A deterministic and logic model on small-world brain functional memory network

In order to understand the formation and evolution mechanisms of small-world characters in brain functional memory network visually and directly, we adopt the deterministic complex network modelling method to simulate the memory process and introduce the logic unit definition to decrease the complexity of the network. By the logic abstraction of memory node and edge, we setup the connection mechanism based on the memory anatomical characters and logic characters. Meanwhile, we introduce the logic memory cell definition meta-memory as the network node to setup the logic brain functional memory network in order to be more close to brain anatomical substrate and decrease the complexity of the transformation from large numbers of neurons to network node. We applied the deterministic modelling algorithm with set as data structure to make the simulation of the small-world characters memory network and got the data retrieval algorithm in accord with memory characters. The theoretical analysis and data simulation results imply that the deterministic and logic brain functional memory network has the small-world characters and it is feasible to model the brain functional memory network with deterministic modelling algorithm on the basis of memory cell by the logic definition of the meta-memory.

[1]  Lanhua Zhang,et al.  Deterministic modeling and simulation in brain functional complex nework , 2011, Proceedings of 2011 International Conference on Computer Science and Network Technology.

[2]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[3]  N Parga,et al.  Associative memory properties of multiple cortical modules. , 1999, Network.

[4]  Yong-An Zhang,et al.  Simulation of technological innovation network diffusion in focal firm cored industrial clusters , 2012, Int. J. Model. Identif. Control..

[5]  Zhang Lanhua,et al.  A Deterministic Small-world Model and Algorithm of Memory Network , 2012 .

[6]  Bo Cartling,et al.  Dynamics control of semantic processes in a hierarchical associative memory , 1996, Biological Cybernetics.

[7]  Yugeng Xi,et al.  Topology simplification strategy for connected multi-agent systems , 2011, Int. J. Model. Identif. Control..

[8]  Steve Williams,et al.  Analysis of connectivity in the resting state of the default mode of brain function: a major role for the cerebellum? , 2010, Int. J. Model. Identif. Control..

[9]  Ali A. Minai,et al.  Efficient associative memory using small-world architecture , 2001, Neurocomputing.

[10]  Risto Miikkulainen,et al.  Modeling large cortical networks with growing self-organizing maps , 2002, Neurocomputing.

[11]  Randa Herzallah,et al.  Enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty , 2011, Int. J. Model. Identif. Control..

[12]  Chen Cao,et al.  A novel deterministic hybrid complex network model created by inner–outer iteration , 2012 .

[13]  Alessandro Treves,et al.  Autoassociative memory retrieval and spontaneous activity bumps in small-world networks of integrate-and-fire neurons , 2005, Journal of Physiology-Paris.

[14]  M. Hasselmo,et al.  Free recall and recognition in a network model of the hippocampus: simulating effects of scopolamine on human memory function , 1997, Behavioural Brain Research.