Biological Cybernetics

, 99:403 | Cite as

Information transmission in oscillatory neural activity

Original Paper


Periodic neural activity not locked to the stimulus or to motor responses is usually ignored. Here, we present new tools for modeling and quantifying the information transmission based on periodic neural activity that occurs with quasi-random phase relative to the stimulus. We propose a model to reproduce characteristic features of oscillatory spike trains, such as histograms of inter-spike intervals and phase locking of spikes to an oscillatory influence. The proposed model is based on an inhomogeneous Gamma process governed by a density function that is a product of the usual stimulus-dependent rate and a quasi-periodic function. Further, we present an analysis method generalizing the direct method (Rieke et al. in Spikes: exploring the neural code. MIT Press, Cambridge, 1999; Brenner et al. in Neural Comput 12(7):1531–1552, 2000) to assess the information content in such data. We demonstrate these tools on recordings from relay cells in the lateral geniculate nucleus of the cat.


Spike Train Lateral Geniculate Nucleus Information Rate Surrogate Data Relay Cell 
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  1. Adrian ED (1942) Olfactory reactions in the brain of the hedgehog. J Physiol 100(4): 459–473PubMedGoogle Scholar
  2. Aldworth ZN, Miller JP, Gedeon T, Cummins GI, Dimitrov AG (2005) Dejittered spike-conditioned stimulus waveforms yield improved estimates of neuronal feature selectivity and spike-timing precision of sensory interneurons. J Neurosci 25(22): 5323–5332CrossRefPubMedGoogle Scholar
  3. Ahissar E, Vaadia E (1990) Oscillatory activity of single units in a somatosensory cortex of an awake monkey and their possible role in texture analysis. Proc Natl Acad Sci USA 87(22): 8935–8939CrossRefPubMedGoogle Scholar
  4. Barlow HB, Fitzhugh R, Kuffler SW (1957) Change of organization in the receptive fields of the cat’s retina during dark adaptation. J Physiol 137(3): 338–354PubMedGoogle Scholar
  5. Barrett P, Hunter J, Miller JT, Hsu JC, Greenfield P (2005) Matplotlib—a portable python plotting package. Astron Data Anal Softw Syst XIV ASP Conf Ser 347: 91–395Google Scholar
  6. Barbieri R, Quirk MC, Frank LM, Wilson MA, Brown EN (2001) Construction and analysis of non-Poisson stimulus–response models of neural spiking activity. J Neurosci Methods 105(1): 25–37CrossRefPubMedGoogle Scholar
  7. Berman M (1981) Inhomogeneous and modulated gamma processes. Biometrika 68(1): 143–152CrossRefGoogle Scholar
  8. Borst A, Theunissen FE (1999) Information theory and neural coding. Nature Neurosci 2: 947–957CrossRefPubMedGoogle Scholar
  9. Brenner N, Strong SP, Koberle R, Bialek W, Steveninck RRR (2000) Synergy in a neural code. Neural Comput 12(7): 1531–1552CrossRefPubMedGoogle Scholar
  10. Brown EN, Barbieri R, Ventura V, Kass RE, Frank LM (2002) The time-rescaling theorem and its application to neural spike train data analysis. Neural Comput 14(2): 325–346CrossRefPubMedGoogle Scholar
  11. Bruno RM, Sakmann B (2006) Cortex is driven by weak but synchronously active thalamocortical synapses. Science 312(5780): 1622–1627CrossRefPubMedGoogle Scholar
  12. Castelo-Branco M, Neuenschwander S, Singer W (1998) Synchronization of visual responses between the cortex, lateral geniculate nucleus, and retina in the anesthetized cat. J Neurosci 18(16): 6395–6410PubMedGoogle Scholar
