Parametric and non-parametric modeling of short-term synaptic plasticity. Part I: Computational study

Area of Science:

• Neuroscience
• Computational Biology
• Systems Biology

Background:

• Short-term plasticity (STP) significantly influences neural information processing in the central nervous system (CNS).
• Understanding STP dynamics is crucial for deciphering complex neural circuit functions.

Purpose of the Study:

• To combine parametric and non-parametric modeling approaches for studying synaptic short-term plasticity (STP).
• To evaluate the efficacy of non-parametric Volterra kernels in representing STP dynamics derived from parametric models.

Main Methods:

• Utilized previously established parametric models based on mechanistic hypotheses for STP.
• Employed non-parametric Volterra kernels to model the transformation of presynaptic to postsynaptic signals.
• Estimated Volterra kernels from synthetic input-output data for four distinct synapse types.

Main Results:

• Non-parametric models, represented by Volterra kernels, accurately replicated the input-output transformations of parametric STP models.
• Volterra kernels demonstrated efficiency in capturing the nonlinear dynamics of STP.
• This approach provides a general and quantitative method for representing STP.

Conclusions:

• Non-parametric Volterra kernels offer a robust and versatile tool for characterizing synaptic short-term plasticity.
• The synergistic use of parametric and non-parametric models enhances the understanding of STP in the CNS.
• This modeling framework facilitates quantitative analysis and prediction of synaptic behavior.