Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/54386
Title: Towards a model of volume transmission in biological and artificial neural networks: a CAST approach
Authors: Suarez Araujo, Carmen Paz 
Lopez, Pablo Fernandez 
García Báez, Patricio 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Nitric-Oxide
Issue Date: 2001
Publisher: 0302-9743
Journal: Lecture Notes in Computer Science 
Conference: 8th International Workshop on Computer Aided Systems Theory 
8th International Workshop on Computer Aided Systems Theory, EUROCAST 2001 
Abstract: At present, a new type of process for signalling between cells seems to be emerging, the diffusion or volume transmission. The volume transmission is performed by means of a gas diffusion process, which is obtained with a diffusive type of signal (NO). We present in this paper a CAST approach, in order to develop a NOdi ffusion model, away from a biologically plausible morphology, that provides a formal framework for the establishing of neural signalling capacity of NOin biological and artificial neural environments. It is also presented a study which shows implications of volume transmission in the emergence of complex structures and self-organisation processes in both biological and artificial neural netwoks. Finally, we present the diffusion version of the Associative Network (AN) [6], the Diffusion Associative Network (DAN), where a more general framework of neural learning, which is based in synaptic and volume transmission, is considered.
URI: http://hdl.handle.net/10553/54386
ISBN: 978-3-540-42959-3
354042959X
ISSN: 0302-9743
Source: Moreno-Díaz R., Buchberger B., Luis Freire J. (eds) Computer Aided Systems Theory — EUROCAST 2001. EUROCAST 2001. Lecture Notes in Computer Science, vol 2178. Springer, Berlin, Heidelberg
Appears in Collections:Actas de congresos
Show full item record

SCOPUSTM   
Citations

4
checked on Aug 1, 2021

WEB OF SCIENCETM
Citations

3
checked on Aug 1, 2021

Page view(s)

34
checked on Jul 24, 2021

Google ScholarTM

Check

Altmetric


Share



Export metadata



Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.