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A Modular Network Architecture Resolving Memory Interference Through Inhibition

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Computational Intelligence (IJCCI 2015)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 669))

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Abstract

In real learning paradigms like pavlovian conditioning, several modes of learning are associated, including generalization from cues and integration of specific cases in their context. Associative memories have been shown to be interesting neuronal models to learn quickly specific cases but they are hardly used in realistic applications because of their limited storage capacities resulting in interference when too many examples are considered. Inspired by biological considerations, we propose a modular model of associative memory including mechanisms to manipulate properly multimodal inputs and to detect and manage interference. This paper reports experiments that demonstrate the good behavior of the model in a wide series of simulations and discusses its impact both in machine learning and in biological modeling.

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References

  1. Hertz, J., Krogh, A., Palmer, R.: Introduction to the Theory of Neural Computation. Addison Wesley, Boston (1991)

    Google Scholar 

  2. Maren, S.: Building and burying fear memories in the brain. Neuroscientist 11, 89–99 (2005)

    Article  Google Scholar 

  3. McClelland, J.L., McNaughton, B.L., O’Reilly, R.C.: Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychol. Rev. 102, 419–457 (1995)

    Article  Google Scholar 

  4. Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. In: Proceedings of the National Academy of Sciences, USA, pp. 2554–2558(1982)

    Google Scholar 

  5. Willshaw, D.J., Buneman, O.P., Longuet-Higgins, H.C.: Non-holographic associative memory. Nature 222, 960–962 (1969)

    Article  Google Scholar 

  6. Graham, B., Willshaw, D.: Capacity and information efficiency of the associative net. Netw. Comput. Neural Syst. 8, 35–54 (1997)

    Article  MATH  Google Scholar 

  7. Knoblauch, A., Palm, G., Sommer, F.T.: Memory capacities for synaptic and structural plasticity. Neural Comput. 22, 289–341 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  8. McNaughton, B., Nadel, L.: Hebb-marr networks and the neurobiological representation of action in space. In: Neuroscience and Connectionist Theory, pp. 1–63. L. Erlbaum, Hillsdale (1990)

    Google Scholar 

  9. Kassab, R., Alexandre, F.: Integration of exteroceptive and interoceptive information within the hippocampus: a computational study. Front. Syst. Neurosci. 9 (2015)

    Google Scholar 

  10. Andersen, P.: The Hippocampus Book. Oxford Neuroscience Series. Oxford University Press, Oxford (2007)

    Google Scholar 

  11. Samura, T., Hattori, M., Ishizaki, S.: Sequence disambiguation and pattern completion by cooperation between autoassociative and heteroassociative memories of functionally divided hippocampal CA3. Neurocomputing 71, 3176–183 (2008)

    Article  Google Scholar 

  12. Tulving, E.: Episodic and semantic memory. In: Organization of Memory. Academic Press, New York (1972)

    Google Scholar 

  13. Carrere, M., Alexandre, F.: A pavlovian model of the amygdala and its influence within the medial temporal lobe. Front. Syst. Neurosci. 9 (2015)

    Google Scholar 

  14. Hasselmo, M.E., Wyble, B.P., Wallenstein, G.V.: Encoding and retrieval of episodic memories: role of cholinergic and gabaergic modulation in the hippocampus. Hippocampus 6, 693–708 (1996)

    Article  Google Scholar 

  15. Meeter, M., Murre, J.M., Talamini, L.M.: Mode shifting between storage and recall based on novelty detection in oscillating hippocampal circuits. Hippocampus 14, 722–41 (2004)

    Article  Google Scholar 

  16. LevyGigi, E., Kelemen, O., Gluck, M.A., Kéri, S.: Impaired context reversal learning, but not cue reversal learning, in patients with amnestic mild cognitive impairment. Neuropsychologia 49, 3320–6 (2011)

    Article  Google Scholar 

  17. Buhusi, C.V., Schmajuk, N.A.: Attention, configuration, and hippocampal function. Hippocampus 6, 621–642 (1996)

    Article  Google Scholar 

  18. Moustafa, A.A., Gilbertson, M.W., Orr, S.P., Herzallah, M.M., Servatius, R.J., Myers, C.E.: A model of amygdala-hippocampal-prefrontal interaction in fear conditioning and extinction in animals. Brain Cogn. 81, 29–43 (2013)

    Article  Google Scholar 

  19. O’Reilly, R.C., Bhattacharyya, R., Howard, M.D., Ketz, N.: Complementary Learning Systems. Cognit. Sci. 38, 1229–1248 (2011)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Inria Bordeaux Sud-Ouest, 200 Avenue de la Vieille Tour, 33405, Talence, France

    Randa Kassab & Frédéric Alexandre

  2. LaBRI, Université de Bordeaux, Bordeaux INP, CNRS, UMR 5800, Talence, France

    Randa Kassab & Frédéric Alexandre

  3. Institut des Maladies Neurodégénératives, Université de Bordeaux, CNRS, UMR 5293, Bordeaux, France

    Randa Kassab & Frédéric Alexandre

Authors
  1. Randa Kassab
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  2. Frédéric Alexandre
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Corresponding author

Correspondence to Frédéric Alexandre .

Editor information

Editors and Affiliations

  1. Computer Architecture and Computer Technology, Universidad de Granada, Granada, Spain

    Juan Julián Merelo

  2. aSEEB-ISR-IST, Technical University of Lisbon (IST), Lisbon, Portugal

    Agostinho Rosa

  3. Facultad de Informática, University of Murcia, Murcia, Spain

    José M. Cadenas

  4. Departamento de Engenharia Informatica, University of Coimbra, Coimbra, Portugal

    António Dourado Correia

  5. Images, Signals and Intelligence Systems Laboratory, University PARIS-EST Creteil (UPEC), Créteil, France

    Kurosh Madani

  6. Campus de Gambelas, Universidade do Algarve, Faro, Portugal

    António Ruano

  7. Escola Superior de Tecnologia de Setúbal, Polytechnic Institute of Setúbal/INSTICC, Setúbal, Portugal

    Joaquim Filipe

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Kassab, R., Alexandre, F. (2017). A Modular Network Architecture Resolving Memory Interference Through Inhibition. In: Merelo, J.J., et al. Computational Intelligence. IJCCI 2015. Studies in Computational Intelligence, vol 669. Springer, Cham. https://doi.org/10.1007/978-3-319-48506-5_21

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  • DOI: https://doi.org/10.1007/978-3-319-48506-5_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48504-1

  • Online ISBN: 978-3-319-48506-5

  • eBook Packages: EngineeringEngineering (R0)

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