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A Tree-Like Model for Brain Growth and Structure

A Tree-Like Model for Brain Growth and Structure The Flory-Stockmayer theory for the polycondensation of branched polymers, modified for finite systems beyond the gel point, is applied to the connection (synapses) of neurons, which can be considered highly branched “monomeric” units. Initially, the process is a linear growth and tree-like branching between dendrites and axons of nonself-neurons. After the gel point and at the maximum “tree” size, the tree-like model prescribes, on average, one pair of twin synapses per neuron. About 13% of neurons, “unconnected” to the maximum tree, migrate to the surface to form cortical layers. The number of synapses in each neuron may reach 10000, indicating a tremendous amount of flexible, redundant, and neuroplastic loop-forming linkages which can be preserved or pruned by experience and learning. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Biophysics Hindawi Publishing Corporation

A Tree-Like Model for Brain Growth and Structure

Journal of Biophysics , Volume 2013 (2013) – Sep 2, 2013

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Publisher
Hindawi Publishing Corporation
Copyright
Copyright © 2013 Benjamin C. Yan and Johnson F. Yan.
ISSN
1687-8000
eISSN
1687-8019
Publisher site
See Article on Publisher Site

Abstract

The Flory-Stockmayer theory for the polycondensation of branched polymers, modified for finite systems beyond the gel point, is applied to the connection (synapses) of neurons, which can be considered highly branched “monomeric” units. Initially, the process is a linear growth and tree-like branching between dendrites and axons of nonself-neurons. After the gel point and at the maximum “tree” size, the tree-like model prescribes, on average, one pair of twin synapses per neuron. About 13% of neurons, “unconnected” to the maximum tree, migrate to the surface to form cortical layers. The number of synapses in each neuron may reach 10000, indicating a tremendous amount of flexible, redundant, and neuroplastic loop-forming linkages which can be preserved or pruned by experience and learning.

Journal

Journal of BiophysicsHindawi Publishing Corporation

Published: Sep 2, 2013

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