The UNT College of Arts and Sciences educates students in traditional liberal arts, performing arts, sciences, professional, and technical academic programs. In addition to its departments, the college includes academic centers, institutes, programs, and offices providing diverse courses of study.
This article discusses cooperation-induced topological complexity and the emergence of intelligence.
Physical Description
7 p.
Notes
Abstract: According to an increasing number of researchers intelligence emerges from criticality as a consequence of locality breakdown and long-range correlation, well known properties of phase transition processes. The authors study a model of interacting units, as an idealization of real cooperative systems such as the brain or a flock of birds, for the purpose of discussing the emergence of long-range correlation from the coupling of any unit with its nearest neighbors. The authors focus on the critical condition that has been recently shown to maximize information transport and the authors study the topological structure of the network of dynamically linked nodes. Although the topology of this network depends on the arbitrary choice of correlation threshold, namely the correlation intensity selected to establish a link between two nodes; the numerical calculations of this paper afford some important indications on the dynamically induced topology. The first important property is the emergence of a perception length as large as the flock size, thanks to some nodes with a large number of links, thus playing the leadership role. All the units are equivalent and leadership moves in time from one to another set of nodes, thereby insuring fault tolerance. Then the authors focus on the correlation threshold generating a scale-free topology with power index v ≈ 1 and the authors find that if this topological structure is selected to establish consensus through the linked nodes, the control parameter necessary to generate criticality is close to the critical value corresponding to the all-to-all coupling condition. The authors find that criticality in this case generates also a third state, corresponding to a total lack of consensus. However, the authors make a numerical analysis of the dynamically induced network, and the authors find that it consists of two almost independent structures, each of which is equivalent to a network in the all-to-all coupling condition. This observation confirms that cooperation makes the system evolve toward favoring consensus topological structures. The authors argue that these results are compatible with both Hebbian learning and fault tolerance.
This article is part of the following collection of related materials.
UNT Scholarly Works
Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.
Turalska, Malgorzata; Geneston, Elvis L.; West, Bruce J.; Allegrini, Paolo & Grigolini, Paolo.Cooperation-induced topological complexity: a promising road to fault tolerance and Hebbian learning,
article,
March 16, 2012;
[Lausanne, Switzerland].
(https://digital.library.unt.edu/ark:/67531/metadc132972/:
accessed April 26, 2024),
University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu;
crediting UNT College of Arts and Sciences.
