Skip to main content
Log in

Cell assembly dynamics in detailed and abstract attractor models of cortical associative memory

  • Published:
Theory in Biosciences Aims and scope Submit manuscript

Summary

During the last few decades we have seen a convergence among ideas and hypotheses regarding functional principles underlying human memory. Hebb’s now more than fifty years old conjecture concerning synaptic plasticity and cell assemblies, formalized mathematically as attractor neural networks, has remained among the most viable and productive theoretical frameworks. It suggests plausible explanations for Gestalt aspects of active memory like perceptual completion, reconstruction and rivalry.

We review the biological plausibility of these theories and discuss some critical issues concerning their associative memory functionality in the light of simulation studies of models with palimpsest memory properties. The focus is on memory properties and dynamics of networks modularized in terms of cortical minicolumns and hypercolumns. Biophysical compartmental models demonstrate attractor dynamics that support cell assembly operations with fast convergence and low firing rates. Using a scaling model we obtain reasonable relative connection densities and amplitudes. An abstract attractor network model reproduces systems level psychological phenomena seen in human memory experiments as the Sternberg and von Restorff effects.

We conclude that there is today considerable substance in Hebb’s theory of cell assemblies and its attractor network formulations, and that they have contributed to increasing our understanding of cortical associative memory function.

The criticism raised with regard to biological and psychological plausibility as well as low storage capacity, slow retrieval etc has largely been disproved. Rather, this paradigm has gained further support from new experimental data as well as computational modeling.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Amari, S. I. (1989) Characteristics of sparsely encoded associative memory. Neural Networks 5: 451–457.

    Article  Google Scholar 

  • Amit, D. J. (1995) The Hebbian paradigm reintegrated: Local reverberations as internal representations. Behavioral and Brain Sciences 18: 617–657.

    Article  Google Scholar 

  • Amit, D. J. and M. V. Tsodyks (1991) Quantitative study of attractor neural network retrieving at low spike rates: I Substrate — spikes, rates and neuronal gain. Network: Computation in Neural Systems 2: 259–273.

    Article  Google Scholar 

  • Bao, J.-X., E. R. Kandel and R. D. Hawkins (1997) Involvement of pre- and postsynaptic mechanisms in posttetanic potentiation at Aplysia synapses. Science 275: 969–973.

    Article  PubMed  CAS  Google Scholar 

  • Braitenberg, V. (1978) Cell assemblies in the cerebral cortex. Theoretical Approaches to Complex Systems. R. Heim and G. Palm, Eds. Berlin, Springer: 171–188.

    Google Scholar 

  • Carandini, M., D. J. Heeger and J. A. Movshon (1997) Linearity and normalization in simple cells of the macaque primary visual cortex. J. Neurosci. 17: 8621–44.

    PubMed  CAS  Google Scholar 

  • Cartling, B. (1993) Control of the complexity of associative memory dynamics by neuronal adaptation. Int. J. Neural Systems 4: 129–141.

    Article  CAS  Google Scholar 

  • Cartling, B. (1995) Autonomous neuromodulatory control of associative processes. Network: Computation in Neural Systems 6: 247–260.

    Article  Google Scholar 

  • Connors, B. W. and M. J. Gutnick (1990) Intrinsic firing patterns of diverse neocortical neurons. Trends Neurosci. 13: 99–104.

    Article  PubMed  CAS  Google Scholar 

  • Eichenbaum, H. (1993) Thinking About Brain Cell Assemblies. Science 261: 993–994.

    Article  PubMed  CAS  Google Scholar 

  • Fransén, E. (1996) Biophysical Simulation of Cortical Associative Memory, Royal Institute of Technology, Stockholm, Sweden, TRITA-NA-P96/28 Dept. of Numerical Analysis and Computer Science.

    Google Scholar 

  • Fransén, E. and A. Lansner (1995) Low spiking rates in a population of mutually exciting pyramidal cells. Network: Computation in Neural Systems 6: 271–288.

    Article  Google Scholar 

  • Fransén, E. and A. Lansner (1998) A model of cortical associative memory based on a horizontal network of connected columns. Network: Computation in Neural Systems 9: 235–264.

    Article  Google Scholar 

  • Freeman, W. and C. Skarda (1985) Spatial EEG patterns, non-linear dynamics and perception: the neo-Sherringtonian view. Brain Res. Rev. 10: 147–175.

    Article  Google Scholar 

  • Fuster, J. and J. P. Jervey (1982) Neuronal firing in the inferotemporal cortex of the monkey in a visual task. J. Neurosci. 2: 361–375.

    PubMed  CAS  Google Scholar 

  • Fuster, J. M. (1995) Memory in the Cerebreal Cortex. Cambridge, Massachusetts, The MIT Press.

    Google Scholar 

  • Gilbert, C. D., J. A. Hirsch and T. N. Wiesel (1990) Lateral interactions in the visual cortex. Cold Spring Harbor Symposia on Quantiative Biology, Cold Spring Harbor Laboratory Press. LV: 663–676.

    Google Scholar 

  • Goldman-Rakic, P. S. (1995) Cellular basis of working memory. Neuron 14: 477–485.

    Article  PubMed  CAS  Google Scholar 

  • Gray, C. M. and W. Singer (1989) Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc. Natl. Acad. Sci. USA 86: 1698–1702.

    Article  PubMed  CAS  Google Scholar 

  • Hebb, D. O. (1949) The Organization of Behavior. New York, John Wiley Inc.

    Google Scholar 

  • Hertz, J., A. Krogh and R. G. Palmer (1991) Introduction to the theory of neural computation. Redwood City, California, Addison-Wesley Publishing Company.

    Google Scholar 

  • Hirsch, J. A. and C. D. Gilbert (1991) Synaptic physiology of horizontal connections in the cat visual cortex. J. Neurosci. 11(6): 1800–1809.

    PubMed  CAS  Google Scholar 

  • Hopfield, J. J. (1982) Neural Networks and physical systems with emergent collective computational properties. Proc. Natl. Acad. Sci. USA 81: 3088–3092.

    Article  Google Scholar 

  • Hubel, D. and T. N. Wiesel (1977) The functional architecture of the macaque visual cortex. The Ferrier lecture. Proc. Royal. Soc. B 198: 1–59.

    Article  CAS  Google Scholar 

  • Johansson, C., A. Sandberg and A. Lansner (2001) A Capacity Study of a Bayesian Neural Network with Hypercolumns, Dept. Numerical Analysis and Computer Science, KTH (TRITA-NA-P0120).

  • Lansner, A. (1982) Information processing in a network of model neurons. A computer simulation study. Royal Institute of Technology, Stockholm, Sweden, Dept. of Numerical Analysis and Computer Science (TRITA-NA-8211).

    Google Scholar 

  • Lansner, A. and Ö. Ekeberg (1989) A one-layer feedback artificial neural network with a Bayesian learning rule. Int. J. Neural Systems 1: 77–87.

    Article  Google Scholar 

  • Lansner, A. and E. Fransén (1992) Modeling Hebbian cell assemblies comprised of cortical neurons. Network: Computation in Neural Systems 3: 105–119.

    Article  Google Scholar 

  • Lansner, A. and A. Holst (1996) A higher order Bayesian neural network with spiking units. Int. J. Neural Systems 7(2): 115–128.

    Article  CAS  Google Scholar 

  • Lansner, A. and A. Sandberg (2001) Functionality and Performance of Brain-Inspired Neural Networks. NOLTA 2001, Zao, Sendai, Japan.

  • LeVay, S. and C. D. Gilbert (1976) Laminar patterns of geniculocortical projections in the cat. Brain Res. 113: 1–19.

    Article  PubMed  CAS  Google Scholar 

  • Levy, W. B. and O. Steward (1979) Synapses as associative memory elements in the hippocampal formation. Brain. Res. 175: 233–245.

    Article  PubMed  CAS  Google Scholar 

  • MacGregor, R. J. and T. McMullen (1978) Computer simulations of diffusely connected neuronal populations. Biol. Cybernetics 28: 121–127.

    Article  CAS  Google Scholar 

  • Martinez, J. L., G. Schulteis and S. B. Weinberger (1991) Learning and Memory: A Biological View. J. L. Martinez and R. P. Kesner, Eds., Academic Press 149–198.

  • McCormick, D. A., B. W. Connors and J. W. Lighthall (1985) Comparative electrophysiology of pyramidal and sparsely spiny stellate neurons of the neocortex. J. Neurophysiol. 54: 782–806.

    PubMed  CAS  Google Scholar 

  • McGaugh, J. L. (2000) Memory — a Century of Consolidation. Science 287: 248–251.

    Article  PubMed  CAS  Google Scholar 

  • Merzenich, M. M., R. J. Nelson, M. P. Stryker, M. S. Cynander, A. Schoppmann and J. M. Zook (1984) Somatosensory map changes following digit amputation in adult monkey. J. Comp. Neurol. 224: 591–605.

    Article  PubMed  CAS  Google Scholar 

  • Miller, K. D. (1995) Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns. Models of Neural Networks III. E. Domany, J. L. van Hemmen and K. Schulten, Eds. New York, Springer-Verlag 55–78.

    Google Scholar 

  • Milner, P. M. (1957) The cell assembly: Mark II. Psychological Review 64: 242–252.

    Article  PubMed  CAS  Google Scholar 

  • Mountcastle, V. B. (1957) Modality and topographic properties of single neurons of cat’s somatic sensory cortex. J. Neurophysiol. 20: 408–434.

    PubMed  CAS  Google Scholar 

  • Mountcastle, V. B. (1998) Perceptual neuroscience: The Cerebral Cortex. Cambridge, Massachusetts, Harvard University Press.

