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Action Potential

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Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Inferring structure of cortical neuronal networks from activity data: A statistical physics approach.

Ho Fai Po¹, Akke Mats Houben^2,3, Anna-Christina Haeb^2,3

¹Department of Mathematics, Aston University, Birmingham B4 7ET, United Kingdom.

|January 10, 2025

Summary

Researchers developed a new probabilistic method to infer neuronal network structure and activity from brain signals. This approach accurately predicts neuronal function and outperforms existing techniques for understanding brain plasticity and learning.

Keywords:

biological neuronal networks inference expectation–maximization algorithms generalized maximum likelihood kinetic Ising model neuronal-type classification

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Area of Science:

Neuroscience
Computational Neuroscience
Machine Learning

Background:

Understanding the relationship between neuronal network structure and activity is crucial for deciphering brain function and evolution.
Existing methods struggle to accurately infer complex neuronal network dynamics and plasticity.

Purpose of the Study:

To develop a novel probabilistic method for inferring effective neuronal network structure from activity.
To identify neuronal types (excitatory/inhibitory) and predict spiking activity.
To provide insights into neural plasticity and learning mechanisms.

Main Methods:

Integration of Bayesian statistics, statistical physics, and machine learning.
Development of a probabilistic algorithm for network inference.
Validation using synthetic data, in silico models, and in vitro neuronal networks.

Main Results:

The method accurately infers effective neuronal network structure and predicts neuronal activity.
The algorithm successfully identifies excitatory and inhibitory neurons.
Demonstrated superior performance compared to existing network inference methods.
Validated across diverse network types (modular and homogeneous).

Conclusions:

The developed probabilistic method offers a powerful tool for understanding neuronal network dynamics.
This approach enhances insights into learning processes and neural plasticity.
Facilitates the development of advanced neuron-based computing circuits.