Skip to main content

Pseudopatterns and dual-network memory models: Advantages and shortcomings

  • Conference paper
Connectionist Models of Learning, Development and Evolution

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

Abstract

The dual-network memory model is designed to be a neurobiologically plausible manner of avoiding catastrophic interference. We discuss a number of advantages of this model and potential clues that the model has provided in the areas of memory consolidation, category-specific deficits, anterograde and retrograde amnesia. We discuss a surprising result about how this class of models handles episodic (“snap-shot”) memory — namely, that they seem to be able to handle both episodic and abstract memory — and discuss two other promising areas of research involving these models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ans, B., & Rousset, S. (1997). A voiding catastrophic forgetting by coupling two reverberating neural networks. Academie des Sciences, Sciences de la vie, 320, 989–997.

    Google Scholar 

  2. Ans, B., & Rousset, S. (2000). Neural Networks with a Self-Refreshing Memory: Knowledge Transfer in Sequential Learning Tasks without Catastrophic Forgetting. Connection Sciences, 12, 1,1–19

    Article  Google Scholar 

  3. Cohen, J. J., & Eichenbaum, H. (1993). Memory, amnesia, and the Hippocampal System. Cambridge: MIT Press.

    Google Scholar 

  4. Frean, M., & Robins, A. (1998). Catastrophic forgetting and “pseudorehearsal” in linear networks. In Downs T, Frean M., & Gallagher M (Eds.) Proc. of the 9th Australian Conference on Neural Networks, 173–178, Brisbane: U. of Queensland

    Google Scholar 

  5. French, R. M., & Mareschal, D. (1998). Could Category-Specific Semantic Deficits Reflect Differences in the Distributions of Features Within a Unified Semantic Memory? In Proceedings of the Twentieth Annual Cognitive Science Society Conference. NJ:LEA. 374–379.

    Google Scholar 

  6. French, R. M. (1997a). Pseudo-recurrent connectionist networks: An approach to the “sensitivity—stability” dilemma. Connection Science, 9(4),353–379.

    Article  Google Scholar 

  7. French, R. M. (1997b). Selective memory loss in aphasics: An insight from pseudo-recurrent connectionist networks. In J. Bullinaria, G. Houghton, D. Glasspool (eds.). Connectionist Representations: Proceedings of the Fourth Neural Computation and Psychology Workshop. Springer-Verlag. 183–195.

    Google Scholar 

  8. French, R. M. (1999). Catastrophic Forgeuing in Connectionist Networks. Trends in Cognitive Sciences, 3(4), 128–135.

    Article  Google Scholar 

  9. Hebb, D. O. (1949). Organization ofBehavior. New York, N. Y.: Wiley&Sons.

    Google Scholar 

  10. Holland, J. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: The University of Michigan Press.

    Google Scholar 

  11. McClelland, J., McNaughton, B., & OReilly, R. (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. 102,419–457.

    Article  Google Scholar 

  12. McCloskey, M., & Cohen, N. (1989). Catastrophic interference in connectionist networks: The sequential learning problem. The Psychology of Learning and Motivation, 24, 109–165.

    Article  Google Scholar 

  13. McNaughton, B., & Morris, R. (1987). Hippocampal synaptic enhancement and information storage within a distributed memory system. Trends in Neurosciences, 10,408–415.

    Article  Google Scholar 

  14. McNaughton, B., & Nadel, L. (1990). Hebb-Marr networks and the neurobiological representation of action in space. In M.A. Gluck, & D. Rumelhart (Eds.) Neuroscience and Connectionist Theory. Hillsdale, NJ: LEA, 1–63.

    Google Scholar 

  15. Ratcliff, R. (1990). Connectionist models of recognition memory: Constraints imposed by Iearning and forgetting functions. Psychological Review, 97, 285–308

    Article  Google Scholar 

  16. Robins, A., & McCallum, S. (1998). Pseudorehearsal and the catastrophic forgeuing solution in Hopfield type networks. Connection Science, 10, 121–135

    Article  Google Scholar 

  17. Robins, A. (1995). Catastrophic forgeuing, rehearsal, and pseudorehearsal. Connection Science, 7, 123–146.

    Article  Google Scholar 

  18. Robins, A. (1996). Consolidation in neural networks and in the sleeping brain. Connection Science, 8,259–275

    Article  Google Scholar 

  19. Squire, L. (1992). Memory and the hippocampus: A synthesis from findings with rats, monkeys, and humans. Psychological Review, 99, 195–231.

    Article  Google Scholar 

  20. Stickgold, R. (1999). Sleep: off-line memory reprocessing. Trends in Cognitive Sciences, 2(12),484–492.

    Article  Google Scholar 

  21. Sutherland, G., & McNaughton, B. (2000). Memory trace reactivation in hippocampal and neocortical neuronal ensembles. Current Opinions in Neurobiology, 10, 180–186.

    Article  Google Scholar 

  22. Traub R., & Miles R (1991). Neuronal networks of the hippocampus. Cambridge, UK: Cambridge Univ. Press.

    Book  Google Scholar 

  23. Treves A., Rolls E. (1994) Computational analysis of the role of the hippocampus in memory. Hippocampus, 4, 374–391.

    Article  Google Scholar 

  24. Vertes, Robert P. and Eastman, K. E. (2000). The Case Against Memory Consolidation in REM sleep. Behavioral and Brain Sciences, 23 (6).

    Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag London

About this paper

Cite this paper

French, R.M., Ans, B., Rousset, S. (2001). Pseudopatterns and dual-network memory models: Advantages and shortcomings. In: French, R.M., Sougné, J.P. (eds) Connectionist Models of Learning, Development and Evolution. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0281-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-0281-6_2

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-354-6

  • Online ISBN: 978-1-4471-0281-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics