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Signals and noise in an inhibitory interneuron diverge to control activity in nearby retinal ganglion cells

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Abstract

Information about sensory stimuli is represented by spatiotemporal patterns of neural activity. The complexity of the central nervous system, however, frequently obscures the origin and properties of signals and noise that underlie these activity patterns. We minimized this constraint by examining mechanisms governing correlated activity in mouse retinal ganglion cells (RGCs) under conditions in which light-evoked responses traverse a specific circuit, the rod bipolar pathway. Signals and noise in this circuit produced correlated synaptic input to neighboring On and Off RGCs. Temporal modulation of light intensity did not alter the degree to which noise in the input to nearby RGCs was correlated, and action potential generation in individual RGCs was largely insensitive to differences in network noise generated by dynamic and static light stimuli. Together, these features enable noise in shared circuitry to diminish simultaneous action potential generation in neighboring On and Off RGCs under a variety of conditions.

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Figure 1: Synaptic input to AII amacrine cells and Off-transient RGCs shows similar sensitivity to light stimuli and AMPA- and kainate-receptor antagonists.
Figure 2: Fluctuating light stimuli generate correlations in excitatory and inhibitory synaptic input to neighboring On and Off RGCs.
Figure 3: Correlated noise in network input to nearby RGCs.
Figure 4: Network noise shows similar properties in the presence and absence of fluctuations in light intensity.
Figure 5: Network noise measured in the presence and absence of fluctuations in light intensity affects the temporal precision of action potential generation similarly.
Figure 6: Correlated network noise accentuates differences in action potential generation between neighboring On and Off-transient RGCs.
Figure 7: Schematic representation of input and output properties of AII amacrine cells.

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  • 17 February 2008

    In the version of this article initially published online, the equation in the Methods section was incorrect. The correct equation is shown. The error has been corrected for all versions of the article.

References

  1. Callaway, E.M. Local circuits in primary visual cortex of the macaque monkey. Annu. Rev. Neurosci. 21, 47–74 (1998).

    Article  CAS  Google Scholar 

  2. Dacey, D.M. Parallel pathways for spectral coding in primate retina. Annu. Rev. Neurosci. 23, 743–775 (2000).

    Article  CAS  Google Scholar 

  3. Gegenfurtner, K.R. & Kiper, D.C. Color vision. Annu. Rev. Neurosci. 26, 181–206 (2003).

    Article  CAS  Google Scholar 

  4. Oertel, D. The role of timing in the brain stem auditory nuclei of vertebrates. Annu. Rev. Physiol. 61, 497–519 (1999).

    Article  CAS  Google Scholar 

  5. Field, G.D. & Chichilnisky, E.J. Information processing in the primate retina: circuitry and coding. Annu. Rev. Neurosci. 30, 1–30 (2007).

    Article  CAS  Google Scholar 

  6. Masland, R.H. The fundamental plan of the retina. Nat. Neurosci. 4, 877–886 (2001).

    Article  CAS  Google Scholar 

  7. Wassle, H. Parallel processing in the mammalian retina. Nat. Rev. Neurosci. 5, 747–757 (2004).

    Article  Google Scholar 

  8. Murphy, G.J. & Rieke, F. Network variability limits stimulus-evoked spike timing precision in retinal ganglion cells. Neuron 52, 511–524 (2006).

    Article  CAS  Google Scholar 

  9. Volgyi, B., Deans, M.R., Paul, D.L. & Bloomfield, S.A. Convergence and segregation of the multiple rod pathways in mammalian retina. J. Neurosci. 24, 11182–11192 (2004).

    Article  Google Scholar 

  10. Bloomfield, S.A. & Dacheux, R.F. Rod vision: pathways and processing in the mammalian retina. Prog. Retin. Eye Res. 20, 351–384 (2001).

    Article  CAS  Google Scholar 

  11. Field, G.D., Sampath, A.P. & Rieke, F. Retinal processing near absolute threshold: from behavior to mechanism. Annu. Rev. Physiol. 67, 491–514 (2005).

    Article  CAS  Google Scholar 

  12. Sharpe, L.T. & Stockman, A. Rod pathways: the importance of seeing nothing. Trends Neurosci. 22, 497–504 (1999).

    Article  CAS  Google Scholar 

  13. Dacheux, R.F. & Raviola, E. The rod pathway in the rabbit retina: a depolarizing bipolar and amacrine cell. J. Neurosci. 6, 331–345 (1986).

