The provided code models a graded synapse with first-order binding kinetics, specifically focusing on the inhibitory mechanisms within neuronal circuits. Below are the key biological aspects represented in the code:
Graded synapses differ from traditional synaptic models that rely on spikes (action potentials) for neurotransmitter release. Instead, graded synapses are more common in certain neural systems (e.g., retinal circuits), where neurotransmitter release is directly related to the membrane potential of the presynaptic neuron. This model captures how synaptic conductance is modulated based on a continuous range of presynaptic voltage levels rather than all-or-nothing spiking.
The model represents an inhibitory synapse as suggested by the negative reversal potential e = -70 mV, likely corresponding to chloride ions or another negatively charged ion gradient. Inhibitory synapses generally hyperpolarize the postsynaptic neuron, making it less likely to reach the threshold for action potentials.
Conductance g in the model is controlled by a gating variable s, which undergoes dynamic changes based on the voltage of the presynaptic neuron (V_pre). The maximum conductance g_max is negative, indicating inhibitory effects on the postsynaptic neuron.
The model uses a state variable s to represent the proportion of open synaptic channels. The change in s is computed using a differential equation that considers the steady-state value s_inf, which is dependent on V_pre. This differential equation reflects first-order synaptic binding kinetics, encapsulating the idea that synaptic conductance is a function of neurotransmitter binding and unbinding dynamics.
The parameters V_thr and V_slope are used to calculate s_inf. V_thr is a threshold potential above which synaptic transmission is altered, influenced by the presynaptic neuron's potential. V_slope affects the sensitivity of the synaptic response to changes in V_pre.
Certain conditions such as bounding s between 1e-06 and 1 ensure the gating variable remains within biologically realistic limits, preventing non-physical values for synaptic conductance.
Overall, this code simulates the dynamics of a graded inhibitory synapse, with a focus on how presynaptic voltage influences synaptic transmission via first-order binding kinetics and continuous modulation of synaptic conductance. This allows for exploring how such synapses contribute to neural circuit function and information processing.