Link intelligence establishing neurocognitive knowledge-processing capabilities in a knowledge network

Abstract The objective of this paper is to demonstrate link intelligence that characterizes neurocognitive knowledge processing capabilities through dynamic knowledge development, learning and stability within a given knowledge network. The existing knowledge networks connect two or more data nodes with edges that have no intelligence embedded into them and provide for static information connectivity and retrieval. This paper focuses on the link intelligence that contributes towards the development of neurocognitive knowledge network model (NCKM) with autonomous processing nodes. Links in NCKM exhibit neurocognitive knowledge processing characteristics by virtue of its four properties viz. efficient knowledge assimilation, self-directivity, self-organization, and equilibrium. The simulation results comprise searching different concepts from NCKM to retrieve knowledge threads for the searched concept. The results exhibit the significance of link-weight gradation in self-directivity and self-organization of links for intelligent knowledge retrieval. The results also demonstrate the significance of equilibrium process to maintain stability in the knowledge network, by limiting the link-weight values within the saturation limits. Additionally, the results depict the criticality of coupling retrieval of random concepts with equilibrium process in providing knowledge build-up and learning within NCKM. NCKM with its intelligent links and processing nodes finds its applicability in many cognitive and intelligent systems.

[1]  Stuart D. Greenhill,et al.  Hebbian and Homeostatic Plasticity Mechanisms in Regular Spiking and Intrinsic Bursting Cells of Cortical Layer 5 , 2015, Neuron.

[2]  T. Bliss,et al.  Plasticity in the human central nervous system. , 2006, Brain : a journal of neurology.

[3]  Hiroshi Nishiyama.  Learning-induced structural plasticity in the cerebellum. , 2014, International review of neurobiology.

[4]  R. Peters,et al.  Ageing and the brain , 2006, Postgraduate Medical Journal.

[5]  S. Nelson,et al.  Homeostatic plasticity in the developing nervous system , 2004, Nature Reviews Neuroscience.

[6]  G. Turrigiano.  The Self-Tuning Neuron: Synaptic Scaling of Excitatory Synapses , 2008, Cell.

[7]  H. Johansen-Berg,et al.  Relevance of Structural Brain Connectivity to Learning and Recovery from Stroke , 2010, Front. Syst. Neurosci..

[8]  G. Turrigiano.  Homeostatic synaptic plasticity: local and global mechanisms for stabilizing neuronal function. , 2012, Cold Spring Harbor perspectives in biology.

[9]  Y. Munakata,et al.  Hebbian learning and development. , 2004, Developmental science.

[10]  T. R. Gopalakrishnan Nair,et al.  Knowledge network model with neurocognitive processing capabilities , 2016, Cognitive Systems Research.

[11]  M. Minsky.  The Society of Mind , 1986 .

[12]  H. Markram.  The Blue Brain Project , 2006, Nature Reviews Neuroscience.

[13]  Stefan Wermter,et al.  An Overview of Hybrid Neural Systems , 1998, Hybrid Neural Systems.

[14]  J. Fritschy,et al.  Epilepsy, E/I Balance and GABAA Receptor Plasticity , 2008, Frontiers in molecular neuroscience.

[15]  L. Abbott,et al.  Cascade Models of Synaptically Stored Memories , 2005, Neuron.

[16]  John F. Sowa,et al.  Mapping the Landscape of Human-Level Artificial General Intelligence , 2012, AI Mag..

[17]  James O McNamara,et al.  Molecular Signaling Mechanisms Underlying Epileptogenesis , 2006, Science's STKE.

[18]  S. Grossberg.  The Link between Brain Learning, Attention, and Consciousness , 1999, Consciousness and Cognition.

[19]  Alexei V. Samsonovich,et al.  On a roadmap for the BICA Challenge , 2012, BICA 2012.

[20]  Alexei V. Samsonovich,et al.  Toward a BICA-Model-Based Study of Cognition Using Brain Imaging Techniques , 2015, BICA.

[21]  P. Farrand,et al.  The efficacy of the `mind map' study technique , 2002, Medical education.

[22]  J. Hopfield,et al.  Computing with neural circuits: a model. , 1986, Science.

[23]  L. Gottfredson.  Mainstream science on intelligence: An editorial with 52 signatories, history, and bibliography , 1997 .

[24]  R. Haier,et al.  Human intelligence and brain networks , 2010, Dialogues in clinical neuroscience.

[25]  Barry J. Wadsworth.  Piaget's Theory of Cognitive and Affective Development: Foundations of Constructivism , 2003 .

[26]  John F. Sowa,et al.  Conceptual Graphs for a Data Base Interface , 1976, IBM J. Res. Dev..

[27]  S. Herculano‐Houzel.  The Human Brain in Numbers: A Linearly Scaled-up Primate Brain , 2009, Front. Hum. Neurosci..

[28]  T. R. Gopalakrishnan Nair,et al.  Creating intelligent linking for information threading in knowledge networks , 2011, 2011 Annual IEEE India Conference.

[29]  T. R. Gopalakrishnan Nair,et al.  Informledge System - A Modified Knowledge Network with Autonomous Nodes using Multi-lateral Links , 2010, KEOD.

[30]  Marvin Minsky,et al.  Steps toward Artificial Intelligence , 1995, Proceedings of the IRE.

[31]  John R. Anderson.  ACT: A simple theory of complex cognition. , 1996 .

[32]  Teuvo Kohonen,et al.  Essentials of the self-organizing map , 2013, Neural Networks.

[33]  John A. Barnden,et al.  Semantic Networks , 1998, Encyclopedia of Social Network Analysis and Mining.

[34]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[35]  K. Miller,et al.  Synaptic Economics: Competition and Cooperation in Synaptic Plasticity , 1996, Neuron.

[36]  T. R. Gopalakrishnan Nair,et al.  Correlating and Cross-linking Knowledge Threads in Informledge System for Creating New Knowledge , 2014, KEOD.

[37]  Lokendra Shastri.  A Connectionist Approach to Knowledge Representation and Limited Inference , 1988 .

[38]  T. R. Gopalakrishnan Nair,et al.  Knowledge Embedding and Retrieval Strategies in an Informledge System , 2011, ArXiv.

[39]  B. Kosko.  Fuzzy Thinking: The New Science of Fuzzy Logic , 1993 .

[40]  E. Tolman.  Cognitive maps in rats and men. , 1948, Psychological review.

[41]  Lokendra Shastri,et al.  Connectionist mechanisms for cognitive control , 2005, Neurocomputing.

[42]  F. Attneave,et al.  The Organization of Behavior: A Neuropsychological Theory , 1949 .

[43]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[44]  K. Miller,et al.  Modeling the Dynamic Interaction of Hebbian and Homeostatic Plasticity , 2014, Neuron.

[45]  James A. Hendler,et al.  The Semantic Web: Webizing Knowledge Representation , 2008, Handbook of Knowledge Representation.

[46]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .