Journal of Computational Neuroscience

, Volume 17, Issue 2, pp 179–201 | Cite as

Modeling Compositionality by Dynamic Binding of Synfire Chains

  • Moshe Abeles
  • Gaby Hayon
  • Daniel Lehmann


This paper examines the feasibility of manifesting compositionality by a system of synfire chains. Compositionality is the ability to construct mental representations, hierarchically, in terms of parts and their relations. We show that synfire chains may synchronize their waves when a few orderly cross links are available. We propose that synchronization among synfire chains can be used for binding component into a whole. Such synchronization is shown both for detailed simulations, and by numerical analysis of the propagation of a wave along a synfire chain. We show that global inhibition may prevent spurious synchronization among synfire chains. We further show that selecting which synfire chains may synchronize to which others may be improved by including inhibitory neurons in the synfire pools. Finally we show that in a hierarchical system of synfire chains, a part-binding problem may be resolved, and that such a system readily demonstrates the property of priming. We compare the properties of our system with the general requirements for neural networks that demonstrate compositionality.

synfire-chains compositionality binding-mechanism neural-networks 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Abeles M (1982) Local Cortical Circuits-An Electrophysiological Study. Springer-Verlag, Berlin.Google Scholar
  2. AbelesM(1991) Corticonics, Neural Circuits of the Cerebral Cortex. Cambridge University Press.Google Scholar
  3. Abeles M, Prut Y, Vaadia E, Bergman H (1994) Synchronization in neuronal transmissionand its importance for information processing. In: G. Buzsaki, et al., eds. Temporal Coding in the Brain, pp. 39–50.Google Scholar
  4. Abeles M, Vaadia E, Bergman H, Prut Y, Haalman I, Slovin H (1993) Dynamics of neural interaction in the frontal cortex of behaving monkeys. Concepts in Neuroscience 4(2): 131–158.Google Scholar
  5. Aertsen A, Diesmann M, Gewaltig MO (1996) Characterization of synfire activity by propagating pulse packets. In: JM Bower, ed. Advances in Computational Neurosc. Plenum.Google Scholar
  6. Aertsen A, Diesmann M, Gewaltig MO (1996) Propagation of synchronous spiking activityin feedforward neural networks. J. Physiology90(3/4): 243–247.Google Scholar
  7. Arnoldi R, Brauer W (1996) Synchronization without oscillatory neurons. Biol. Cybernet. 74: 209–223.CrossRefGoogle Scholar
  8. Barlow H (1995) The neuron doctrine in perception. In: M. Gazzaniga, ed., The Cognitive Neuronsciences pp. 415–435. MIT Press.Google Scholar
  9. Biederman I, Hummel JE (1992) Dynamical binding in a neural network for shape recognition. Psychol. Review 99(3): 480–517.CrossRefGoogle Scholar
  10. Bienenstock E (1991) Notes on the growth of a composition machine. In: D. Adler, E. Bienenstock. Laks, eds., Proceeding of the Royaumont Interdisciplinary Workshop on Compositionality in Cognition and Neural Network-I.Google Scholar
  11. Bienenstock E (1992) Suggestions for a Neurobiological Approach to Syntax. Private Communication.Google Scholar
  12. Bienenstock E (1995) A model of neocortex. Network: Comput. Neural Syst. 6: 179–224.CrossRefGoogle Scholar
  13. Bienenstock E (1996) Composition. In: A. Aertsen and V. Braitenberg, eds., Brain Theory: Biological Basis and Computational Theory of Vision. Elsevier.Google Scholar
  14. Bienenstock E, Geman S (1994) Compositionality. In: Michael Arbib, ed., The Handbookof Brain Theory and Neural Networks. MIT Press.Google Scholar
  15. Bienenstock E, Geman S, PotterD(1997) Compositionality, mdl priors, and object recognition.In: MI Jordan, MC Mozer, T Petsche, eds., Advances in Neural Information Processing Systems, vol. 9. MIT Press.Google Scholar
  16. Braitenberg V (1986) Two views of the cerebral cortex. In: G Palm, A Aertsen, eds., Brain Theory Springer, Berlin, pp. 81–96.Google Scholar
