Hippocampal formation trains independent components via forcing input reconstruction
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.
KeywordsInternal Representation Independent Component Entorhinal Cortex Independent Component Analysis Control Architecture
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- 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.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.Comon, C.: Independent component analysis-A new concept?. Signal Processing 36 (1994) 287–314Google Scholar
- 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.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.Szepesvàri, C., Cimmer, S., L6rincz, A.: Dynamic state feedback neurocontroller for compensatory control. Neural Networks (1997) In pressGoogle Scholar
- 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.Laheld, B., Cardoso, J.F.: Adaptive source separation with uniform performance. Proc. EUSIPCO-94 2 (1994) 183–186Google Scholar
- 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.Buzsáki, Gy.: Two-stage model of memory trace formation: A role for “noisy” brain states. Neuroscience 31 (1989) 551–570Google Scholar