Advertisement

Subsymbolic Versus Symbolic Data Flow in the Meaningful-Based Cognitive Architecture

  • Howard SchneiderEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 948)

Abstract

The biologically inspired Meaningful-Based Cognitive Architecture (MBCA) integrates the subsymbolic sensory processing abilities found in neural networks with many of the symbolic logical abilities found in human cognition. The basic unit of the MBCA is a reconfigurable Hopfield-like Network unit (HLN). Some of the HLNs are configured for hierarchical sensory processing, and these groups subsymbolically process the sensory inputs. Other HLNs are organized as causal memory (including holding of multiple world views) and as logic/working memory units, and can symbolically process input vectors as well as vectors from other parts of the MBCA, in accordance with intuitive physics, intuitive psychology, intuitive scheduling and intuitive world views stored in the instinctual core goals module, and similar learned views stored in causal memory. The separation of data flow into the subsymbolic and symbolic streams, and the subsequent re-integration in the resultant actions, are explored. The integration of logical processing in the MBCA predisposes it to a psychotic-like behavior, and predicts that in Homo sapiens psychosis should occur for a wide variety of mechanisms.

Keywords

Cognitive architecture Cortical minicolumns Psychosis 

Notes

Acknowledgments

This article builds upon work originally presented at BICA 2018 (reference 1).

References

  1. 1.
    Schneider H (2018) Meaningful-based cognitive architecture. Procedia Comput Sci 145:471–480 BICA 2018 ed Samsonovich A VCrossRefGoogle Scholar
  2. 2.
    Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, CambridgezbMATHGoogle Scholar
  3. 3.
    Mnih V, Kavukcuoglu K, Silver D et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533CrossRefGoogle Scholar
  4. 4.
    Ullman S (2019) Using neuroscience to develop artificial intelligence. Science 363(6428):692–693CrossRefGoogle Scholar
  5. 5.
    Waismeyer A, Meltzoff AN, Gopnik A (2015) Causal learning from probabilistic events in 24-month-olds: an action measure. Dev Sci 18(1):175–182CrossRefGoogle Scholar
  6. 6.
    Laird JE, Lebiere C, Rosenbloom PS (2017) A standard model of the mind: toward a common computational framework across artificial intelligence, cognitive science. Neurosci Robot AI Mag 38(4):13–26Google Scholar
  7. 7.
    Anderson JR, Bothell D, Byrne MD et al (2004) An integrated theory of mind. Psychol Rev 111(4):1036–1060CrossRefGoogle Scholar
  8. 8.
    Kilicay-Ergin N, Jablokow K (2012) Problem-solving variability in cognitive architectures. IEEE Trans Syst Man Cybern Part C Appl Rev 42:1231–1242CrossRefGoogle Scholar
  9. 9.
    Bach J (2008) Seven principles of synthetic intelligence. In: Proceedings of 1st Conference on AGI, Memphis, pp 63–74Google Scholar
  10. 10.
    Rosenbloom P, Demski A, Ustun V (2016) The sigma cognitive architecture and system: towards functionally elegant grand unification. J Artif Gen Intell 7(1):1–103CrossRefGoogle Scholar
  11. 11.
    Graves A, Wayne G, Reynolds M et al (2016) Hybrid computing using a neural network with dynamic external memory. Nature 538:471–476CrossRefGoogle Scholar
  12. 12.
    Collier M, Beel J (2018) Implementing neural Turing machines. In: 27th International Conference on Artificial Neural Networks (ICANN), Rhodes, Greece, 4–7 OctoberCrossRefGoogle Scholar
  13. 13.
    Li S, Xu H, Lu Z (2018) Generalize symbolic knowledge with neural rule engine. arXiv:1808.10326
  14. 14.
    Mountcastle VB (1997) Columnar organization of the neocortex. Brain 20:701–722CrossRefGoogle Scholar
  15. 15.
    Buxhoeveden DP, Casanova MF (2002) The minicolumn hypothesis in neuroscience. Brain 125(5):935–951CrossRefGoogle Scholar
  16. 16.
    Schwalger T, Deger M, Gerstner W (2017) Towards a theory of cortical columns. PLoS Comput. Biol 13(4): e1005507.  https://doi.org/10.1371/journal.pcbi.1005507 CrossRefGoogle Scholar
  17. 17.
    Eliasmith C, Trujillo O (2014) The use and abuse of large-scale brain models. Curr Opin Neurobiol 25:1–6CrossRefGoogle Scholar
  18. 18.
    Lázaro-Gredilla M, Liu Y, Phoenix DS, George D (2017) Hierarchical compositional feature learning. arXiv preprint. arXiv:1611.02252v2
  19. 19.
    Hawkins J, Blakeslee S (2004) On intelligence. Times books, New YorkGoogle Scholar
  20. 20.
    Kurzweil R (2012) How to create a mind. Viking Press, New YorkGoogle Scholar
  21. 21.
    Sabour S, Frosst N Hinton GE (2017) Dynamic routing between capsules. In: NIPS 2017, Long Beach, CA, USAGoogle Scholar
  22. 22.
    Sipser M (2012) Introduction to the theory of computation, 3rd edn. Course Technology, BostonzbMATHGoogle Scholar
  23. 23.
    Schneider H (2019, in press) Emergence of belief systems and the future of artificial intelligence. In: Samsonovich AV (ed) Biologically inspired cognitive architectures 2019, BICA 2019. Advances in intelligent systems and computing. SpringerGoogle Scholar
  24. 24.
    Siddiqui M, Wedemann RS, Jensen HJ (2018) Avalanches and generalized memory associativity in a network model for conscious and unconscious mental functioning. Physica A 490:127–138MathSciNetCrossRefGoogle Scholar
  25. 25.
    Cohen JD, Servan-Schreiber D (1992) Context, cortex and dopamine: a connectionist approach to behavior and biology in schizophrenia. Psychol Rev 99(1):45–77CrossRefGoogle Scholar
  26. 26.
    Sabaroedin K, Tiego J, Parkers L et al (2019) Functional connectivity of corticostriatal circuitry and psychosis-like experiences in the general community. Biol Psychiatry pii: S0006–3223(19)30119-2Google Scholar
  27. 27.
    van Os J, Hanssen M, Bijil RV et al (2001) Prevalence of psychotic disorder and community level psychotic symptoms: an urban-rural comparison. Arch Gen Psychiatry 58(7):663–668CrossRefGoogle Scholar
  28. 28.
    Jones CA, Watson DJG, Fone KCF (2011) Animal models of schizophrenia. Br J Pharmacology 164:1162–1194CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Sheppard Clinic NorthTorontoCanada

Personalised recommendations