Open AccessHOLISTIC SELF-REPROGRAMMING OF NEURAL NETWORKS: BETWEEN SELF-ORGANIZATION AND SELF-OBSERVATION
Author(s) -
Jean-Jacques Mariage
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.5.3.409
Subject(s) - cognitive science , computer science , metaphor , consciousness , artificial neural network , reprogramming , artificial intelligence , neural substrate , self organization , convergence (economics) , psychology , neuroscience , biology , cognition , philosophy , linguistics , genetics , cell , economics , economic growth
Neural networks (NNs) are inspired – at least metaphorically –from biological solutions nature selected by evolution. On one hand, learning algorithms' efficacy has been widely demonstrated experimentally, even if the mathematical proof of their convergence is not always very easy to establish (SOM). On the other hand, biological mechanisms like brain wiring or embryology remain partly understood and how life or the bases of consciousness are related to the underlying biological substrate remains a total mystery. The same goes for memory. We don’t really know how information is stored in and recovered from biological neural structures. We therein paradoxically use complex systems, the hard core of which we still don't always fully understand, both regarding the models we build, as well as their former roots in the leaving world. In this theoretical paper, we resort to a few biological encoding schemata that bring insights into neural structures' growth, plasticity and reorganization, and we suggest reconsidering the metaphor in an adaptive developmental view.