Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Disentangling the functional consequences of the connectivity between optic-flow processing neurons

Nature Neuroscience volume 15, pages 441–448 (2012)Cite this article

Subjects

Abstract

Typically, neurons in sensory areas are highly interconnected. Coupling two neurons can synchronize their activity and affect a variety of single-cell properties, such as their stimulus tuning, firing rate or gain. All of these factors must be considered to understand how two neurons should be coupled to optimally process stimuli. We quantified the functional effect of an interaction between two optic-flow processing neurons (Vi and H1) in the fly (Lucilia sericata). Using a generative model, we estimated a uni-directional coupling from H1 to Vi. Especially at a low signal-to-noise ratio (SNR), the coupling strongly improved the information about optic-flow in Vi. We identified two constraints confining the strength of the interaction. First, for weak couplings, Vi benefited from inputs by H1 without a concomitant shift of its stimulus tuning. Second, at both low and high SNR, the coupling strength lay in a range in which the information carried by single spikes is optimal.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: The responses of the left Vi and right H1 cells are correlated.
Figure 2: Random rotation stimulus.
Figure 3: Generative model for Vi and H1.
Figure 4: GLM components.
Figure 5: H1 enhances the amplitude of Vi's rotation tuning without affecting the tuning shape.
Figure 6: The coupling improves Vi's stimulus representation.
Figure 7: Effect of a fictive coupling from Vi to H1.

Similar content being viewed by others

References

  1. Gilbert, C.D. & Wiesel, T.N. Intrinsic connectivity and receptive field properties in visual cortex. Vision Res. 25, 365–374 (1985).

    Article  CAS  Google Scholar 

  2. DeVries, S.H. & Baylor, D.A. Synaptic circuitry of the retina and olfactory bulb. Cell 72 (suppl.) 139–149 (1993).

    Article  Google Scholar 

  3. Nassi, J.J. & Callaway, E.M. Parallel processing strategies of the primate visual system. Nat. Rev. Neurosci. 10, 360–372 (2009).

    Article  CAS  Google Scholar 

  4. Borst, A., Haag, J. & Reiff, D.F. Fly motion vision. Annu. Rev. Neurosci. 33, 49–70 (2010).

    Article  CAS  Google Scholar 

  5. Read, H.L., Winer, J.A. & Schreiner, C.E. Functional architecture of auditory cortex. Curr. Opin. Neurobiol. 12, 433–440 (2002).

    Article  CAS  Google Scholar 

  6. Averbeck, B.B., Latham, P.E. & Pouget, A. Neural correlations, population coding and computation. Nat. Rev. Neurosci. 7, 358–366 (2006).

    Article  CAS  Google Scholar 

  7. Usrey, W.M. & Reid, R.C. Synchronous activity in the visual system. Annu. Rev. Physiol. 61, 435–456 (1999).

    Article  CAS  Google Scholar 

  8. Tkacik, G., Prentice, J.S., Balasubramanian, V. & Schneidman, E. Optimal population coding by noisy spiking neurons. Proc. Natl. Acad. Sci. USA 107, 14419–14424 (2010).

    Article  CAS  Google Scholar 

  9. Farrow, K., Borst, A. & Haag, J. Sharing receptive fields with your neighbors: tuning the vertical system cells to wide field motion. J. Neurosci. 25, 3985–3993 (2005).

    Article  CAS  Google Scholar 

  10. Olsen, S.R., Bhandawat, V. & Wilson, R.I. Excitatory interactions between olfactory processing channels in the Drosophila antennal lobe. Neuron 54, 89–103 (2007).

    Article  CAS  Google Scholar 

  11. Olsen, S.R. & Wilson, R.I. Lateral presynaptic inhibition mediates gain control in an olfactory circuit. Nature 452, 956–960 (2008).

    Article  CAS  Google Scholar 

  12. Cafaro, J. & Rieke, F. Noise correlations improve response fidelity and stimulus encoding. Nature 468, 964–967 (2010).

    Article  CAS  Google Scholar 

  13. Hausen, K. The lobula-complex of the fly: structure, function and significance in visual behavior. in Photoreception and Vision in Invertebrates (ed. M.A. Ali) 523–559 (Plenum Press, New York, 1984).

  14. Krapp, H.G. & Hengstenberg, R. Estimation of self-motion by optic flow processing in single visual interneurons. Nature 384, 463–466 (1996).

