English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This study deals with the evolution of the so called 'intelligent' networks(insect society without leader, cells of an organism, brain,...) during theirlearning period. First we summarize briefly the Version 2 (published inFrench), whose the main characteristics are: 1) A network connected to itsenvironment is considered as immersed into an information field created by thisenvironment which so dictates to it the learning constraints. 2) The usedformalism draws one's inspiration from the one of the Quantum field theory(Principle of stationary action, gauge fields, invariance by symmetrytransformations,...). 3) We obtain Lagrange equations whose solutions describethe network evolution during the whole learning period. 4) Then, whileproceeding with the same formalism inspiration, we suggest other study wayscapable of evolving the knowledge in the considered scope. In a second part,after a reminder of the points to be improved, we exhibit the Version 5 whichbrings, we think, relevant improvements. Indeed: 5) We consider the weightedaverages of the variables; this introduces probabilities. 6) We define twoobservables (L average of information flux and A activity of the network) whichcould be measured and so be compared with experimental results. 7) We find thatL , weighted average of information flows, is an invariant. 8) Finally, wepropose two expressions for the conactance, from which we deduce thecorresponding Lagrange equations which have to be solved to know the evolutionof the considered weighted averages. But, at the present stage, we think thatwe can progress only by carrying out experiments (see projects like Human brainproject) and discovering invariants, symmetries which would allow us, like inPhysics, to classify networks and above all to understand better theconnections between them. Indeed, and that is what we propose among the futureresearch ways, the underlying problem is to understand how, after theirlearning period, several networks can connect together to produce, in the braincase for instance, what we call mental states.

New elements for a network (including brain) general theory during learning period