Parametric and non-parametric modeling of short-term synaptic plasticity. Part II: Experimental study

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Short-term plasticity (STP) significantly influences information processing in neural circuits.
• Existing parametric models of synaptic dynamics offer biological interpretability but may lack predictive accuracy.
• Non-parametric models can capture complex system dynamics from experimental data.

Purpose of the Study:

• To synergistically model short-term plasticity (STP) in the Schaffer collateral to hippocampal CA1 pyramidal neuron (SC) synapse.
• To compare the predictive power of parametric and non-parametric models for synaptic dynamics.
• To refine parametric models using insights from non-parametric approaches for improved biological interpretability.

Main Methods:

• Development of parametric models based on differential and algebraic equations representing biological mechanisms.
• Derivation of non-parametric Poisson-Volterra models from broadband experimental input-output data.
• Validation of parametric models against experimentally-derived non-parametric models to assess synaptic nonlinear dynamics.

Main Results:

• The non-parametric model provided superior prediction of experimental output compared to a single-process parametric model.
• Discrepancies between models indicated the presence of multiple facilitation/depression (FD) processes in SC synapses.
• An enhanced parametric model incorporating additional FD processes better replicated the non-parametric model's characteristics.

Conclusions:

• Non-parametric modeling offers a comprehensive representation of synaptic nonlinear dynamics.
• Refined parametric models, informed by non-parametric analysis, achieve improved predictive accuracy and retain biological interpretability.
• The study suggests multiple FD processes are crucial for accurately modeling SC synapse short-term plasticity.