ExpSyn

ExpSyn is a simple, conductance-based excitatory synapse model used in computational neuroscience, notably within the NEURON simulation environment. It characterizes synaptic input as a conductance change that rises essentially instantaneously at a presynaptic spike and then decays exponentially with a single time constant. The resulting synaptic current is I_syn = g_syn · (V_m − E_rev), where g_syn is the time-varying conductance and E_rev is the reversal potential for the excitatory receptor.

Key parameters of ExpSyn include the peak conductance gmax (the maximum gain reached after a spike), the decay time constant tau (the time over which the conductance falls exponentially), and the reversal potential E_rev (typically set near 0 mV for AMPA-like excitation). When a presynaptic event arrives, the model increases g_syn by a fixed amount (often proportional to gmax), after which g_syn decays according to the exponential law governed by tau. This mechanism is commonly driven by NetCon or other event delivery systems in the simulation environment.

ExpSyn is valued for its simplicity and computational efficiency, making it a standard choice for modeling fast excitatory inputs in neuronal networks. It provides a baseline against which more complex kinetics can be compared. A related variant, Exp2Syn, uses two exponentials to capture a finite rise time in addition to the decay. Limitations include the lack of an explicit rise phase and more detailed receptor dynamics, which can be addressed by more complex models when necessary.