    Article  CAS  Google Scholar 

  15. Borst, A. & Weber, F. Neural action fields for optic flow based navigation: a simulation study of the fly lobula plate network. PLoS ONE 6, e16303 (2011).

    Article  CAS  Google Scholar 

  16. Haag, J. & Borst, A. Neural mechanism underlying complex receptive field properties of motion-sensitive interneurons. Nat. Neurosci. 7, 628–634 (2004).

    Article  CAS  Google Scholar 

  17. Pillow, J.W. et al. Spatio-temporal correlations and visual signaling in a complete neuronal population. Nature 454, 995–999 (2008).

    Article  CAS  Google Scholar 

  18. Okatan, M., Wilson, M.A. & Brown, E.N. Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity. Neural Comput. 17, 1927–1961 (2005).

    Article  Google Scholar 

  19. Paninski, L., Pillow, J. & Lewi, J. Statistical models for neural encoding, decoding, and optimal stimulus design. Prog. Brain Res. 165, 493–507 (2007).

    Article  Google Scholar 

  20. Beersma, D.G.M., Stavenga, D.G. & Kuiper, J.W. Retinal lattice, visual field and binocularities in flies. J. Comp. Physiol. [A] 119, 207–220 (1977).

    Article  Google Scholar 

  21. Haag, J. & Borst, A. Reciprocal inhibitory connections within a neural network for rotational optic-flow processing. Front. Neurosci. 1, 111–121 (2007).

    Article  Google Scholar 

  22. Haag, J. & Borst, A. Dendro-dendritic interactions between motion-sensitive large-field neurons in the fly. J. Neurosci. 22, 3227–3233 (2002).

    Article  CAS  Google Scholar 

  23. Haag, J. & Borst, A. Orientation tuning of motion-sensitive neurons shaped by vertical-horizontal network interactions. J. Comp. Physiol. [A] 189, 363–370 (2003).

    CAS  Google Scholar 

  24. Borst, A. & Theunissen, F.E. Information theory and neural coding. Nat. Neurosci. 2, 947–957 (1999).

    Article  CAS  Google Scholar 

  25. Reichardt, W. Autocorrelation, a principle for the evaluation of sensory information by the central nervous system. in Principles of Sensory Communication (ed. W.A. Rosenblith) 303–317 (MIT Press and John Wiley & Sons, 1961).

  26. Haag, J., Denk, W. & Borst, A. Fly motion vision is based on Reichardt detectors regardless of the signal-to-noise ratio. Proc. Natl. Acad. Sci. USA 101, 16333–16338 (2004).

    Article  CAS  Google Scholar 

  27. Gerwinn, S., Macke, J.H. & Bethge, M. Bayesian inference for generalized linear models for spiking neurons. Front. Comput. Neurosci. 4, 12 (2010).

    Article  Google Scholar 

  28. Weber, F., Machens, C.K. & Borst, A. Spatiotemporal response properties of optic-flow processing neurons. Neuron 67, 629–642 (2010).

    Article  CAS  Google Scholar 

  29. Rieke, F., Bialek, W. & Warland, D. Spikes (Mit Press, 1999).

  30. Machens, C.K. et al. Representation of acoustic communication signals by insect auditory receptor neurons. J. Neurosci. 21, 3215–3227 (2001).

    Article  CAS  Google Scholar 

  31. Bialek, W., Rieke, F., de Ruyter van Steveninck, R. & Warland, D. Reading a neural code. Science 252, 1854–1857 (1991).

    Article  CAS  Google Scholar 

  32. Bair, W., Zohary, E. & Newsome, W.T. Correlated firing in macaque visual area MT: time scales and relationship to behavior. J. Neurosci. 21, 1676–1697 (2001).

    Article  CAS  Google Scholar 

  33. Zohary, E., Shadlen, M.N. & Newsome, W.T. Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370, 140–143 (1994).

    Article  CAS  Google Scholar 

  34. Moore, G.P., Segundo, J.P., Perkel, D.H. & Levitan, H. Statistical signs of synaptic interaction in neurons. Biophys. J. 10, 876–900 (1970).

    Article  CAS  Google Scholar 

  35. Kohn, A. & Smith, M.A. Stimulus dependence of neuronal correlation in primary visual cortex of the macaque. J. Neurosci. 25, 3661–3673 (2005).

