Title
An Instruction Language for Self-Construction in the Context of Neural Networks.
Abstract
Biological systems are based on an entirely different concept of construction that human artifacts. They construct themselves by a process of self-organization that is a systematic spatio-temporal generation of, and interaction between, various specialized cell types. We propose a framework for designing gene-like codes for guiding the self-construction of neural networks. The description of neural development is formalized by defining a set of primitive actions taken locally by neural precursors during corticogenesis. These primitives can be combined into networks of instructions similar to biochemical pathways, capable of reproducing complex developmental sequences in a biologically plausible way. Moreover, the conditional activation and deactivation of these instruction networks, can also be controlled by these primitives, allowing for the design of a "genetic code" containing both coding and regulating elements. We demonstrate in a simulation of physical cell development how this code can be incorporated into a single progenitor, which then by replication and differentiation, reproduces important aspects of corticogenesis.
Year
DOI
Venue
2011
10.3389/fncom.2011.00057
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
Keywords
Field
DocType
self-construction,simulation,neural growth,development,cortex,self-organization
Corticogenesis,Neuroscience,Computer science,Self-organization,Neural Growth,Coding (social sciences),Artificial intelligence,Artificial neural network,Neural development,Machine learning
Journal
Volume
ISSN
Citations 
5
1662-5188
3
PageRank 
References 
Authors
0.40
16
6
Name
Order
Citations
PageRank
Frederic Zubler1424.12
Andreas Hauri230.40
Sabina Pfister3131.78
Adrian M. Whatley414211.84
Matthew Cook514211.78
Rodney J. Douglas6593242.90