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New ideas for brain modelling 6

  • Received: 12 May 2020 Accepted: 07 July 2020 Published: 23 July 2020
  • This paper describes implementation details for a 3-level cognitive model, described in the paper series. The whole architecture is now modular, with different levels using different types of information. The ensemble-hierarchy relationship is maintained and placed in the bottom optimising and middle aggregating levels, to store memory objects and their relations. There is a geometric progression from overlapping to contained/fuzzy pattern sets. The top-level cognitive layer has been re-designed to model the Cognitive Process Language (CPL) of an earlier paper, by refactoring it into a network structure with a light scheduler. The cortex brain region is thought to be hierarchical - clustering from simple to more complex features. The refactored network might therefore challenge conventional thinking on that brain region, by making it more horizontal and type-based. It is also argued that the function and structure in particular, of the new top level, is similar to the psychology theory of chunking. The model is still only a framework and does not have enough information for real intelligence. But a framework is now implemented over the whole design and so can give a more complete picture about the potential for results.

    Citation: Kieran Greer. New ideas for brain modelling 6[J]. AIMS Biophysics, 2020, 7(4): 308-322. doi: 10.3934/biophy.2020022

    Related Papers:

  • This paper describes implementation details for a 3-level cognitive model, described in the paper series. The whole architecture is now modular, with different levels using different types of information. The ensemble-hierarchy relationship is maintained and placed in the bottom optimising and middle aggregating levels, to store memory objects and their relations. There is a geometric progression from overlapping to contained/fuzzy pattern sets. The top-level cognitive layer has been re-designed to model the Cognitive Process Language (CPL) of an earlier paper, by refactoring it into a network structure with a light scheduler. The cortex brain region is thought to be hierarchical - clustering from simple to more complex features. The refactored network might therefore challenge conventional thinking on that brain region, by making it more horizontal and type-based. It is also argued that the function and structure in particular, of the new top level, is similar to the psychology theory of chunking. The model is still only a framework and does not have enough information for real intelligence. But a framework is now implemented over the whole design and so can give a more complete picture about the potential for results.


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    The author confirms that this is an independent piece of research, carried out without external funding or support.

    Conflict of interest



    The author declares no conflict of interest.

    [1] Greer K New ideas for brain modelling 5 (2019) .Available from https://arxiv.org/abs/1803.01690.
    [2] Greer K (2019) New ideas for brain modelling 3. Cogn Syst Res 55: 1-13.
    [3] Greer K (2018) New ideas for brain modelling 4. BRAIN. Broad Res Artif Intell Neurosci 9: 155-167.
    [4] Greer K (2016) New ideas for brain modelling. BRAIN. Broad Res Artif Intell Neurosci 6: 26-46.
    [5] Greer K (2015) New ideas for brain modelling 2. Intelligent Systems in Science and Information 2014 Springer, 23-39.
    [6] Greer KRC Thinking Networks-the Large and Small of it: Autonomic and Reasoning Processes for Information Networks (2008) .LuLu.com.
    [7] Greer K (2013) Turing: then, now and still key. Artificial Intelligence, Evolutionary Computing and Metaheuristics Berlin: Springer, 43-62.
    [8] Greer K (2019) Adding context to concept trees. Int J Intell Syst Design Comput 3: 84-100.
    [9] Greer K (2014) Concept trees: building dynamic concepts from semi-structured data using nature-inspired methods. Complex System Modelling and Control Through Intelligent Soft Computations Springer-Verlag, 221-252.
    [10] Hawkins J., Blakeslee S. (2004)  On Intelligence New York: Times Books, Available from: https://en.wikipedia.org/wiki/On_Intelligence.
    [11] Yuste R (2011) Dendritic spines and distributed circuits. Neuron 71: 772-781.
    [12] Gruber TR (1993) A translation approach to portable ontology specifications. Knowl Acquisition 5: 199-220.
    [13] Greer K (2011) Symbolic neural networks for clustering higher-level concepts. NAUN Int J Comput 5: 378-386.
    [14] Mountcastle VB (1997) The columnar organization of the neocortex. Brain: J Neurol 120: 701-722.
    [15] Sukanya P, Gayathri KS (2013) An unsupervised pattern clustering approach for identifying abnormal user behaviors in smart homes. Int J Comput Sci Netw 3: 115-122.
    [16] Mnih V, Kavukcuoglu K, Silver D, et al. (2015) Human-level control through deep reinforcement learning. Nature 518: 529-533.
    [17] Lieto A, Lebiere C, Oltramari A (2018) The knowledge level in cognitive architectures: Current limitations and possible developments. Cogn Syst Res 48: 39-55.
    [18] Eliasmith C, Stewart TC, Choo X, et al. (2012) A large-scale model of the functioning brain. Science 338: 1202-1205.
    [19] Laird JE (2012)  The Soar Cognitive Architecture MIT press.
    [20] Newell A, Simon HA (2007)  Computer Science as Empirical Inquiry: Symbols and Search ACM Turing award lectures.
    [21] High R (2012) The era of cognitive systems: An inside look at IBM Watson and how it works. Redbooks 1-16.
    [22] Hawkins J, Ahmad S (2016) Why neurons have thousands of synapses, a theory of sequence memory in neocortex. Front Neural Circuit 10: 23.
    [23] Cer DM, O'Reilly RC (2006) Neural mechanisms of binding in the hippocampus and neocortex: insights from computational models. Handbook of binding and memory: Perspectives from cognitive neuroscience Oxford University Press, 193-220.
    [24] Hinton GE (1986) Distributed representations. Parallel Distributed Processing: Explorations in the Microstructure of Cognition Cambridge: MIT Press, 77-109.
    [25] Mastrandrea R, Gabrielli A, Piras F, et al. (2017) Organization and hierarchy of the human functional brain network lead to a chain-like core. Sci Rep 7: 1-13.
    [26] Miller GA (1956) The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychol Rev 63: 81.
    [27] Johnson NF (1970) The role of chunking and organization in the process of recall. Psychol Learn Motivation 4: 171-247.
    [28] Gobet F, Retschitzki J, de Voogt A (2004)  Moves in Mind: The Psychology of Board Games Psychology Press.
    [29] Gobet F (2017) Entrenchment, Gestalt formation, and chunking, Language and the human lifespan series. Entrenchment, Memory and Automaticity: The Psychology of Linguistic Knowledge and Language Learning Berlin: Gruyter Mouton.
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