Quantification and statistical verification of neuronal stimulus responses from noisy spike train data

Area of Science:

• Neuroscience
• Computational Neuroscience
• Biophysics

Background:

• Peri-stimulus time histograms (PSTHs) are standard for visualizing neuronal responses to stimuli.
• PSTHs struggle to interpret secondary response components, which may be artifacts or true neural activity.
• Accurate quantification of neuronal stimulus responses is crucial for understanding neural circuits.

Purpose of the Study:

• To develop novel output functions for quantifying neuronal stimulus responses.
• To overcome the limitations of traditional peri-stimulus time histograms, particularly regarding secondary response components.
• To provide methods for analyzing stimulus-correlated spike train data with high inter-spike interval variability.

Main Methods:

• Development of two new neuronal output functions: the interspike interval change function and the displaced impulses function.
• Simulation of neuronal responses using a Hodgkin-Huxley-type model of a mammalian neuron.
• Experimental validation using human soleus motoneurons stimulated via the tibial nerve.

Main Results:

• The interspike interval change function quantifies neuronal stimulus response amplitude, showing independence from stimulation strategy.
• The displaced impulses function enables verification of the statistical significance of observed response components.
• Both functions effectively analyze stimulus-correlated spike trains, even with high intrinsic neuronal variability.

Conclusions:

• The interspike interval change and displaced impulses functions offer improved methods for analyzing neuronal responses to stimuli.
• These novel functions provide more accurate interpretations of neuronal activity compared to traditional peri-stimulus time histograms.
• The developed methods are applicable to both simulated and experimentally recorded neuronal data, enhancing the study of neural dynamics.