    Article  CAS  Google Scholar 

  14. Dunn, F.A., Doan, T., Sampath, A.P. & Rieke, F. Controlling the gain of rod-mediated signals in the mammalian retina. J. Neurosci. 26, 3959–3970 (2006).

    Article  CAS  Google Scholar 

  15. Nelson, R. AII amacrine cells quicken time course of rod signals in the cat retina. J. Neurophysiol. 47, 928–947 (1982).

    Article  CAS  Google Scholar 

  16. Pang, J.J., Gao, F. & Wu, S.M. Light-evoked excitatory and inhibitory synaptic inputs to ON and OFF alpha ganglion cells in the mouse retina. J. Neurosci. 23, 6063–6073 (2003).

    Article  CAS  Google Scholar 

  17. Xin, D. & Bloomfield, S.A. Comparison of the responses of AII amacrine cells in the dark- and light-adapted rabbit retina. Vis. Neurosci. 16, 653–665 (1999).

    Article  CAS  Google Scholar 

  18. Chun, M.H., Han, S.H., Chung, J.W. & Wassle, H. Electron microscopic analysis of the rod pathway of the rat retina. J. Comp. Neurol. 332, 421–432 (1993).

    Article  CAS  Google Scholar 

  19. Famiglietti, E.V.J. & Kolb, H. A bistratified amacrine cell and synaptic cirucitry in the inner plexiform layer of the retina. Brain Res. 84, 293–300 (1975).

    Article  Google Scholar 

  20. Kolb, H. The inner plexiform layer in the retina of the cat: electron microscopic observations. J. Neurocytol. 8, 295–329 (1979).

    Article  CAS  Google Scholar 

  21. Kolb, H. & Famiglietti, E.V. Rod and cone pathways in the inner plexiform layer of cat retina. Science 186, 47–49 (1974).

    Article  CAS  Google Scholar 

  22. Strettoi, E., Raviola, E. & Dacheux, R.F. Synaptic connections of the narrow-field, bistratified rod amacrine cell (AII) in the rabbit retina. J. Comp. Neurol. 325, 152–168 (1992).

    Article  CAS  Google Scholar 

  23. Cohen, E.D. Interactions of inhibition and excitation in the light-evoked currents of X type retinal ganglion cells. J. Neurophysiol. 80, 2975–2990 (1998).

    Article  CAS  Google Scholar 

  24. Margolis, D.J. & Detwiler, P.B. Different mechanisms generate maintained activity in ON and OFF retinal ganglion cells. J. Neurosci. 27, 5994–6005 (2007).

    Article  CAS  Google Scholar 

  25. Pang, J.J. et al. Relative contributions of rod and cone bipolar cell inputs to AII amacrine cell light responses in the mouse retina. J. Physiol. (Lond.) 580, 397–410 (2007).

    Article  CAS  Google Scholar 

  26. Trexler, E.B., Li, W. & Massey, S.C. Simultaneous contribution of two rod pathways to AII amacrine and cone bipolar cell light responses. J. Neurophysiol. 93, 1476–1485 (2005).

    Article  Google Scholar 

  27. Tsukamoto, Y. et al. A novel connection between rods and ON cone bipolar cells revealed by ectopic metabotropic glutamate receptor7 (mGluR7) in mGluR6-deficient mouse retinas. J. Neurosci. 27, 6261–6267 (2007).

    Article  CAS  Google Scholar 

  28. Baylor, D.A., Matthews, G. & Yau, K.W. Two components of electrical dark noise in toad retinal rod outer segments. J. Physiol. (Lond.) 309, 591–621 (1980).

    Article  CAS  Google Scholar 

  29. Baylor, D.A., Nunn, B.J. & Schnapf, J.L. The photocurrent, noise and spectral sensitivity of rods of the monkey Macaca fascicularis. J. Physiol. (Lond.) 357, 575–607 (1984).

    Article  CAS  Google Scholar 

  30. Sharp, A.A., O'Neil, M.B., Abbott, L.F. & Marder, E. Dynamic clamp: computer-generated conductances in real neurons. J. Neurophysiol. 69, 992–995 (1993).

    Article  CAS  Google Scholar 

  31. Robinson, H.P. & Kawai, N. Injection of digitally synthesized synaptic conductance transients to measure the integrative properties of neurons. J. Neurosci. Methods 49, 157–165 (1993).

