The provided code is part of a computational neuroscience model designed to simulate inhibitory synapses mediated by the neurotransmitter gamma-aminobutyric acid (GABA). This synapse model is configured to mimic the biological effects of GABA receptors, particularly the GABA_A subtype, which is crucial for fast synaptic inhibition in the central nervous system. ### Biological Basis 1. **Inhibitory Synapses and GABA:** - **GABA** is the primary inhibitory neurotransmitter in the mammalian central nervous system. It plays a crucial role in reducing neuronal excitability by promoting inhibitory postsynaptic potentials (IPSPs). - **GABA_A Receptors** are ionotropic receptors that, upon activation by GABA, open chloride channels. This allows the influx of Cl^- ions, leading to hyperpolarization of the postsynaptic membrane and reducing the likelihood of an action potential. 2. **Key Biological Parameters Modeled:** - **Tau_d (`tau_d`)**: This represents the decay time constant of the synaptic current (in milliseconds). It signifies how quickly the GABAergic current diminishes, affecting the duration of inhibition. - **Fraction of Recovery (`frac_rec`)**: This indicates the proportion of receptors that recover and are ready for activation after synaptic transmission, impacting synapse readiness and efficacy. - **Reversal Potential (`e`)**: This is set to a hyperpolarizing potential, often near -70 to -80 mV, reflecting the resting potential of neurons and ensuring the inhibitory effect of GABA. - **Synaptic Strength (`g`)**: This dimensionless factor influences the conductance change produced by synaptic activation, thus modulating the impact on postsynaptic potential. 3. **Biophysical Characteristics:** - The **GABA-mediated synapse** in this model affects both excitatory (E) and inhibitory (I) neurons with different synaptic strength factors (`g_factor`). This reflects the modulation of network dynamics where inhibitory interneurons can influence the overall excitatory/inhibitory balance in neural circuits. 4. **Cellular and Network Context:** - The model considers different cellular compartments (e.g., soma, dendrites) to simulate the spatial dynamics of synaptic input. Dendritic and soma-targeted synapses can have differing impacts on neuronal output due to the passive and active properties of dendrites. - **Surface Area (`area_cell`)**: This biophysical feature can affect the conductance and amplitude of inhibitory currents, as it impacts the distribution and density of receptors. In essence, the code simulates the dynamic properties of GABAergic synapses, capturing essential features like temporal dynamics (tau_d), synaptic plasticity (frac_rec), and the ionic basis of inhibition (reversal potential), facilitating the exploration of inhibitory mechanisms in neural network models.