Biological Cybernetics

, Volume 103, Issue 3, pp 227–236 | Cite as

Enhancement of information transmission of sub-threshold signals applied to distal positions of dendritic trees in hippocampal CA1 neuron models with stochastic resonance

Original Paper


Stochastic resonance (SR) has been shown to enhance the signal-to-noise ratio and detection of low level signals in neurons. It is not yet clear how this effect of SR plays an important role in the information processing of neural networks. The objective of this article is to test the hypothesis that information transmission can be enhanced with SR when sub-threshold signals are applied to distal positions of the dendrites of hippocampal CA1 neuron models. In the computer simulation, random sub-threshold signals were presented repeatedly to a distal position of the main apical branch, while the homogeneous Poisson shot noise was applied as a background noise to the mid-point of a basal dendrite in the CA1 neuron model consisting of the soma with one sodium, one calcium, and five potassium channels. From spike firing times recorded at the soma, the mutual information and information rate of the spike trains were estimated. The simulation results obtained showed a typical resonance curve of SR, and that as the activity (intensity) of sub-threshold signals increased, the maximum value of the information rate tended to increased and eventually SR disappeared. It is concluded that SR can play a key role in enhancing the information transmission of sub-threshold stimuli applied to distal positions on the dendritic trees.


Computer simulation Poisson shot noise Hodgkin–Huxley model Information rate 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Abenavoli A, Forti L, Bossi M, Bergamaschi A, Villa A, Malgaroli A (2002) Multimodal quantal release at individual hippocampal synapses: evidence for no lateral inhibition. J Neurosci 22: 6336–6346PubMedGoogle Scholar
  2. Axmacher N, Mormann F, Fernandez G, Elger CE, Fell J (2006) Memory formation by neuronal synchronization. Brain Res Rev 52: 170–182CrossRefPubMedGoogle Scholar
  3. Benzi R, Sutera A, Vulpiani A (1981) The mechanism of stochastic resonance. J Phys A 14: L453–457CrossRefGoogle Scholar
  4. Brette R, Guigon E (2003) Reliability of spike timing is a general property of spiking model neurons. Neural Comput 15: 279–308CrossRefPubMedGoogle Scholar
  5. Bulsara A, Zador A (1996) Threshold detection of wideband signals: a noise-induced maximum in the mutual information. Phys Rev E 54: R2185–R2188CrossRefGoogle Scholar
  6. Bulsara A, Jacobs E, Zhou T, Moss F, Kiss L (1991) Stochastic resonance in a single neuron model: theory and analog simulation. J Theor Biol 152: 531–555CrossRefPubMedGoogle Scholar
  7. Collins J, Imhoff T, Grigg T (1996) Noise-enhanced information transmission in rat SA1 cutaneous mechanoreceptors via aperiodic stochastic resonance. J Neurophysiol 76: 642–645PubMedGoogle Scholar
  8. Cook EP, Johnston D (1997) Active dendrites reduce location-dependent variability of synaptic input trains. J Neurophysiol 78: 2116–2128PubMedGoogle Scholar
  9. Cox DR, Lewis PAW (1966) The statistical analysis of series of events. Methuen, LondonGoogle Scholar
  10. Dayan P, Abbott LF (2001) Theoretical neuroscience: computational and mathematical modeling of neural systems. The MIT Press, Cambridge, MAGoogle Scholar
  11. Deco G, Schumann B (1997) Stochastic resonance in the mutual information between input and output spike trains of noisy central neurons. Phys D 117: 276–282CrossRefGoogle Scholar
  12. de Ruyter van Steveninck RR, Lewen GD, Strong SP, Koberle R, Bialek W (1997) Reproducibility and variability in neural spike trains. Science 275(5307): 1805–1808CrossRefPubMedGoogle Scholar