    Article  CAS  Google Scholar 

  36. Smith, M.A. & Kohn, A. Spatial and temporal scales of neuronal correlation in primary visual cortex. J. Neurosci. 28, 12591–12603 (2008).

    Article  CAS  Google Scholar 

  37. Takeuchi, D., Hirabayashi, T., Tamura, K. & Miyashita, Y. Reversal of interlaminar signal between sensory and memory processing in monkey temporal cortex. Science 331, 1443–1447 (2011).

    Article  CAS  Google Scholar 

  38. Brody, C.D. Correlations without synchrony. Neural Comput. 11, 1537–1551 (1999).

    Article  CAS  Google Scholar 

  39. Cohen, M.R. & Kohn, A. Measuring and interpreting neuronal correlations. Nat. Neurosci. 14, 811–819 (2011).

    Article  CAS  Google Scholar 

  40. 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 

  41. Cohen, M.R. & Maunsell, J.H.R. Attention improves performance primarily by reducing interneuronal correlations. Nat. Neurosci. 12, 1594–1600 (2009).

    Article  CAS  Google Scholar 

  42. Schneidman, E., Berry, M.J., Segev, R. & Bialek, W. Weak pairwise correlations imply strongly correlated network states in a neural population. Nature 440, 1007–1012 (2006).

    Article  CAS  Google Scholar 

  43. Shlens, J. et al. The structure of multi-neuron firing patterns in primate retina. J. Neurosci. 26, 8254–8266 (2006).

    Article  CAS  Google Scholar 

  44. Stevenson, I.H. & Kording, K.P. How advances in neural recording affect data analysis. Nat. Neurosci. 14, 139–142 (2011).

    Article  CAS  Google Scholar 

  45. Nauhaus, I., Busse, L., Carandini, M. & Ringach, D.L. Stimulus contrast modulates functional connectivity in visual cortex. Nat. Neurosci. 12, 70–76 (2009).

    Article  CAS  Google Scholar 

  46. Schilstra, C. & Hateren, J.H. Blowfly flight and optic flow. I. Thorax kinematics and flight dynamics. J. Exp. Biol. 202, 1481–1490 (1999).

    PubMed  Google Scholar 

  47. Ko, H. et al. Functional specificity of local synaptic connections in neocortical networks. Nature 473, 87–91 (2011).

    Article  CAS  Google Scholar 

  48. Elyada, Y.M., Haag, J. & Borst, A. Different receptive fields in axons and dendrites underlie robust coding in motion-sensitive neurons. Nat. Neurosci. 12, 327–332 (2009).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank E. Schneidman and G. Tkacik for important discussions during the Methods in Computational Neuroscience course in Woods Hole, Massachusetts. We thank M. Walter for improving the functionality of the LED arena, F. Schwarz for help with the experiments and H. Eichner for critically reading the manuscript. F.W. is supported by a grant from the Deutsche Forschungsgemeinschaft (DFG, Research Training Group 1091) and the Max Planck Society. C.K.M. is funded through the Emmy-Noether program of the DFG and a “Chaire d'excellence” of the Agence National de la Recherche.

Author information

Author notes

  1. Christian K. Machens

    Present address: Present address: Champalimaud Neuroscience Program, Champalimaud Foundation, Lisbon, Portugal.,

Authors and Affiliations

  1. Department of Systems and Computational Neurobiology, Max Planck Institute of Neurobiology, Martinsried, Germany

    Franz Weber & Alexander Borst

  2. Graduate School of Systemic Neurosciences, Ludwig Maximilians University, Munich, Germany

    Franz Weber

  3. Département d'Études Cognitives, Group for Neural Theory, INSERM Unité 960, École Normal Supérieure, Paris, France

    Christian K. Machens

Authors
  1. Franz Weber
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christian K. Machens
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexander Borst
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

F.W., C.K.M. and A.B. designed the study. F.W. performed all of the experiments and analyzed the data. F.W., C.K.M. and A.B. wrote the manuscript.

Corresponding author

Correspondence to Franz Weber.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 and Supplementary Results (PDF 466 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Weber, F., Machens, C. & Borst, A. Disentangling the functional consequences of the connectivity between optic-flow processing neurons. Nat Neurosci 15, 441–448 (2012). https://doi.org/10.1038/nn.3044

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.3044

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing