Skip to main content

Simulation Studies of the Speed of Recurrent Processing

  • Chapter
  • First Online:
Emergent Neural Computational Architectures Based on Neuroscience

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2036))

  • 692 Accesses

Abstract

The speed of processing in the cortex can be fast. For exam- ple, the latency of neuronal responses in the visual system increases by only approximately 10-20 ms per area in the ventral pathway sequence V1 to V2 to V4 to Inferior Temporal visual cortex. Since individual neu- rons can be regarded as relatively slow computing elements, this may imply that such rapid processing can only be based on the feedforward connections across cortical areas. In this paper, we study this problem by using computer simulations of networks of spiking neurons. We eval- uate the speed with which different architectures, namely feed-forward and recurrent architectures, retrieve information stored in the synaptic efficacy. Through the implementation of continuous dynamics, we found that recurrent processing can take as little as 10-15 ms per layer. This is much faster than obtained with simpler models of cortical processing that are based on simultaneous updating of the firing rate of the individual units. These findings suggest that cortical information processing can be very fast even when local recurrent circuits are critically involved.

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 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. J. Bullier and L.G. Nowak. Parallel versus serial processing: new vistas on the distributed organization of the visual system. Current Opinion in Neurobiology, 5:497–503, 1995.

    Article  Google Scholar 

  2. M.T. Schmolesky, Y. Wang, D.P. Hanes, K.G. Thompson, S. Leutgeb, J.D. Schall, and A.G. Leventhal. Signal timing across the macaque visual system. J. Neurophysiol., 79:3272–3277, 1998.

    Google Scholar 

  3. Y. Sugase, S. Yamane, S. Ueno, and K. Kawano. Global and fine information coded by single neurons in the temporal visual cortex. Nature, 400:869–873, 1999.

    Article  Google Scholar 

  4. M.J. Tovée, E.T. Rolls, A. Treves, and R.P. Bellis. Information encoding and the response of single neurons in the primate temporal visual cortex. J. Neurophysiol., 70:640–654, 1993.

    Google Scholar 

  5. J. Heller, J.A. Hertz, T.W. Kjaer, and B.J. Richmond. Information flow and temporal coding in primate pattern vision. J. Comp. Neurosci., 2:175–193, 1995.

    Article  Google Scholar 

  6. E.T. Rolls, M.J. Tovee, and S. Panzeri. The neurophysiology of backward visual masking: Information analysis. J. Cognitive Neurosci., 11:300–311, 1999.

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. R.S. Petersen and M.E. Diamond. Spatial-temporal distribution of whisker-evoked activity in rat somatosensory cortex and the coding of stimulus location. Journal of Neuroscience, 20:6135–6143, 2000.

    Google Scholar 

  9. C. Koch and I. Segev, editors. Methods in Neuronal Modelling. MIT Press, Cambridge, MA, 2nd edition, 1998.

    Google Scholar 

  10. S.J. Thorpe and M. Imbert. Biological constraints on connectionist models. In R. Pfeifer, Z. Schreter, and F. Fologelman-Soulie, editors, Connectionism in Perspective, pages 63–92, Amsterdam, 1989. Elsevier.

    Google Scholar 

  11. M.W. Oram and D.I. Perrett. Time course of neuronal responses discriminating different views of face and head. J. Neurophysiol., 68:70–84, 1992.

    Google Scholar 

  12. M.W. Oram and D.I. Perrett. Modeling visual recognition from neurobiological constraints. Neural Networks, 7:945–972, 1994.

    Article  Google Scholar 

  13. M. Riesenhuber and T. Poggio. Are cortical models really bound by the “binding problem#x201D;? Neuron, 24:87–93, 1999.

    Article  Google Scholar 

  14. J.J. Hopffeld. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the USA, 79:2554–2558, 1982.

    Article  Google Scholar 

  15. D.E. Rumelhart and J.L. McClelland, editors. Parallel Distributed Processing. MIT Press, Cambridge, MA, 1986.

    Google Scholar 

  16. D.J. Amit. Modeling Brain Function. Cambridge University Press, Cambridge, UK, 1989.

    MATH  Google Scholar 

  17. E.T. Rolls and A. Treves. Neural networks and brain function. Oxford University Press, Oxford, U.K., 1998.

    Google Scholar 

  18. M.V. Tsodyks and T.J. Sejnowski. Rapid state switching in balanced cortical models. Network, 6:111–124, 1995.

    MATH  Google Scholar 

  19. W. Gerstner. Population dynamics of spiking neurons: fast transients, asynchronous states and locking. Neural Comp., 12:43–89, 2000.

