Analytical solutions for the short-term plasticity

Synaptic dynamics plays a key role in neuronal communication. Due to its high dimensionality, the main fundamental mechanisms triggering different synaptic dynamics and their relation with the neurotransmitter release regimes (facilitation, biphasic, and depression) are still elusive. For a general set of parameters, and employing an approximated solution for a set of differential equations associated with a synaptic model, we obtain a discrete map that provides analytical solutions that shed light on the dynamics of synapses. Assuming that the presynaptic neuron perturbing the neuron whose synapse is being modelled is spiking periodically, we derive the stable equilibria and the maximal values for the release regimes as a function of the percentage of neurotransmitter released and the mean frequency of the presynaptic spiking neuron. Assuming that the presynaptic neuron is spiking stochastically following a Poisson distribution, we demonstrate that the equations for the time average of the trajectory are the same as the map under the periodic presynaptic stimulus, admitting the same equilibrium points. Thus, the synapses under stochastic presynaptic spikes, emulating the spiking behaviour produced by a complex neural network, wander around the equilibrium points of the
synapses under periodic stimulus, which can be fully analytically calculated.