Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-23T17:26:06.121Z Has data issue: false hasContentIssue false
  • English
  • Français

Fine structure analysis of temporal patterns in the light response of cells in the lateral geniculate nucleus of cat

Published online by Cambridge University Press:  02 June 2009

Florentin Wörgötter  and
Klaus Funke
Florentin Wörgötter
Affiliation:
Institute of Physiology, Department of Neurophysiology, Ruhr-Universität Bochum, 44780 Bochum, Germany
Klaus Funke
Affiliation:
Institute of Physiology, Department of Neurophysiology, Ruhr-Universität Bochum, 44780 Bochum, Germany
Get access Rights & Permissions [Opens in a new window]

Abstract

This study focuses on the analysis of temporal patterns in the spike train of cells in the lateral geniculate nucleus (LGN) of cat. Two-hundred eighty-three units have been recorded extracellularly in anesthetized animals during visual stimulation with flashing spot stimuli of different size. We used a novel method of temporally local computed interval distributions (intervalogram; Funke & Wörgötter, 1995) to visualize the statistical distribution of interspike intervals during different phases of the visual response. Multimodal interval distributions were observed mainly in X- and Y-ON cells, reflecting the tendency of these cells to fire with preferred intervals during the sustained light response. The shortest preferred interval is called the fundamental interval and the longer ones (higher-order intervals) are, in general, multiples thereof. During increasing surround inhibition a redistribution of the intervals towards the higher orders was observed. We regarded the different peaks in the interval distributions as different components of possible temporal spike sequences and performed a pattern search up to the level of five subsequent intervals. While it is obvious, that the dominant peak is most strongly represented in any interval sequence, we also show that a significant overrepresentation of short sequences of similar intervals exists. The repetition rate is rather small (4–5 intervals) and, therefore, no long-lasting oscillatory pattern was observed in the autocorrelograms. Power spectral analysis of the peristimulus-time histograms, however, revealed that the sequential firing pattern is strongly stimulus locked at least for the majority of sweeps in the records.

The mean firing rate of an LGN cell decreases with increasing stimulus size as well as with decreasing contrast. Therefore, the mean rate cannot be used to distinguish between these situations. While in the whole network this tradeoff can be resolved by the combined activity of multiple cells, our findings additionally suggest that contrast and size can be distinguished already at the single-cell level using different temporal patterns.

Keywords

CatVisionSingle unitInterval analysisRecurrent interval sequences
Type
Research Articles
Information
Visual Neuroscience , Volume 12 , Issue 3 , May 1995 , pp. 469 - 484
Copyright
Copyright © Cambridge University Press 1995