  17. Carreiro LRR, Haddad H Jr, Baldo MVC (2003) The modulation of simple reaction time by the spatial probability of a visual stimulus. Braz. J. Med. Biol. Res. 36: 907–911.PubMedGoogle Scholar
  18. Cohen A, Shoup R (2000) Response selection target for conjunctive targets. J. Experim. Psych.: Human Percep. Perform. 26: 391–411.CrossRefGoogle Scholar
  19. Damasio AR (1989) Time-locked multiregional retroactivation: A systems-level proposal for the neural substrates of recalll and cognition. Cognition 33: 25–62.CrossRefPubMedGoogle Scholar
  20. Dickinson SJ, Pentland AP, Rosenfeld A (1992) From volumes to views: An approach to 3-d object recognition. Image Understanding 55: 130–154.Google Scholar
  21. Diesmann M, Gewaltig MO, Aertsen A (1999) Stable propagation of synchronous spiking in cortical neural networks. Nature 402: 529–533.CrossRefPubMedGoogle Scholar
  22. Doursat R (1991) Contribution `a l'etude des representations dans le syst`eme nerveux et dans les reseaux de neurones formels. PhD thesis, Universite Paris, 6.Google Scholar
  23. Eckhorn R, Brauer R, Jordan W, Brosch M, Kruse W, Munk M, Reitboeck HJ (1988) Coherent oscillations: A mechanism for feature linking in the visual cortex? Biol. Cyber. 60: 121–130.Google Scholar
  24. Ellis W (1938) A Source Book of Gesstalt Psychology. Humanities Press.Google Scholar
  25. Van Essen DC, Felleman DJ, De Yoe EA, Olavarria J, Knierim J (1991) Modular and hierarchical organization of extrastriate visual cortex in macaque monkey. In: Cold Spring Harbor Symposiom of Quantitative Biology vol. 55, pp. 679–696.Google Scholar
  26. Geman S, Potter DF, Chi Z (1998) Composition systems. Technical report, Division of Applied Mathematics, Brown University.Google Scholar
  27. Gray CM (1999) The temporal correlation hypothesis of visual feature integration: Still alive and well. Neuron 24: 31–47.CrossRefPubMedGoogle Scholar
  28. Gray CM, Singer W (1989) Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc. Natl. Acad. Sci USA 92: 6655–6662.Google Scholar
  29. Hayon G (2004) Modeling compositionality in biological neural networks by dynamic binding of synfire chains. PhD thesis, the Hebrew University in Jerusalem, 2003.Google Scholar
  30. Hayon G, Abeles M, Lehmann D (2004) A model for reresenting the dynamics of a system of synfire chains. J. Comp. Neurosci., submitted.Google Scholar
  31. HebbDO(1949) Organization of Behaviour.Wiley Press, NewYork.Google Scholar
  32. Herrmann M, Hertz J, Prugel-Bennett A (1995) Analysis of synfire chains. Network: Computation in Neural Systems 6: 403–414.CrossRefGoogle Scholar
  33. Hertz J, Krogh A, Palmer R (1991) Introduction to the Theory of Neural Computation. Addison-Wesley Publishing Company.Google Scholar
  34. Hertz J, Prugel-Bennett A (1995) Learning short synfire chains by self-organization. Network: Computation in Neural Systems.Google Scholar
  35. Horn D, Levy N, Meilijson I, Ruppin E (1999) Distributed synchrony of spiking neurons in a hebbian cell assembly. In: NIPS99.Google Scholar
  36. Horn D, Opher I (1996) The importance of noise for segmentation and binding in dynamical neural systems. International Journal of Neural Systems 7(4): 529–535.CrossRefPubMedGoogle Scholar
  37. Horn D, Sagi D, UsherM(1991) Segmentation, binding and illusory conjunctions. Neural Comput. 3: 510–525.Google Scholar
  38. Hummel JE (2001) Complementary solution to the binding problem in vision: Implications for shape perception and object recognition. Visual Cognition 8: 489–517.CrossRefGoogle Scholar
  39. Hummel JE, Stankiewicz BJ (1996) An architecture for rapid hierarchical structural description. In: T Inui, J McClelland, eds., Information Integration in Perception and Communication vol. XVI of Attention and Performance, MIT Press, Cambridge, MA, pp. 93–121.Google Scholar
  40. Litvak V, Sompolinsky H, Segev I, Abeles M(2002) On the transmission of rate code in long feed-forward networks with excitatoryinhibitory balance. J. Neurosci. 23: 3006–3015.Google Scholar
  41. MaassW(1997) Fast sigmoidal networks via spiking neurons. Neural Computation 9: 279–304.Google Scholar
