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Related Experiment Video

Updated: Jul 4, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Population dynamics: variance and the sigmoid activation function.

André C Marreiros¹, Jean Daunizeau, Stefan J Kiebel

¹Wellcome Trust Centre for Neuroimaging, University College London, UK. a.marreiros@fil.ion.ucl.ac.uk <a.marreiros@fil.ion.ucl.ac.uk>

|June 13, 2008

Summary

This summary is machine-generated.

This study reveals that the sigmoid function in neural-mass models relates to neuronal state variance. This finding helps estimate hidden neuronal states using EEG and dynamic causal modeling for better understanding cortical dynamics.

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Published on: December 7, 2021

Area of Science:

Computational neuroscience
Neuroimaging analysis

Background:

Neural-mass models are crucial for understanding brain dynamics.
The sigmoid activation function is a key component in these models.
Interpreting the sigmoid function's role in neuronal state dynamics is essential.

Purpose of the Study:

To demonstrate the relationship between the sigmoid activation function and neuronal state variance.
To develop a method for estimating probability density on hidden neuronal states.
To highlight the significance of neuronal state variance in cortical dynamics.

Main Methods:

Utilizing dynamic causal modeling (DCM) with non-invasive electrophysiological (EEG) measures.
Analyzing the variance or dispersion of neuronal states.
Applying the sigmoid-variance relationship to estimate probability densities.

Main Results:

Established a direct link between the sigmoid function and neuronal state variance.
Successfully estimated probability densities on hidden neuronal states.
Demonstrated the importance of implicit variance in synthetic and real EEG data.

Conclusions:

The sigmoid function in neural-mass models can be interpreted through neuronal state variance.
This provides a novel approach for inferring hidden states from EEG data.
Understanding variance is key for accurate modeling of cortical dynamics.