    Google Scholar 

  • Nadal, J. P., G. Toulouse, J.-P. Changeux and S. Dehaene (1986) Networks of formal neurons and palimpsests. Europhys. Lett. 1: 535–42.

    Google Scholar 

  • Olshausen, B. A. and D. J. Field (1997) Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by V1? Vision Research 37: 3311–3325.

    Article  PubMed  CAS  Google Scholar 

  • Palm, G. (1982) Neural Assemblies. An Alternative Approach to Artificial Intelligence. Berlin, Springer.

    Google Scholar 

  • Pulvermuller, F. (1999) Words in the brain’s language. Behavioral and Brain Sciences 22: 280–336.

    Article  Google Scholar 

  • Quinlan, P. (1991) Connectionism and Psychology. A Psychological Perspective on Connectionist Research. New York, Harvester, Whaetsheaf.

    Google Scholar 

  • Rochester, N., J. H. Holland, L. H. Haibt and W. L. Duda (1956) Tests on a cell assembly theory of the action of the brain, using a large digital computer. IRE Trans. Information Theory IT-2: 80–93.

    Article  Google Scholar 

  • Sandberg, A. and A. Lansner (2002) Synaptic Depression as an Intrinsic Driver of Reinstatement Dynamics in an Attractor Network. Neurocomputing 44–46: 615–622.

    Article  Google Scholar 

  • Sandberg, A., A. Lansner, K.-M. Petersson and Ö. Ekeberg (2002) Bayesian attractor networks with incremental learning. Network: Computation in Neural Systems 13(2): 179–194.

    Article  CAS  Google Scholar 

  • Sandberg, A., K. M. Petersson and A. Lansner (2001) Selective Enhancement of Recall through Plasticity Modulation in an Autoassociative Memory. Neurocomputing 38–40: 867–873.

    Article  Google Scholar 

  • Scannell, J. W., C. Blakemore and M. P. Young (1995) Analysis of connectivity in the cat cerebral cortex. J. Neurosci. 15: 1463–1483.

    PubMed  CAS  Google Scholar 

  • Sommer, F. T. and T. Wennekers (2001) Associative memory in networks of spiking neurons. Neural Networks 14: 825–834.

    Article  PubMed  CAS  Google Scholar 

  • Sternberg, S. (1966) High-speed scanning in human memory. Science 153: 652–654.

    Article  PubMed  CAS  Google Scholar 

  • Thorpe, S., D. Fize and C. Marlot (1996) Speed of processing in the human visual system. Nature 381: 520–522.

    Article  PubMed  CAS  Google Scholar 

  • Thorpe, S. and M. Imbert (1989) Biological constraints on connectionist modelling. Connectionism in Perspective. R. Pfeiffer, Ed. Berlin, Springer-Verlag.

    Google Scholar 

  • Traub, R. D., M. A. Whittington, S. B. Colling, G. Buzsaki and J. G. R. Jeffreys (1996) Analysis of gamma rhythms in the rat hippocampus in vitro and in vivo. J. Physiol. 493: 471–84.

    PubMed  CAS  Google Scholar 

  • von Restorff, H. (1933) Analyse von Vorgängen in Spurenfeld. Psychol. Forschung 18: 299–342.

    Article  Google Scholar 

  • Wahlgren, N. and A. Lansner (2001) Biological Evaluation of a Hebbian-Bayesian Learning rule. Neurocomputing 38–40, 433–438.

    Article  Google Scholar 

  • Wallén, P., J. Buchanan, S. Grillner, J. Christenson and T. Hökfelt (1989) The effects of 5-hydroxytryptamine on the afterhyperpolarisation, spike frequency regulation and oscillatory membrane properties in lamprey spinal cord neurons. J. Neurophysiol. 61: 759–768.

    PubMed  Google Scholar 

  • Wickens, J. and R. Kötter (1995) Cellular Models of Reinforcement. Models of Information Processing in the Basal Ganglia. J. C. Houk, J. L. Davis and D. G. Beiser, Eds. Cambridge, Massachusetts, The MIT Press 187–214.

    Google Scholar 

  • Willshaw, D. J. and H. C. Longuet-Higgins (1969) Associative memory models. Machine Learning 5. Meltzer and Michie, Eds. Edinburgh, Scotland, Edinburgh University Press.

    Google Scholar 

  • Yen, S.-C., E. D. Menschik and L. H. Finkel (1999) Perceptual grouping in striate cortical networks mediated by synchronization and desynchronization. Neurocomputing 26–27(1–3): 609–616.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anders Lansner.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lansner, A., Fransén, E. & Sandberg, A. Cell assembly dynamics in detailed and abstract attractor models of cortical associative memory. Theory Biosci. 122, 19–36 (2003). https://doi.org/10.1007/s12064-003-0035-x

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12064-003-0035-x

Key words

Navigation