A Heteroassociative Learning Model Robust to Interference

Randa Kassab, Frédéric Alexandre

Abstract

Neuronal models of associative memories are recurrent networks able to learn quickly patterns as stable states of the network. Their main acknowledged weakness is related to catastrophic interference when too many or too close examples are stored. Based on biological data we have recently proposed a model resistant to some kinds of interferences related to heteroassociative learning. In this paper we report numerical experiments that highlight this robustness and demonstrate very good performances of memorization. We also discuss convergence of interests for such an adaptive mechanism for biological modeling and information processing in the domain of machine learning.

References

  1. Buhusi, C. V. and Schmajuk, N. A. (1996). Attention, configuration, and hippocampal function. Hippocampus, 6(6):621-642.
  2. Carrere, M. and Alexandre, F. (2015). A pavlovian model of the amygdala and its influence within the medial temporal lobe. Frontiers in Systems Neuroscience, 9(41).
  3. Graham, B. and Willshaw, D. (1997). Capacity and information efficiency of the associative net. Network: Computation in Neural Systems, 8(1):35-54.
  4. Hasselmo, M. E., Wyble, B. P., and Wallenstein, G. V. (1996). Encoding and retrieval of episodic memories: Role of cholinergic and gabaergic modulation in the hippocampus. Hippocampus, 6(6):693-708.
  5. Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. In Proceedings of the National Academy of Sciences, USA, pages 2554-2558.
  6. Kassab, R. and Alexandre, F. (2015). Integration of exteroceptive and interoceptive information within the hippocampus: a computational study. Frontiers in Systems Neuroscience, 9(87).
  7. Knoblauch, A., Palm, G., and Sommer, F. T. (2010). Memory capacities for synaptic and structural plasticity. Neural Computation, 22(2):289-341.
  8. Levy-Gigi, E., Kelemen, O., Gluck, M. A., and Kéri, S. (2011). Impaired context reversal learning, but not cue reversal learning, in patients with amnestic mild cognitive impairment. Neuropsychologia, 49(12):3320-6.
  9. McClelland, J. L., McNaughton, B. L., and O'Reilly, R. C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychological review, 102(3):419-457.
  10. McNaughton, B. and Nadel, L. (1990). Hebb-marr networks and the neurobiological representation of action in space. In Neuroscience and Connectionist Theory, pages 1-63. Hillsdale, NJ: L. Erlbaum.
  11. Meeter, M., Murre, J. M., and Talamini, L. M. (2004). Mode shifting between storage and recall based on novelty detection in oscillating hippocampal circuits. Hippocampus, 14(6):722-41.
  12. O'Reilly, R. C., Bhattacharyya, R., Howard, M. D., and Ketz, N. (2011). Complementary Learning Systems. Cognitive Science.
  13. Samura, T., Hattori, M., and Ishizaki, S. (2008). Sequence disambiguation and pattern completion by cooperation between autoassociative and heteroassociative memories of functionally divided hippocampal CA3. Neurocomputing, 71(16-18):3176-183.
  14. Tulving, E. (1972). Episodic and semantic memory. Organization of Memory. Academic Press.
  15. Willshaw, D. J., Buneman, O. P., and Longuet-Higgins, H. C. (1969). Non-holographic associative memory. Nature, 222(5197):960-962.
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Paper Citation


in Harvard Style

Kassab R. and Alexandre F. (2015). A Heteroassociative Learning Model Robust to Interference . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015) ISBN 978-989-758-157-1, pages 49-57. DOI: 10.5220/0005606800490057


in Bibtex Style

@conference{ncta15,
author={Randa Kassab and Frédéric Alexandre},
title={A Heteroassociative Learning Model Robust to Interference},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015)},
year={2015},
pages={49-57},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005606800490057},
isbn={978-989-758-157-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015)
TI - A Heteroassociative Learning Model Robust to Interference
SN - 978-989-758-157-1
AU - Kassab R.
AU - Alexandre F.
PY - 2015
SP - 49
EP - 57
DO - 10.5220/0005606800490057