The provided code simulates a single synaptic input in a computational model, with a focus on gamma-aminobutyric acid (GABA) synapses. Here are the key biological aspects of the code:

GABAergic Synapses

γ-aminobutyric acid (GABA) is the primary inhibitory neurotransmitter in the mammalian central nervous system. Inhibitory synapses using GABA help regulate neuronal excitability by hyperpolarizing postsynaptic neurons, making it less likely for an action potential to be generated.

Key Biological Details Modeled

1. Synaptic Conductance (G_GABA):

   • The synaptic strength is represented by G_GABA, equivalent to 0.1 nS (10 pS). This value determines the maximum conductance change produced by the synaptic event, reflecting the effect of neurotransmitter binding to the receptor.

2. Synapse Location (GABA_SYN_LOCATION):

   • The synapse is simulated on a dendritic compartment, specifically at a fraction (0.5) of the dendritic length. This spatial factor is relevant in neuromodulation, as the location can affect the integration of synaptic inputs.

3. Decay Time Constant (DECAY_GABA):

   • The decay parameter, set to 50000 ms, corresponds to the time it takes for the synaptic conductance to fall to a fraction of its peak value after activation. This slow decay can model prolonged inhibitory postsynaptic potentials (IPSPs) potentially representing tonic inhibition or long-lasting synaptic effects.

4. Onset and Number of Pulses (ONSET_PULSE, ANZAHL_GABA):

   • The syncing of neurotransmitter release is managed by onset time at 50 ms and a set number of pulses (ANZAHL_GABA = 1). Timely neurotransmitter release initiation is crucial for temporal synaptic processing.

5. Ionic Concentration:

   • HCO3e_gaba and HCO3i_gaba specify the extracellular and intracellular bicarbonate concentrations, impacting the reversal potential and overall synaptic current. The difference between these values can influence the ionic driving force and the inhibitory action of the GABAergic synapses, reflecting physiological ionic gradients.

Biological Interpretation

The model specifically simulates a GABAergic synapse on a dendrite's midpoint, incorporating a single GABAergic pulse with specified conductance and detailed timing characteristics. Synaptic parameters, such as decay time and location, are critical for dictating the efficacy and timing of inhibition in computational neurons. These synaptic events emulate inhibitory control over neuronal firing patterns, emphasizing the regulation of neuronal network activity.

By encoding such synaptic inputs, the model emulates a simplified version of biological inhibition to study neuronal response to inhibitory synapses, exploring its timing, magnitude, and coupling with dendritic properties.

Overall, the model offers insights into the role of GABAergic synapses in modulating neuronal excitability and synaptic integration, playing a pivotal role in neurological processes such as rhythm generation, synaptic plasticity, and feedback inhibitory circuits.