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 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. ### Inhibitory Synaptic Transmission 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. ### Synaptic Conductance 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. ### Binding Kinetics and State Variables 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. ### Threshold and Slope Factors 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`. ### Homeostatic Constraints 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.