  13. Destexhe A, Pare D (1999) Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo. J Neurophysiol 81: 1531–1547PubMedGoogle Scholar
  14. Destexhe A, Rudolph M, Pare D (2003) The high-conductance state of neocortical neurons in vivo. Nat Rev 4: 739–751CrossRefGoogle Scholar
  15. Douglass J, Wilkins L, Pantazelou E, Moss F (1993) Noise enhancement of information transfer in crayfish mechanoreceptors by stochastic resonance. Nature 365: 337–340CrossRefPubMedGoogle Scholar
  16. Gammaitoni L, Hanggi P, Jung P, Marchesoni F (1998) Stochastic resonance. Rev Mod Phys 70: 223–287CrossRefGoogle Scholar
  17. Kish LB, Harmer GP, Abbott D (2001) Information transfer rate of neurons: stochastic resonance of Shannonś information capacity. Fluctuation Noise Lett 1: L13–L19CrossRefGoogle Scholar
  18. Magee J, Johnston D (1995) Synaptic activation of voltage-gated channels in the dendrites of hippocampal pyramidal neurons. Science 268: 301–304CrossRefPubMedGoogle Scholar
  19. Moss F (2000) Stochastic resonance: looking forward. In: Walleczek J (eds) Self-organized biological dynamics & nonlinear control. Cambridge University Press, Cambridge, pp 236–256CrossRefGoogle Scholar
  20. Moss F, Pierson D, O’Gorman D (1994) Stochastic resonance: tutorial and update. Int J Bifurc Chaos 6: 1383–1397Google Scholar
  21. Moss F, Ward LM, Sannita WG (2004) Stochastic resonance and sensory information processing: a tutorial and review of application. Clin Neurophys 115: 267–281CrossRefGoogle Scholar
  22. Nicolis C (1982) Stochastic aspects of climatic transitions—response to periodic forcing. Tellus 34: 1–9CrossRefGoogle Scholar
  23. Rieke F, Warland D, de Ruyter van Steveninck RR, Bialek W (1997) Spikes: exploring the neural code. The MIT Press, Cambridge, MAGoogle Scholar
  24. Scott DW (1979) On optimal and data-based histograms. Biometrika 66: 605–610CrossRefGoogle Scholar
  25. Shannon CE (1949) Communication in the presence of noise. Proc IRE 37: 10–21CrossRefGoogle Scholar
  26. Simonotto E, Riani M, Seife C, Roberts M, Twitty J, Moss F (1997) Visual perception of stochastic resonance. Phys Rev Lett 78: 1186–1189CrossRefGoogle Scholar
  27. Snyder DL, Miller MI (1991) Random point processes in time and space, 2nd edn. Springer-Verlag, New YorkGoogle Scholar
  28. Spruston N, Jaffe DB, Williams SH, Johnston D (1993) Voltage- and space-clamp errors associated with the measurement of electrically remote synaptic events. J Neurophysiol 70: 781–802PubMedGoogle Scholar
  29. Stacey WC, Durand DM (2000) Stochastic resonance improves signal detection in hippocampal CA1 neurons. J Neurophysiol 83: 1394–1402PubMedGoogle Scholar
  30. Stacey WC, Durand DM (2001) Synaptic noise improves detection of subthreshold signals in hippocampal CA1 neurons. J Neurophysiol 86: 1104–1112PubMedGoogle Scholar
  31. Stacey WC, Durand DM (2002) Noise and coupling affect signal detection and bursting in a simulated physiological neural network. J Neurophysiol 88: 2598–2611CrossRefPubMedGoogle Scholar
  32. Stein RB, Gossen ER, Jones KE (2005) Neuronal variability: noise or part of the signal?. Nat Rev 6: 389–397Google Scholar
  33. Traub RD (1982) Simulation of intrinsic bursting in CA3 hippocampal neurons. Neuroscience 7: 1233–1242CrossRefPubMedGoogle Scholar
  34. Warman EN, Durand DM (1994) Reconstruction of hippocampal CA1 pyramidal cell electrophysiology by computer simulation. J Neurophysiol 83: 2192–2208Google Scholar
  35. Zador A (1998) mpact of synaptic unreliability on the information transmitted by spiking neurons. J Neurophysiol 79: 1219–1229PubMedGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringKanto Gakuin UniversityKanazawa-ku, YokohamaJapan
  2. 2.Department of Biomedical Engineering, Neural Engineering CenterCase Western Reserve UniversityClevelandUSA

Personalised recommendations