How to build the cognitive network?
-
1
University of Warsaw, Interdisciplinary Centre for Mathematical and Comp, Poland
How to build the cognitive network? What is the influence of the learning network on the final quality of information processing in cognitive systems? How a node of such network has to be designed in order to effectively process incoming information that is both time dependent and spatially heterogenous? We address those questions by benchmarking different machine learning algorithms using real life examples (mostly from bioinformatics and chemoinformatics) that mimics the different pattern recognition tasks typically used in various machine learning applications. In the framework of Agent Based Modelling (ABM) the most promising cognitive network appears to be constructed from an sparse ensemble of interacting agents. The cognitive networks are able to successfully combine outcomes of individual learning agents in order to determine the final decision with the higher accuracy than any single learning method used in the construction of a network. We discuss here also the mean-field approximation of the cognitive networks in the limit of the infinite number of learning agents. The ensemble of interacting learning agents, acquires and process incoming information using various types, or different versions of machine learning and clustering algorithms. The abstract learning space, where all agents are located, is constructed here using a randomly connected, sparse model. It couples locally the certain small percentage of similar learning agents, yet also connect remotely about 10% of the other agents with random strength values. Such network simulates the higher level integration of information acquired from the independent learning trials. The final classification of incoming input data is therefore defined as the stationary state of the cognitive system using simple majority rule, yet the minority clusters that share opposite classification outcome can be observed in the system. The cognitive network is able to couple different scales of both space and time patterns by assigning them to different subsets of learning nodes. The probability of selecting proper class for a given input data, can be estimated even without the prior knowledge of its affiliation. The fuzzy logic can be easily introduced into the system, even if learning agents are build from simple binary classification machine learning algorithms by calculating the percentage of agreeing agents. The model is inspired by the large scale cognitive computing models of the brain cortex areas (D. Modha et al., Izhikevich et al.).
References
1. Dharmendra S. Modha, "A Conceptual Cortical Surface Atlas", PLoS ONE 4(6): e5693. doi:10.1371/journal.pone.0005693
2. James S. Albus, George A. Bekey, John H. Holland, Nancy G. Kanwisher, Jeffrey L. Krichmar, Mortimer Mishkin, Dharmendra S. Modha, Marcus E. Raichle, Gordon M. Shepherd, and Giulio Tononi, "A Proposal for a Decade of the Mind Initiative" Science [Letter], Vol 317, Issue 5843, 7 September 2007:1321.
3. Izhikevich E. M. and Edelman G. M. (2008) Large-Scale Model of Mammalian Thalamocortical Systems. PNAS, 105:3593-3598
Conference:
Neuroinformatics 2010 , Kobe, Japan, 30 Aug - 1 Sep, 2010.
Presentation Type:
Poster Presentation
Topic:
General neuroinformatics
Citation:
Dariusz
P
(2010). How to build the cognitive network?.
Front. Neurosci.
Conference Abstract:
Neuroinformatics 2010 .
doi: 10.3389/conf.fnins.2010.13.00043
Copyright:
The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers.
They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.
The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.
Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.
For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.
Received:
10 Jun 2010;
Published Online:
10 Jun 2010.
*
Correspondence:
Plewczynski Dariusz, University of Warsaw, Interdisciplinary Centre for Mathematical and Comp, Warsaw, Poland, D.Plewczynski@icm.edu.pl