Skip to main content

Excitatory Synaptic Interaction on the Dendritic Tree

  • Conference paper
Advances in Brain, Vision, and Artificial Intelligence (BVAI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4729))

Included in the following conference series:

  • 1489 Accesses

Abstract

A neuron in the Central Nervous System receives thousands of synaptic inputs arriving both from close and long distance neurons. Synaptic activity modulates the electrical potential of the neuronal membrane producing an output which is regulated by a threshold mechanism. The crossing of the threshold produces a sequence of spikes which, very likely, is the neural representation of the stimulus. Dendrites usually receive the larger amount of synaptic inputs and their role in synaptic integration and code formation in the single neuron cannot be neglected. In the present paper, the mutual interaction of a couple of excitatory synapses connected to the same, terminal, dendritic trunk will be analyzed and some aspects of the computational ability of the “dendritic machinery” will be discussed.

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. Abeles, M.: Role of the cortical neuron: integrator or coincidence detector? Isr. J. Med. Sci. 18, 83–92 (1982)

    Google Scholar 

  2. Abeles, M.: Corticonics: neural circuits of the cerebral cortex. Cambridge University Press, Cambridge, New York (1991)

    Google Scholar 

  3. Agmon-Snir, H., Carr, C.E., Rinzel, J.: The roles of dendrites in auditory coincidence detection. Nature 393, 268–272 (1998)

    Article  Google Scholar 

  4. Cecchi, G.A., Alonso, J.-M., Martinez, L., Chialvo, D.R., Magnasco, M.O.: Noise in neurons is message dependent. Proc. Acad. Sci. USA 97, 5557–5561 (2000)

    Article  Google Scholar 

  5. Di Maio, V., Lánský, P., Rodriguez, R.: Different types of noise in leaky integrate-and-fire model of neuronal dynamics with discrete periodical input. Gen. Physiol. Biophys. 23, 21–38 (2004)

    Google Scholar 

  6. Forti, L., Bossi, M., Bergamaschi, A., Villa, A., Malgaroli, A.: Loose patch recordings of single quanta at individual hippocampal synapse. Nature 388, 874–878 (1997)

    Article  Google Scholar 

  7. Gerstein, G.L., Mandelbrot, B.: Random walk models for the spike activity of single neuron. Biophys. J. 71, 41–68 (1964)

    Google Scholar 

  8. Golding, N.L., Spruston, N.: Dendritic sodium spikes are variable triggers of axon action potential in hippocampal CA1 pyramidal neurons. Neuron 21, 1189–1200 (1998)

    Article  Google Scholar 

  9. Golding, N.L., Staff, N.P., Spruston, N.: Dendritic spikes as mechanism for cooperative long term potentiation. Nature 418, 326–331 (2002)

    Article  Google Scholar 

  10. Jonas, P., Major, G., Sakman, B.: Quantal components of unitary EPSCs at the mossy fibre synapse on CA3 pyramidal cells of rat hippocampus. J. Physiol. 472, 615–663 (1993)

    Google Scholar 

  11. Kock, C., Segev, I.: The role of sinlge neurons in information processing. Nat. Neurosci. 3, 1171–1177 (2000)

    Article  Google Scholar 

  12. Lánský, P.: On approximation of Stein’s neuronal model. J. Theor. Biol. 107, 631–647 (1984)

    Google Scholar 

  13. Lánský, P.: Source of periodical force in noisy integrate-and-fire model of neuronal dynamics. Phys. Rev. E 55, 2040–2043 (1997)

    Article  Google Scholar 

  14. Lánský, P., Lánská, V.: Diffusion approximation of the neural model with synaptic reversal potentials. Biol. Cybern. 56, 19–26 (1987)

    Article  MATH  Google Scholar 

  15. Lánský, P., Rospar, J.-P.: Ornstein-Uhlenbeck model neuron revisited. Biol. Cybern. 72, 397–406

    Google Scholar 

  16. Lánský, P., Sacerdote, L.: The Ornstein-Uhlenbeck neuronal model with the signal dependent noise. Phys. Let. A 285, 132–140 (2001)

    Article  MATH  Google Scholar 

  17. Liu, G.: Local structural balance and functional interaction of excitatory and inhibitory synapses in hippocampal dendrites. Nat. Neurosci. 7, 373–379 (2004)

    Article  Google Scholar 

  18. London, M., Häusser, M.: Dendritic computation. Ann. Rev. Neurosci. 28, 503–532

    Google Scholar 

  19. McCullogh, W.S., Pitts, W.H.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)

    Article  MathSciNet  Google Scholar 

  20. Megías, M., Emri, Z.S., Freund, T.F., Gulyás, A.I.: Total number and distribution of inhibitory and excitatory synapses of hippocampal CA1 pyramidal cells. Neurosc. 102, 527–540 (2001)

    Article  Google Scholar 

  21. Rall, W.: Branching dendritic tree and motorneuron membrane resistivity. Exp. Neurol. 1, 491–527 (1959)

    Article  Google Scholar 

  22. Rall, W.: Theoretical significance of dendritic trees for neuronal input-output relationship. In: Reis, R.F. (ed.) Neural theory and Modeling, Stanford University Press, Palo Alto (1964)

    Google Scholar 

  23. Rall, W., Rinzel, J.: Branch input resistance and steady attenuation for input to one branch of a dendritic neuron model. Biophys. J. 13, 648–688 (1973)

    Google Scholar 

  24. Rinzel, J., Rall, W.: Transient response in a dendritic neuron model for current injected at one branch. Biophys. J. 14, 759–790 (1974)

    Article  Google Scholar 

  25. Segev, I., London, M.: Untangling dendrites with quantitative models. Science 290, 744–750 (2000)

    Article  Google Scholar 

  26. Segev, I., Rinzel, J., Shepherd, G.M.: The theoretical foundation of dendritic function. The MIT Press, Cambridge, London (1995)

    Google Scholar 

  27. Tuckwell, H.C.: Determination of the inter-spike times of neurons receiving randomly arriving post synaptic potentials. Biol. Cybern. 18, 225–237 (1975)

    Article  MATH  Google Scholar 

  28. Ventriglia, F., Di Maio, V.: Neural code and irregular spike trains. In: De Gregorio, M., Di Maio, V., Frucci, M., Musio, C. (eds.) BVAI 2005. LNCS, vol. 3704, pp. 89–98. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  29. Ventriglia, F., Di Maio, V.: Stochastic fluctuation of the synaptic function. Biosystems 67, 287–294 (2002)

    Article  Google Scholar 

  30. Ventriglia, F., Di Maio, V.: Stochastic fluctuation of the quantal EPSC amplitude in computer simulated excitatory synapses of hippocampus. Biosys. 71, 195–204 (2003)

    Article  Google Scholar 

  31. Zador, A.M.: The basic units of computation. Nat. Neurosci. 3, 1167 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Francesco Mele Giuliana Ramella Silvia Santillo Francesco Ventriglia

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Di Maio, V. (2007). Excitatory Synaptic Interaction on the Dendritic Tree. In: Mele, F., Ramella, G., Santillo, S., Ventriglia, F. (eds) Advances in Brain, Vision, and Artificial Intelligence. BVAI 2007. Lecture Notes in Computer Science, vol 4729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75555-5_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-75555-5_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75554-8

  • Online ISBN: 978-3-540-75555-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics