The code provided is a computational model of a GABA(_A) receptor-mediated synapse. This model captures the dynamics of synaptic transmission modulated by GABA (gamma-aminobutyric acid), the main inhibitory neurotransmitter in the central nervous system. Below are the key biological aspects that the code simulates:
i): This model calculates the inhibitory post-synaptic current produced due to the activation of GABA(_A) receptors. The current is dependent on the conductance (ggaba) and the driving force given by the difference between the membrane potential (v) and the reversal potential (e) for GABA, which is set at -60 mV.Conductance (ggaba): The code models the synaptic conductance changes over time, represented by variables R (rise of conductance) and D (decay of conductance), typical in response to neurotransmitter release. The conductance change results in a transient increase, followed by an exponential decay back to baseline.
Rise and Decay Times: The rise time (risetime) and decay time (decaytime) constants capture the kinetics of the GABA(_A) receptor opening and closing. These correspond to the rapid activation and slower deactivation phases observed biologically.
Facilitation (fgaba) and Depression (dgaba): The model incorporates mechanisms of synaptic plasticity, specifically facilitation and short-term depression. Facilitation is modeled as the temporary increase in synaptic strength following repeated stimulation. Depression reflects the transient weakening due to neurotransmitter vesicle depletion or receptor desensitization.
Time Constants: The facilitation (taufgaba) and depression (taudgaba) time constants control the recovery rates of these processes.
Weight and Stimulation: Upon a synaptic event (NET_RECEIVE block), the model calculates updates to the synaptic conductance based on the weight parameter. This represents the strength of the presynaptic input or the amount of neurotransmitter released.
Normalization Factor: A conductance normalization factor (factor) is used to ensure that the peak conductance remains consistent across varying time courses of rise and decay phases.
The model reflects a standard kinetic scheme for a synapse with the incorporation of short-term synaptic plasticity. It is suitable for simulating the inhibitory effects of GABAergic inputs in neural network models, helping to stabilize neuron firing and integrate inputs over time. These properties are essential for diverse neural computations, including rhythm generation, timing precision, and network oscillations.
Overall, this synapse model aims to reproduce typical GABA(_A) receptor behaviors as observed in biological systems and forms a crucial component of any computational framework seeking to emulate the inhibitory synaptic transmission in the brain.