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

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. Proceedings of the National Academy of Sciences of the U.S.A. 87, 8935–8939.CrossRefGoogle Scholar
Ariel, M., Daw, N.W. & Rader, R.K. (1983). Rhythmicity in rabbit retinal ganglion cell responses. Vision Research 23, 1485–1493.CrossRefGoogle ScholarPubMed
Bishop, P.O., Levick, W.R. & Williams, W.O. (1964). Statistical analysis of the dark discharge of lateral geniculate neurones. Journal of Physiology 170, 598–612.CrossRefGoogle Scholar
Bracewell, R.N. (1986). The Fourier Transform and Its Applications. New York: McGraw-Hill Inc.Google Scholar
Braun, H.A., Wissing, H., Schäfer, K. & Hirsch, M.C. (1994). Oscillation and noise determine signal transduction in shark multimodal sensory cells. Nature 367, 270–273.CrossRefGoogle ScholarPubMed
Derrington, A.M. & Fuchs, A.F. (1979). Spatial and temporal properties of X and Y cells in the cat lateral geniculate nucleus. Journal of Physiology 293, 347–364.CrossRefGoogle ScholarPubMed
Duda, R.O. & Hart, P.E. (1973). Pattern Classification and Scene Analysis. New York: Wiley.Google Scholar
Eckhorn, R., Bauer, R., Jordan, W., Brosch, M., Kruse, W., Munk, M. & Reitboeck, H.J. (1988). Coherent oscillations: A mechanism of feature linking in the visual cortex? Biological Cybernetics 60, 121–130.CrossRefGoogle ScholarPubMed
Eckhorn, R., Frien, A., Bauer, R., Woelbern, T. & Kehr, H. (1993). High frequency (60–90 Hz) oscillations in primary visual cortex of awake monkey. NeuroReport 4, 243–246.CrossRefGoogle ScholarPubMed
Engel, A.K., König, P., Kreiter, A.K. & Singer, W. (1991). Interhemispheric synchronization of oscillatory neuronal responses in cat visual cortex. Science 252, 1177–1179.CrossRefGoogle ScholarPubMed
Engel, A.K., König, P., Kreiter, A.K., Schillen, T.B. & Singer, W. (1992). Temporal coding in the visual cortex: New vistas on integration in the nervous system. Trends in Neuroscience 15, 218–226.CrossRefGoogle ScholarPubMed
Eskandar, E.N., Optican, L.N. & Richmond, B.J. (1992). Role of inferior temporal neurons in visual memory: II. Multiplying temporal waveforms related to vision and memory. Journal of Neurophysiology 68, 1296–1306.CrossRefGoogle ScholarPubMed
Eysel, U.Th. & Gaedt, C. (1971). Maintained activity in the lateral geniculate body of the cat and the effects of visual deprivation. Pfliigers Archiv 327, 68–81.CrossRefGoogle Scholar
Eysel, U.T., Grüsser, O.-J. & Hoffmann, K.-P. (1979). Monocular deprivation and the signal transmission by X- and Y-neurons of the cat lateral geniculate nucleus. Experimental Brain Research 34, 521–539.CrossRefGoogle ScholarPubMed
Funke, K. & Eysel, U.T. (1992). EEG-dependent modulation of response dynamics of cat dLGN relay cells and the contribution of corticogeniculate feedback. Brain Research 573, 217–227.CrossRefGoogle ScholarPubMed
Funke, K. & Wörgötter, F. (1995). Differences in the temporal dynamics of the visual ON- and OFF-pathways. Experimental Brain Research (in press).CrossRefGoogle Scholar
Gawne, T., McClurkin, J.W., Richmond, B.J. & Optican, L.M. (1991). Interactive effects among several stimulus parameters on the responses of striate cortical complex cells. Journal of Neurophysiology 66, 379–389.CrossRefGoogle ScholarPubMed
Ghose, G.M. & Freeman, R.D. (1992). Oscillatory discharge in the visual system: Does it have a functional role? Journal of Neurophysiology 69, 1558–1574.CrossRefGoogle Scholar
Gray, C.M., König, P., Engel, A.K. & Singer, W. (1989). Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338, 334–337.CrossRefGoogle ScholarPubMed
Hashemiyoon, R. & Chaplin, J.K. (1993). Retinally derived fast oscillations coding for global stimulus properties synchronize multiple visual system structures. Society for Neuroscience Abstracts 19, S220.1.Google Scholar
Jagadeesh, B., Gray, C.M. & Ferster, D. (1992). Visually evoked oscillations of membrane potential in cells of cat visual cortex. Science 257, 552–554.CrossRefGoogle ScholarPubMed
