Parameterized phase response curves for characterizing neuronal behaviors under transient conditions

Area of Science:

• Computational Neuroscience
• Electrophysiology
• Neural Dynamics

Background:

• Phase Response Curves (PRCs) are crucial for modeling neural network behavior but face challenges with non-stationary firing rates.
• Existing PRC models do not adequately capture the dynamic firing rate dependency of neuronal responses to synaptic input.

Purpose of the Study:

• To develop and validate a novel method for estimating neuron Phase Response Curves (PRCs) that are dependent on the neuron's firing rate.
• Introduce the parameterized PRC (pPRC) model to account for firing rate variations in synaptic integration.

Main Methods:

• A two-part current perturbation was applied to neurons: a rate-setting constant current and a synaptic input pulse.
• Developed models to predict interspike intervals and fit spike time modulation by the synaptic stimulus.
• Modeled the parameterized PRC (pPRC) using a polynomial function of stimulus phase and expected interspike interval.

Main Results:

• Successfully developed and validated the parameterized PRC (pPRC) model in computational simulations.
• Applied the pPRC model to pyramidal neurons in rat hippocampal slices, demonstrating its experimental applicability.
• The pPRC effectively models the influence of changing firing rates on spike timing and network synchrony.

Conclusions:

• The parameterized PRC (pPRC) offers a robust method to characterize neuronal responses across varying firing rates.
• pPRCs can be used to analyze how neuromodulators, genetic factors, and other manipulations impact network synchrony.
• The pPRC framework is extensible for incorporating additional variables influencing neuronal dynamics.