  13. Eckhorn R, Popel B (1975) Rigorous and extended application of information theory to the afferent visual system of the cat. II. Experimental results. Biol Cybern 17(1): 71–77CrossRefPubMedGoogle Scholar
  14. Fellous JM, Houweling AR, Modi RH, Rao RPN, Tiesinga PHE, Sejnowski TJ (2001) Frequency dependence of spike timing reliability in cortical pyramidal cells and interneurons. J Neurophysiol 85(4): 1782–1787PubMedGoogle Scholar
  15. Freeman WJ (1972) Measurement of oscillatory responses to electrical stimulation in olfactory bulb of cat. J Neurophysiol 35(6): 762–779PubMedGoogle Scholar
  16. Fries P, Nikolić D, Singer W (2007) The gamma cycle. Trends Neurosci 30(7): 309–316CrossRefPubMedGoogle Scholar
  17. Gelperin A, Tank DW (1990) Odour-modulated collective network oscillations of olfactory interneurons in a terrestrial mollusc. Nature 345(6274): 437–440CrossRefPubMedGoogle Scholar
  18. Gray CM, Koenig P, Engel AK, Singer W (1989) Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338(6213): 334–337CrossRefPubMedGoogle Scholar
  19. Heiss WD, Bornschein H (1966) Multimodal interval histograms of the continuous activity of retinal cat neurons. Kybernetik 3(4): 187–191CrossRefPubMedGoogle Scholar
  20. Hodgkin AL, Huxley AF (1952) Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo. J Physiol 116(4): 449–472PubMedGoogle Scholar
  21. Hutcheon B, Yarom Y (2000) Resonance, oscillation and the intrinsic frequency preferences of neurons. Trends Neurosci 23(5): 216–222CrossRefPubMedGoogle Scholar
  22. Ishikane H, Gangi M, Honda S, Tachibana M (2005) Synchronized retinal oscillations encode essential information for escape behavior in frogs. Nat Neurosci 8(8): 1087–1095CrossRefPubMedGoogle Scholar
  23. Jarvis MR, Mitra PP (2001) Sampling properties of the spectrum and coherency of sequences of action potentials. Neural Comput 13(4): 717–749CrossRefPubMedGoogle Scholar
  24. Kenyon GT, Theiler J, George JS, Travis BJ, Marshak DW (2004) Correlated firing improves stimulus discrimination in a retinal model. Neural Comput 16(11): 2261–2291CrossRefPubMedGoogle Scholar
  25. Koepsell K, Wang X, Vaingankar V, Wei Y, Wang Q, Rathbun DL, Usrey WM, Hirsch JA, Sommer FT (2008) Retinal oscillations carry visual information to cortex (submitted)Google Scholar
  26. Koyama S, Shinomoto S (2005) Empirical Bayes interpretations of random point events. J Phys A Math Gen 38(29): L531–L537CrossRefGoogle Scholar
  27. Kuffler SW, Fitzhugh R, Barlow HB (1957) Maintained activity in the cat’s retina in light and darkness. J Gen Physiol 40(5): 683–702CrossRefPubMedGoogle Scholar
  28. Laurent G, Davidowitz H (1994) Encoding of olfactory information with oscillating neural assemblies. Science 265(5180): 1872–1875CrossRefPubMedGoogle Scholar
  29. Laufer M, Verzeano M (1967) Periodic activity in the visual system of the cat. Vis Res 7(3): 215–229CrossRefPubMedGoogle Scholar
  30. Masse NY, Cook EP (2008) The effect of middle temporal spike phase on sensory encoding and correlates with behavior during a motion-detection task. J Neurosci 28(6): 1343CrossRefPubMedGoogle Scholar
  31. Munemori J, Hara K, Kimura M, Sato R (1984) Statistical features of impulse trains in cat’s lateral geniculate neurons. Biol Cybern 50(3): 167–172CrossRefPubMedGoogle Scholar
  32. Muresan RC, Jurjut OF, Moca VV, Singer W, Nikolic D (2008) The oscillation score: an efficient method for estimating oscillation strength in neuronal activity. J Neurophysiol 99(3): 1333–1353CrossRefPubMedGoogle Scholar
