The provided code models a graded synaptic response using a computational model of a synapse with first-order binding kinetics, specifically designed for inhibitory synaptic transmission. This type of model is commonly utilized in computational neuroscience to simulate the graded nature of synaptic conductance changes in response to varying presynaptic membrane potentials.
Point Process for Synaptic Modeling:
The model uses a POINT_PROCESS to represent a synapse that produces a postsynaptic current (i). This abstraction is often used to simulate synaptic interactions within neuronal models by describing the conductance change dynamics across the synapse.
Conductance-Based Synapse:
A crucial aspect of synaptic modeling is the use of conductance (g) to represent the strength and dynamics of the synaptic response. The term g_max represents the maximal conductance, indicative of the peak synaptic influence on the postsynaptic cell when the presynaptic stimulus is at its maximum efficacious level.
Inhibitory Synapse:
The equilibrium potential (e = -70 mV) suggests an inhibitory synaptic effect. This potential is typically associated with chloride ions (Cl^-), which mediate inhibitory postsynaptic potentials (IPSPs). The negative value of g_max further supports the inhibitory nature, as it implies a hyperpolarizing effect on the postsynaptic neuron when the synapse is active.
Graded Synaptic Response:
The model supports a graded synaptic transmission through variables like V_pre, V_thr, and V_slope. Graded synapses are distinguished by their ability to produce variable conductance changes based on the presynaptic membrane potential (V_pre). The V_thr and V_slope parameters define the threshold and sensitivity of the synaptic response to presynaptic voltage changes.
First-Order Kinetics:
This model applies first-order kinetics to the synaptic state variable s, which represents the synaptic conductance state. The kinetic model captures the transition dynamics between inactive and active synaptic states, where the rate of change is dependent on the difference between the steady-state conductance (s_inf) and the current state, modulated by tau, the time constant.
State Variable Dynamics:
The s variable obeys differential equations to simulate the continuous transition in conductivity over time, constrained by preset bounds (0 to 1) ensuring realistic physiological activity levels during simulation. This maintains stability in the simulations and simulates biologically realistic activation and deactivation dynamics of synaptic receptors, such as GABA_A receptors.
Overall, this code snippet models a graded inhibitory synapse with conductance changes influenced by presynaptic voltage dynamics, providing insights into inhibitory signal transmission's temporal and quantitative characteristics in neural circuits.