Laufer, M. & Verzeano, M. (1967). Periodic activity in the visual system of the cat. Vision Research 7, 215–229.CrossRefGoogle ScholarPubMed
Llinás, R. & Ribary, U. (1993). Coherent 40-Hz oscillations characterizes dream state in humans. Proceedings of the National Academy of Sciences of the U.S.A. 90, 2078–2081.CrossRefGoogle ScholarPubMed
McClurkin, J.W., Gawne, T.J., Optican, L.M. & Richmond, B.J. (1991 a). Lateral geniculate neurons in behaving primates. II. Encoding of visual information in the temporal shape of the response. Journal of Neurophysiology 66, 794–808.CrossRefGoogle ScholarPubMed
McClurkin, J.W., Optican, L.M., Richmond, B.J. & Gawne, T.J. (1991 b). Concurrent processing and complexity of temporally encoded neuronal messages in visual perception. Science 253, 675–677.CrossRefGoogle ScholarPubMed
Moore, G.P., Perkel, D.H. & Segundo, J.P. (1966). Statistical analysis and functional interpretation of neuronal spike data. Annual Reviews of Physiology 28, 493–522.CrossRefGoogle Scholar
Munemori, J., Hara, K., Kimura, M. & Sato, R. (1984). Statistical features of impulse trains in cat's lateral geniculate neurons. Biological Cybernetics 50, 167–172.CrossRefGoogle ScholarPubMed
Neuenschwander, S. & Varela, F.J. (1993). Visually triggered neuronal oscillations in the pigeon—An autocorrelation study of tectal activity. European Journal of Neuroscience 5, 870–881.CrossRefGoogle Scholar
Pinault, D. & Deschēnes, M. (1992). Voltage-dependent 40–Hz oscillations in rat reticular thalamic neurons in vivo. Neuroscience 51, 245–258.CrossRefGoogle ScholarPubMed
Podvigin, N.F., Jokeit, H. & Poeppel, E. (1992). Stimulus/dependent oscillatory activity in the lateral geniculate body of the cat. Naturwissenschaften 79, 428–431.CrossRefGoogle ScholarPubMed
Przybyszewski, A.W., Lankheet, M.J.M. & Van De Grind, W.A. (1993). Nonlinearity and oscillations in X-type ganglion cells of the cat retina. Vision Research 33, 861–875.CrossRefGoogle ScholarPubMed
Ribary, U., Ioannides, A.A., Singh, K.D., Hasson, R., Bolton, J.P.R., Lado, F., Mogilner, A. & Llinás, R. (1991). Magnetic field tomography of coherent thalamocortical 40–Hz oscillations in humans. Proceedings of the National Academy of Sciences of the U.S.A. 88, 11037–11041.CrossRefGoogle ScholarPubMed
Richmond, B.J. & Optican, L.M. (1990). Temporal encoding of two-dimensional patterns by single units in the primate primary visual cortex. II. Information transmission. Journal of Neurophysiology 64, 370–380.CrossRefGoogle ScholarPubMed
Sebestyen, G.S. (1962). Decision Making Processes in Pattern Recognition New York: The McMillan Company.Google Scholar
Steriade, M., Curró Dossi, R., Paré, D. & Oakson, G. (1991). Fast oscillations (20–40 Hz) in thalamocortical systems and their potentiation by mesopontine cholinergic nuclei in the cat. Proceedings of the National Academy of Sciences of the U.S.A. 88, 4396–4400.CrossRefGoogle ScholarPubMed
Stone, J. & Hoffmann, K.-P. (1971). Conduction velocity as a parameter in the organization of the afferent relay in the cat's lateral geniculate nucleus. Brain Research 32, 454–459.CrossRefGoogle ScholarPubMed
Ten Hoopen, M. (1966). Multimodal interval distributions. Kybernetik 3, 17–24.CrossRefGoogle ScholarPubMed
van de Grind, W.A., Koenderink, J.J., van der Heyde, G.L., Landman, H.A.A. & Bouman, M.A. (1971). Adapting coincidence scalers and neural modeling studies of vision. Kybernetik 8, 85–105.CrossRefGoogle ScholarPubMed
Von der Malsburg, C. (1981). The correlation theory of brain function. Internal Report 81–2, Department of Neurobiology MPI Biophysysic und Chemie. Göttingen: Germany.Google Scholar
Von der Malsburg, C. & Schneider, W. (1986). A neural cocktail-party processor. Biological Cybernetics 54, 29–40.CrossRefGoogle ScholarPubMed
Wörgötter, F. & Eysel, U.Th. (1988). A simple glass-coated, fire polished tungsten electrode with conductance adjustment using hydrofluoridic acid. Journal of Neuroscience Methods 25, 135–138.CrossRefGoogle ScholarPubMed