.
Overall, the code simulates the passive properties of neuron membranes, focusing on the ionic movement through leak channels and their impact on maintaining crucial electrical properties. Modeling leak currents aids in understanding various cellular processes, from basic neuronal function to the integration of synaptic inputs in complex circuitry.