Volterra representation enables modeling of complex synaptic nonlinear dynamics in large-scale simulations

Area of Science:

• Computational neuroscience
• Synaptic plasticity modeling
• Biophysics of neuronal signaling

Background:

• Chemical synapses exhibit complex signaling dynamics crucial for neural computation.
• Current simplified synapse models fail to capture essential nonlinear dynamics.
• Accurate modeling of synaptic transmission is vital for understanding neural networks.

Purpose of the Study:

• To develop a computationally efficient input-output (IO) synapse model.
• To accurately represent the nonlinear dynamics of synaptic transmission.
• To enhance the realism of neuron network simulations.

Main Methods:

• Extended a detailed mechanistic glutamatergic synapse model.
• Employed the Volterra functional power series to capture input-output relationships.
• Validated the IO model against a mechanistic model and kinetic models in compartmental neuron simulations.

Main Results:

• The IO synapse model accurately tracked nonlinear synaptic dynamics up to the third order.
• Model performance was evaluated across various input frequencies.
• The IO model demonstrated efficient replication of complex nonlinear dynamics.

Conclusions:

• The proposed IO synapse model offers a method to efficiently replicate complex synaptic transmission.
• This model enhances the capability of neuron network simulations to represent diverse synaptic behaviors.
• The IO model balances accuracy in nonlinear dynamics with computational efficiency.