The provided code models the presynaptic release of the neurotransmitter gamma-aminobutyric acid (GABA) and its effects on a postsynaptic neuron within a neural network. This computational model is intended to simulate GABAergic synaptic interactions with a focus on processes involved in neurotransmitter release, synaptic vesicle dynamics, and postsynaptic response. ### Biological Basis #### Presynaptic Release Mechanism - **Voltage Dependence:** The code models GABA release as dependent on the presynaptic voltage (`Vpre`). This mirrors biological scenarios where the depolarization of the presynaptic neuron influences neurotransmitter release, albeit GABAergic systems being less directly dependent on high voltage spikes compared to glutamatergic systems. - **Vesicle Dynamics:** - **Vesicle Stockpile:** The parameter `maxves` represents the total number of vesicles available for release, simulating the readily releasable pool (RRP) of vesicles within an axon terminal. - **Release Probability (`s_inf`):** This is modeled as proportional to the presynaptic voltage (`Vpre`), linking electrical activity with the probability of vesicle fusion and neurotransmitter exocytosis. - **Replenishment (`newves`):** The code includes a replenishment mechanism to replace vesicles after they have been released, reflecting the biological process of vesicle recycling and reserve pool dynamics. #### Postsynaptic Response - **GABA Transmission:** - **Conductance (`g`):** Represents the GABA-induced conductance change in the postsynaptic neuron, which affects the neuron's membrane potential and ability to fire action potentials. - **Gating Variable (`tau`):** Reflects the time constant for the post-synaptic channel gating, important for capturing the decay characteristics of post-synaptic currents following neurotransmitter binding. - **Inhibition Effect:** The reversal potential parameter (`e = -65 mV`) is typical for chloride ion channels associated with GABA_A receptor activation, thus simulating the hyperpolarizing (inhibitory) effect of GABA on the postsynaptic neuron. #### Additional Modeling Aspects - **Noise and Randomness:** The use of a pseudo-random function (`scop_random()`) reflects the inherent stochastic nature of vesicle release in synaptic transmission. - **Spatial Parameters (`locx`, `locy`):** While not crucial to the biological mechanism per se, these parameters might be used to simulate the neuron’s spatial properties and connectivity in a larger, not included, model space. This model captures essential elements of GABAergic synaptic transmission, focusing on the role of voltage in presynaptic release and the slow postsynaptic conductance changes typical of inhibitory signaling, thereby providing insights into the dynamics and effects of inhibitory synaptic interactions in neural circuits.