  42. MaassW, Natschlager T (1997) Network of spiking neurons can emulate arbitrary hopfield nets in temporal coding. Network: Computation in Neural Systems.Google Scholar
  43. Postma EO, van der Herik HJ, Hudson PTW (1996) Robust feedforward processing in synfire chains. International Journal of Neural Systems 7(4): 537–542.CrossRefPubMedGoogle Scholar
  44. Potter DF (1999) Compositional Pattern Recognition. PhD thesis, Division of Applied Mathematics, Brown University.Google Scholar
  45. Prut Y, Vaadia E, Bergman H, Haalman I, Slovin H, AbelesM(1998) Spatiotemporal structure of cortical activity: Properties and behavioral relevance. J. Neurophysiol. 79(6): 2857–2874.PubMedGoogle Scholar
  46. Reynolds JH, Desimone R (1999) The role of neural mechanisms of attention in solving the binding problem. Neuron 24: 19–29.CrossRefPubMedGoogle Scholar
  47. Riesenhuber M, Poggio T (1999) Are cortical models realy bound by the “binding problem”. Neuron 24: 87–93.CrossRefPubMedGoogle Scholar
  48. Rissanen J (1989) Stochastic Complexity in Statistical Inquiry.World Scientific Press.Google Scholar
  49. Roskies AL (1999) The binding problem. Neuron 24: 7–9.CrossRefPubMedGoogle Scholar
  50. Shadlen MN, Newsome WT(1998) The variable discharge of cortical neurons: Implications for connectivity, computation, and information coding. J. Neurosci. 18(10): 3870–3896.Google Scholar
  51. Shadlen MN, Movshon A (1999) Synchrony unbound: A critical evaluation of the temporal binding hypothesis. Neuron 24: 67–77.CrossRefPubMedGoogle Scholar
  52. Shastri L, Ajjanagadde V(1993) From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic binding using temporal synchrony. Behav. Br. Sci. 16: 417–494.Google Scholar
  53. SingerW(1999) Neural synchrony:Aversatile code for the definition of relations? Neuron 24: 49–65.Google Scholar
  54. Sougne JP, French RM (2001) Synfire chains and catastrophic interference. In: Proceedings of the 23rd Annual Conferences of the Cognitive Science Society. Lawrence Erbaum Ass., Mahwah, NJ.Google Scholar
  55. Sterratt DC (1999) Is a biological temporal learning rule compatible with learning synfire chains? In: Proceedings of ICANN 99 can be found at dcs/research.html.Google Scholar
  56. Treisman A (1996) The binding problem. Current Opinion in Neurobiology 6: 171–178.CrossRefPubMedGoogle Scholar
  57. Treisman A (1999) Solutions to the binding problem: Progress through controversy and convergence. Neuron 24: 105–110.CrossRefPubMedGoogle Scholar
  58. Triesch J, von der Malsburg C (1996) Binding-A proposed experiment and a model. In: Proceedings of the International Conference on Artificial Neural Networks Bochum.Google Scholar
  59. Ullman S (1989) Aligning pictorial descriptions: An approach to object recognition. Cognition 32(3): 193–254.CrossRefPubMedGoogle Scholar
  60. Ullman S (1996) High-level Vision. The MIT Press.Google Scholar
  61. Villa AEP, Tetko IV, Holand B, Najem A (1999) Spatiotemporal activity patterns of rat cortical neurons predict responses in a conditioned task. In: Proceedings of the National Academy of Science USA 96: 1106–1111.CrossRefGoogle Scholar
  62. von der Malsburg C (1981) The Correlation Theory of Brain Function, Springer, Berlin, vol. Models of Neural Networks II, Chap. 2, pp. 95–119.Google Scholar
  63. von der Malsburg C (1987) Synaptic plasticity as basis of brain organization.In: JP Changeuxand, M Konishi, eds., The Neural and Molecular Bases of Learning Wiley, New York. Exact dynamics in feedforward neural networks. Europhysics Letters 38(8): 631–636.Google Scholar
  64. Zucker RS (1989) Short-term synaptic plasticity. Ann. Rev. Neuroscience 12: 13–31.CrossRefGoogle Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Moshe Abeles
    • 1
    • 2
  • Gaby Hayon
    • 3
  • Daniel Lehmann
    • 4
  1. 1.Department of Physiology and the Center for Neural ComputationThe Hebrew UniversityJerusalemIsrael
  2. 2.Gonda Brain Research CenterBar-Ilan UniversityRamat-GanIsrael
  3. 3.The Center for Neural ComputationThe Hebrew UniversityJerusalemIsrael
  4. 4.The Hebrew UniversityJerusalemIsrael

Personalised recommendations