    Article  Google Scholar 

  20. A. Treves. Mean-field analysis of neuronal spike dynamics. Network, 4:259–284, 1993.

    MATH  MathSciNet  Google Scholar 

  21. F.P. Battaglia and A. Treves. Rapid stable retrieval in high-capacity realistic associative memories. Neural Comp., 10:431–450, 1998.

    Article  Google Scholar 

  22. S. Panzeri, E.T. Rolls, F.P. Battaglia, and R. Lavis. Speed of feed-forward and recurrent processing in multilayer networks of integrate-and-fire neurons. 2000.

    Google Scholar 

  23. C. VanVreeswijk, L.F. Abbott, and G. Bard Ermentrout. When inhibition not excitation synchronizes neuronal firing. J. Comput. Neurosci., 1:313–321, 1994.

    Article  Google Scholar 

  24. G. Parisi. A memory which forgets. Journal of Physics, A 19:617–619, 1986.

    Google Scholar 

  25. L.F. Abbott. Realistic synaptic inputs for model neural networks. Network, 2:245–258, 1991.

    MATH  Google Scholar 

  26. C.E. Shannon and W. Weaver. The Mathematical Theory of Information. University of Illinois Press, Urbana, Illinois, USA, 1949.

    Google Scholar 

  27. E.T. Rolls, A. Treves, and M.J. Tovée. The representational capacity of the distributed encoding of information provided by populations of neurons in primate temporal visual cortex. Exp. Brain Res., 114:149–162, 1997.

    Article  Google Scholar 

  28. S. Panzeri, A. Treves, S. Schultz, and E.T. Rolls. On decoding the responses of a population of neurons from short time windows. Neural Comp., 11:1553–1577, 1999.

    Article  Google Scholar 

  29. S. Panzeri and A. Treves. Analytical estimates of limited sampling biases in different information measures. Network, 7:87–107, 1996.

    Article  MATH  Google Scholar 

  30. S. Son, K.D. Miller, and L.F. Abbott. Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience, 3:919–926, 2000.

    Article  Google Scholar 

  31. M.N. Shadlen and W.T. Newsome. The variable discharge of cortical neurons: implications for connectivity, computation and coding. J. Neurosci., 18(10):3870–3896, 1998.

    Google Scholar 

  32. D.A. McCormick. Membrane properties and neurotransmitter actions. In G.M. Shepherd, editor, The Synaptic Organization of the Brain, chapter 2, pages 37–75. Oxford University Press, Oxford, U.K., 1998.

    Google Scholar 

  33. A. Treves. Local neocortical processing: a time for recognition. Int. J. of Neuronal Systems, 3:115–119, 1993.

    Article  Google Scholar 

  34. R/ Ben-Yishai, R. Bar-Or, and H. Sompolinsky. Theory of orientation tuning in visual cortex. Proc. Natl. Acad. Sci. USA, 92:3844–3848, 1995.

    Article  Google Scholar 

  35. D.J. Felleman and D.C. VanEssen. Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1:1–47, 1991.

    Article  Google Scholar 

  36. C.C. Hilgetag, M.A. O’Neill, and M.P. Young. Indeterminate organization of the visual system. Science, 217:776–777, 1996.

    Article  Google Scholar 

  37. M. Jordan. An introduction to linear algebra in parallel distributed processing. In D.E. Rumelhart and J.L. McClelland, editors, Parallel Distributed Processing, pages 365–422, Cambridge, MA, 1986. MIT Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Panzeri, S., Rolls, E.T., Battaglia, F.P., Lavis, R. (2001). Simulation Studies of the Speed of Recurrent Processing. In: Wermter, S., Austin, J., Willshaw, D. (eds) Emergent Neural Computational Architectures Based on Neuroscience. Lecture Notes in Computer Science(), vol 2036. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44597-8_24

Download citation

  • DOI: https://doi.org/10.1007/3-540-44597-8_24

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42363-8

  • Online ISBN: 978-3-540-44597-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics