Advertisement

Contextual Learning in the Neurosolver

  • Andrzej Bieszczad
  • Kasia Bieszczad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)

Abstract

In this paper, we introduce an enhancement to the Neurosolver, a neuromorphic planner and a problem solving system. The enhanced architecture enables contextual learning. The Neurosolver was designed and tested on several problem solving and planning tasks such as re-arranging blocks and controlling a software-simulated artificial rat running in a maze. In these tasks, the Neurosolver learned temporal patterns independent of the context. However in the real world no skill is acquired in vacuum; Contextual cues are a part of every situation, and the brain can incorporate such stimuli as evidenced through experiments with live rats. Rats use cues from the environment to navigate inside mazes. The enhanced architecture of the Neurosolver accommodates similar learning.

Keywords

Target Node Contextual Learn Place Cell General Problem Solver Context Cell 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bieszczad, A., Pagurek, B.: Neurosolver: Neuromorphic General Problem Solver, Information Sciences. An International Journal 105, 239–277 (1998)MathSciNetGoogle Scholar
  2. 2.
    Newell, A., Simon, H.A.: GPS: A program that simulates human thought. In: Feigenbaum, E.A., Feldman, J. (eds.) Computer and Thought. McGrawHill, New York (1963)Google Scholar
  3. 3.
    Burnod, Y.: An Adaptive Neural Network: The Cerebral Cortex. Masson, Paris (1988)Google Scholar
  4. 4.
    Laird, J.E., Newell, A., Rosenbloom, P.S.: SOAR: An architecture for General Intelligence. Artificial Intelligence 33, 1–64 (1987)CrossRefGoogle Scholar
  5. 5.
    Nillson, N.J.: Principles of Artificial Intelligence. Tioga Publishing Company, Palo Alto (1980)Google Scholar
  6. 6.
    Deutsch, M.: The Effect Of Motivational Orientation Upon Trust And Suspicion. Human Relations 13, 123–139 (1960)CrossRefGoogle Scholar
  7. 7.
  8. 8.
    Hodges, H.: Maze Procedures: The Radial-Arm And Water Maze Compared, Cognitive Brain Research 3, pp. 167–181. Elsevier, North-Holland (1996)Google Scholar
  9. 9.
    Fenton, A.A., Muller, R.U.: Place Cell Discharge Is Extremely Variable During Individual Passes of The Rat Through The Firing Field. Proc. Natl. Acad. Sci. USA 95, 3182–3187 (1998)CrossRefGoogle Scholar
  10. 10.
    Poucet, B., Save, E.: Attractors in Memory. Science 308, 799–800 (2005)CrossRefGoogle Scholar
  11. 11.
    Fyhn, M., Molden, S., Witter, M.P.: Spatial Representation in the Entorhinal Cortex Marianne. Science 305, 1258–1264 (2004)CrossRefGoogle Scholar
  12. 12.
    O’Keefe, J., Dostrovsky, J.: The Hippocampus as a Spatial Map. Preliminary Evidence from Unit Activity in the Freely-Moving Rat. Brain Research 34, 171–175 (1971)CrossRefGoogle Scholar
  13. 13.
    Pavlov, I.P.: Conditioned Reflexes. Routledge and Kegan Paul, London (1927)Google Scholar
  14. 14.
    Bitterman, M.E., Lolordo, V.M., Overmier, J.B., Rashotte, M.E.: Animal Learning: Survey And Analysis. Plenum Press, New York (1979)Google Scholar
  15. 15.
    Kemp, C.C.: Think Like a Rat. Paper for MIT EECS Area Exam (2001), http://people.csail.mit.edu/cckemp/cckemp_place_cells_area_exam_2001.pdf
  16. 16.
    Hartley, T., Burgess, N.: Models Of Spatial Cognition. Encyclopaedia of Cognitive Science. MacMillan, Basingstoke (2002)Google Scholar
  17. 17.
    Burgess, N., O’Keefe, J.: Spatial Models of the Hippocampus. In: Arbib, M.A. (ed.) The Handbook of Brain Theory and Neural Networks, 2nd edn. MIT press, Cambridge (2002)Google Scholar
  18. 18.
    Chavarriaga, R., Strösslin, T., Sheynikhovich, D., Gerstner, W.: Competition Between Cue Response And Place Response. A Model Of Rat Navigation Behavior, Connection Science 17(1-2), 167–183 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Andrzej Bieszczad
    • 1
  • Kasia Bieszczad
    • 2
  1. 1.Computer ScienceCalifornia State University Channel IslandsCamarilloUSA
  2. 2.Center for the Neurobiology of Learning and Memory, U.C. Irvine 320 Qureshey Research LaboratoryUniversity of CaliforniaIrvineUSA

Personalised recommendations