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A simple growth model constructs critical avalanche networks.

L F Abbott¹, R Rohrkemper

¹Department of Physiology and Cellular Biophysics, Center for Neurobiology and Behavior, Columbia University College of Physicians and Surgeons, New York, NY 10032-2695, USA. lfa2103@columbia.edu

Progress in Brain Research

|October 11, 2007

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Neuronal avalanches exhibit critical scaling properties. A simple model of neuronal growth demonstrates that these scaling laws can emerge from the spatial interactions of neuronal processes.

Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

Neurons exhibit spontaneous activity organized into bursts called avalanches.
The observed scaling properties of neuronal avalanches suggest underlying critical dynamics in neural circuits.

Purpose of the Study:

To investigate whether a simple model of neuronal growth can reproduce the critical scaling laws observed in neuronal avalanches.
To explore the relationship between neuronal process growth and the emergence of critical dynamics.

Main Methods:

Utilized a computational model simulating neuronal process growth based on intracellular calcium concentration.
Modeled neuronal interactions as overlapping spatial regions (circles) representing neuronal processes.
Analyzed the scaling properties of simulated activity patterns.

Related Experiment Videos

Main Results:

The model successfully generated activity patterns exhibiting scaling laws similar to those observed in experimental neuronal avalanches.
Neuronal interaction strength, proportional to the overlap area of neuronal processes, was a key factor in emergent scaling.
The growth and shrinkage dynamics of neuronal processes influenced the observed scaling behavior.

Conclusions:

Simple models of neuronal growth and interaction can explain the critical scaling properties of neuronal avalanches.
Spatial dynamics and connectivity arising from neuronal process extension are fundamental to critical brain dynamics.
This model provides a parsimonious explanation for emergent criticality in neural networks.