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The Power of Interstimulus Interval for the Assessment of Temporal Processing in Rodents

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Published on: April 19, 2019

Can poststimulus time histograms be used to test for convergence?

E Palmer¹, P Ashby

¹Playfair Neuroscience Unit, Toronto Western Hospital, University of Toronto, Toronto, Ontario, Canada.

Journal of Electromyography and Kinesiology : Official Journal of the International Society of Electrophysiological Kinesiology

|September 28, 2010

Summary

This study shows that poststimulus time histograms (PSTHs) can detect convergence onto interneurons. Understanding potential errors, like superimposed excitatory postsynaptic potentials (EPSPs), is key for accurate analysis in human neurophysiology.

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Area of Science:

Neuroscience
Computational Neuroscience
Human Physiology

Background:

Poststimulus time histograms (PSTHs) analyze postsynaptic potentials (PSPs) in single human motoneurons.
Detecting convergence onto interneurons typically involves intracellular recordings in animal models.

Purpose of the Study:

To investigate if PSTHs can be extended to detect convergence onto interneuron pools in humans.
To model PSP summation and predict potential errors in PSTH-based experiments.

Main Methods:

Modeled postsynaptic potentials (PSPs) and their summation.
Simulated the resulting PSTHs in a repetitively discharging neuron.
Analyzed the impact of PSP timing and type on PSTH summation.

Main Results:

Algebraic summation of PSTH peaks occurs when PSPs are perfectly superimposed.
Non-ideal superposition of excitatory postsynaptic potentials (EPSPs) can lead to underestimation of convergence (occlusion).
EPSP-IPSP combinations may falsely suggest facilitatory convergence.

Conclusions:

PSTHs can detect non-algebraic PSP summation, indicating convergence onto interneurons.
Understanding model-predicted errors is crucial for accurate interpretation of human neurophysiological data.
This technique offers a potential non-invasive method for studying human interneuron networks.