    Article  CAS  Google Scholar 

  32. Victor, J.D. & Purpura, K.P. Nature and precision of temporal coding in visual cortex: a metric-space analysis. J. Neurophysiol. 76, 1310–1326 (1996).

    Article  CAS  Google Scholar 

  33. Buzsaki, G. & Draguhn, A. Neuronal oscillations in cortical networks. Science 304, 1926–1929 (2004).

    Article  CAS  Google Scholar 

  34. Destexhe, A. & Contreras, D. Neuronal computations with stochastic network states. Science 314, 85–90 (2006).

    Article  CAS  Google Scholar 

  35. Somogyi, P. & Klausberger, T. Defined types of cortical interneurone structure space and spike timing in the hippocampus. J. Physiol. (Lond.) 562, 9–26 (2005).

    Article  CAS  Google Scholar 

  36. Callaway, E.M. Feedforward, feedback, and inhibitory connections in primate visual cortex. Neural Netw. 17, 625–632 (2004).

    Article  Google Scholar 

  37. Arnett, D. & Spraker, T.E. Cross-correlation analysis of the maintained discharge of rabbit retinal ganglion cells. J. Physiol. (Lond.) 317, 29–47 (1981).

    Article  CAS  Google Scholar 

  38. Ginsburg, K.S., Johnsen, J.A. & Levine, M.W. Common noise in the firing of neighbouring ganglion cells in goldfish retina. J. Physiol. (Lond.) 351, 433–450 (1984).

    Article  CAS  Google Scholar 

  39. Mastronarde, D.N. Correlated firing of cat retinal ganglion cells. II. Responses of X- and Y-cells to single quantal events. J. Neurophysiol. 49, 325–349 (1983).

    Article  CAS  Google Scholar 

  40. Chance, F.S., Abbott, L.F. & Reyes, A.D. Gain modulation from background synaptic input. Neuron 35, 773–782 (2002).

    Article  CAS  Google Scholar 

  41. de la Rocha, J., Doiron, B., Shea-Brown, E., Josic, K. & Reyes, A. Correlation between neural spike trains increases with firing rate. Nature 448, 802–806 (2007).

    Article  CAS  Google Scholar 

  42. Mitchell, S.J. & Silver, R.A. Shunting inhibition modulates neuronal gain during synaptic excitation. Neuron 38, 433–445 (2003).

    Article  CAS  Google Scholar 

  43. Shu, Y., Hasenstaub, A., Badoual, M., Bal, T. & McCormick, D.A. Barrages of synaptic activity control the gain and sensitivity of cortical neurons. J. Neurosci. 23, 10388–10401 (2003).

    Article  CAS  Google Scholar 

  44. Demb, J.B., Sterling, P. & Freed, M.A. How retinal ganglion cells prevent synaptic noise from reaching the spike output. J. Neurophysiol. 92, 2510–2519 (2004).

    Article  Google Scholar 

  45. Marder, E. & Goaillard, J.M. Variability, compensation and homeostasis in neuron and network function. Nat. Rev. Neurosci. 7, 563–574 (2006).

    Article  CAS  Google Scholar 

  46. Jonas, P., Bischofberger, J. & Sandkuhler, J. Corelease of two fast neurotransmitters at a central synapse. Science 281, 419–424 (1998).

    Article  CAS  Google Scholar 

  47. Protti, D.A., Gerschenfeld, H.M. & Llano, I. GABAergic and glycinergic IPSCs in ganglion cells of rat retinal slices. J. Neurosci. 17, 6075–6085 (1997).

    Article  CAS  Google Scholar 

  48. Press, W.H., Flannery, B.P., Teukolsky, S.A. & Vetterling, W.T. Numerical Recipes in C: The Art of Scientific Computing (Cambridge University Press, Cambridge, UK and New York, 1992).

    Google Scholar 

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Acknowledgements

We thank D. Perkel, K. Briggman, L. Glickfeld and B. Wark for comments on the manuscript and E. Martinson and P. Newman for technical assistance. Support for this research was provided by the Howard Hughes Medical Institute and US National Institutes of Health (EY-11850).

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G.J.M. performed the experiments; G.J.M. and F.R. contributed equally to all other aspects of this work.

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Correspondence to Gabe J Murphy.

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Murphy, G., Rieke, F. Signals and noise in an inhibitory interneuron diverge to control activity in nearby retinal ganglion cells. Nat Neurosci 11, 318–326 (2008). https://doi.org/10.1038/nn2045

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