A computational study of spike time reliability in two types of threshold dynamics

Area of Science:

• Computational Neuroscience
• Neuronal Dynamics
• Signal Processing

Background:

• Spike time reliability (STR) describes a neuron's ability to consistently fire action potentials at precise times in response to repeated identical stimuli.
• STR is influenced by both the neuron's intrinsic properties and the characteristics of the applied external signals.
• Previous studies have explored STR in various neuronal states, but its dependence on specific input features in quiescent neurons near oscillation thresholds requires further investigation.

Purpose of the Study:

• To numerically analyze spike time reliability (STR) in a quiescent model neuron.
• To investigate the influence of spike-evoking epochs (SEEs) and input signal properties on STR.
• To compare STR in Type I and Type II neurons concerning their frequency-current (f-I) relationships.

Main Methods:

• Numerical simulations of a model neuron near the onset of oscillations.
• Analysis of averaged properties and individual features of spike-evoking epochs (SEEs).
• Minimization of spike interactions by using signals with long interspike intervals (ISIs).

Main Results:

• Input signal frequency content has minimal impact on STR when interspike intervals are long.
• Spike time reliability (STR) in Type I and Type II neurons exhibits both commonalities and differences.
• Individual SEE time profiles are crucial for precise spike timing, often more so than the overall 'action' (average current) of the signal.

Conclusions:

• Precise spike timing in quiescent neurons is significantly influenced by the temporal characteristics of individual spike-evoking epochs (SEEs).
• Favorable SEE time profiles can lead to higher spike timing precision, even at lower levels of input signal strength.
• The study provides insights into the complex interplay between neuronal excitability, input signal dynamics, and reliable neural information processing.