Header

UZH-Logo

Maintenance Infos

An instruction language for self-construction in the context of neural networks


Zubler, F; Hauri, A; Pfister, S; Whatley, A M; Cook, M; Douglas, R J (2011). An instruction language for self-construction in the context of neural networks. Frontiers in Computational Neuroscience:5:57.

Abstract

Biological systems are based on an entirely different concept of construction than 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.

Abstract

Biological systems are based on an entirely different concept of construction than 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.

Statistics

Citations

4 citations in Web of Science®
5 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

78 downloads since deposited on 05 Mar 2012
8 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Uncontrolled Keywords:Self-construction;Simulation;Neural growth;Development;Cortex;Self-organization
Language:English
Date:1 December 2011
Deposited On:05 Mar 2012 09:48
Last Modified:07 Dec 2017 13:15
ISSN:1662-5188
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.3389/fncom.2011.00057
PubMed ID:22163218

Download

Download PDF  'An instruction language for self-construction in the context of neural networks'.
Preview
Content: Published Version
Filetype: PDF
Size: 736kB
View at publisher
Licence: Creative Commons: Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)