  33. Montemurro MA, Rasch MJ, Murayama Y, Logothetis NK, Panzeri S (2008) Phase-of-firing coding of natural visual stimuli in primary visual cortex. Curr Biol 8(5): 375–380CrossRefGoogle Scholar
  34. Nowak LG (1997) Influence of low and high frequency inputs on spike timing in visual cortical neurons. Cerebral Cortex 7(6): 487–501CrossRefPubMedGoogle Scholar
  35. Neuenschwander S, Singer W (1996) Long-range synchronization of oscillatory light responses in the cat retina and lateral geniculate nucleus. Nature 379(6567): 728–733CrossRefPubMedGoogle Scholar
  36. Ogawa T, Bishop PO, Levick WR (1966) Cortex is driven by weak but synchronously active thalamocortical synapses. J Neurophysiol 29: 1–30PubMedGoogle Scholar
  37. Oliphant TE (2007) Python for scientific computing. Comput Sci Eng 9(3): 10–20CrossRefGoogle Scholar
  38. OKeefe J, Recce ML (1993) Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus 3(3): 317–330CrossRefGoogle Scholar
  39. Pérez F, Granger BE (2007) IPython: a system for interactive scientific computing. Comput Sci Eng 9(3): 21–29CrossRefGoogle Scholar
  40. Perkel DH, Gerstein GL, Moore GP (1967) Neuronal spike trains and stochastic point processes: I. The single spike train. Biophys J 7(4): 391–418CrossRefPubMedGoogle Scholar
  41. Rodieck RW (1967) Maintained activity of cat retinal ganglion cells. J Neurophysiol 5: 1043–1071Google Scholar
  42. Richmond BJ, Optican LM, Spitzer H (1990) Temporal encoding of two-dimensional patterns by single units in primate primary visual cortex. I. Stimulus-response relations. J Neurophysiol 64(2): 351–369PubMedGoogle Scholar
  43. Rieke F, Warland D, van Steveninck RR, Bialek W (1999) Spikes: exploring the neural code. MIT Press, CambridgeGoogle Scholar
  44. Szwed M, Bagdasarian K, Ahissar E (2003) Encoding of vibrissal active touch. Neuron 40(3): 621–630CrossRefPubMedGoogle Scholar
  45. Stopfer M, Jayaraman V, Laurent G (2003) Intensity versus identity coding in an olfactory system. Neuron 39(6): 991–1004CrossRefPubMedGoogle Scholar
  46. Sahani M, Linden JF (2003) Evidence optimization techniques for estimating stimulus–response functions. In: Obermayer K, Becker S, Thrun S(eds) Advances in neural information processing systems, proceedings of the 2002 conference, vol 15.. MIT Press, Cambridge, pp 109–116Google Scholar
  47. Samonds JM, Zhou Z, Bernard MR, Bonds AB (2006) Synchronous activity in cat visual cortex encodes collinear and cocircular contours. J Neurophysiol 95(4): 2602–2616CrossRefPubMedGoogle Scholar
  48. Tiesinga P, Fellous JM, Sejnowski TJ (2008) Regulation of spike timing in visual cortical circuits. Nat Rev Neurosci 9(2): 97–107CrossRefPubMedGoogle Scholar
  49. Tuckwell HC (1988) Introduction to theoretical neurobiology. Cambridge University Press, CambridgeGoogle Scholar
  50. Usrey WM, Alonso JM, Reid RC (2000) Synaptic interactions between thalamic inputs to simple cells in cat visual cortex. J Neurosci 20(14): 5461PubMedGoogle Scholar
  51. Wang X, Wei Y, Vaingankar V, Wang Q, Koepsell K, Sommer FT, Hirsch JA (2007) Feedforward excitation and inhibition evoke dual modes of firing in the cat’s visual thalamus during naturalistic viewing. Neuron 55(3): 465–478CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Redwood Center for Theoretical Neuroscience, Helen Wills Neuroscience InstituteUniversity of California at BerkeleyBerkeleyUSA

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