Hippocampal formation trains independent components via forcing input reconstruction

  • András Lőrincz
Part I: Coding and Learning in Biology
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1327)


It is assumed that higher order concept formation utilizes independent components (ICs). It is argued that ICs require dynamic input reconstruction networks (RNs) to form a reliable internal representation. Input reconstruction, however, can be slow and poor with ICs on substrates with lossy dynamics. A model of the hippocampal formation is proposed that develops the ICs on lossy RNs by means of locking inputs to the internal representation and thus forcing fast reconstruction and cancelling losses. It is assumed that upon training ICs can lock themselves, thus hippocampal lesion mostly affects anterograde memories.


Internal Representation Independent Component Entorhinal Cortex Independent Component Analysis Control Architecture 
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  1. 1.
    Kalmár, Z., Szepesvàri, C., Lőrincz, A.: Generalized dynamic concept model as a route to construct adaptive autonomous agents. Neural Network World 5 (1995) 353–360Google Scholar
  2. 2.
    Jutten, C., Herault, J.: Blind separation of sources, Part 1: An adaptive algorithm based on neuromimetic architecture. Signal Processing 24 (1991) 1–10Google Scholar
  3. 3.
    Comon, C.: Independent component analysis-A new concept?. Signal Processing 36 (1994) 287–314Google Scholar
  4. 4.
    Karhunen, J., Oja, E., Wang, L., Vigário, R., Joutsensalo, J.: A class of neural networks for independent component analysis. IEEE Trans. on Neural Networks (1997) In pressGoogle Scholar
  5. 5.
    Lőrincz, A.: Towards a unified model of cortical computation II: From control architecture to a model of consciousness. Neural Network World 7 (1997) 137–152Google Scholar
  6. 6.
    Szepesvàri, C., Cimmer, S., L6rincz, A.: Dynamic state feedback neurocontroller for compensatory control. Neural Networks (1997) In pressGoogle Scholar
  7. 7.
    Szepesvàri, C., Lőrincz, A.: Approximate inverse-dynamics based robust control using static and dynamic state feedback. Neural Adaptive Control Theory, World Sci. Singapore, 2 In pressGoogle Scholar
  8. 8.
    Laheld, B., Cardoso, J.F.: Adaptive source separation with uniform performance. Proc. EUSIPCO-94 2 (1994) 183–186Google Scholar
  9. 9.
    Bell, A.J., Sejnowski, T.J.: Edges are the independent components of natural scenes. Advances in Neural Information Processing Systems 9 (1997) 831–837Google Scholar
  10. 10.
    Buzsáki, Gy.: Two-stage model of memory trace formation: A role for “noisy” brain states. Neuroscience 31 (1989) 551–570Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • András Lőrincz
    • 1
    • 2
  1. 1.Department of Chemical PhysicsInstitute of Isotopes of the Hungarian Academy of Sciences BudapestHungary
  2. 2.Department of Adaptive SystemsAttila József University, SzegedHungary

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