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.

From circuits to behavior: a bridge too far?

Nature Neuroscience volume 15, pages 507–509 (2012)Cite this article

Subjects

Neuroscience seeks to understand how neural circuits lead to behavior. However, the gap between circuits and behavior is too wide. An intermediate level is one of neural computations, which occur in individual neurons and populations of neurons. Some computations seem to be canonical: repeated and combined in different ways across the brain. To understand neural computations, we must record from a myriad of neurons in multiple brain regions. Understanding computation guides research in the underlying circuits and provides a language for theories of behavior.

One of the fundamental mandates of neuroscience is to reveal how neural circuits lead to perception, thought and, ultimately, behavior. The general public might think that this goal has already been achieved; when they read that a behavior is associated with some part of the brain, they take that statement as an explanation1. But most neuroscientists would agree that, with a few notable exceptions, the relationship between neural circuits and behavior has yet to be established.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Figure 1: Before and after.
Figure 2: Between circuits and behavior: the Marr approach applied to computers and brains.

References

  1. Weisberg, D.S., Keil, F.C., Goodstein, J., Rawson, E. & Gray, J.R. The seductive allure of neuroscience explanations. J. Cogn. Neurosci. 20, 470–477 (2008).

    Article  Google Scholar 

  2. Blakemore, C. The Mind Machine. (BBC Books, 1988).

    Google Scholar 

  3. Anderson, P.W. More is different. Science 177, 393–396 (1972).

    Article  CAS  Google Scholar 

  4. Laughlin, R.B. & Pines, D. The theory of everything. Proc. Natl. Acad. Sci. USA 97, 28–31 (2000).

    Article  CAS  Google Scholar 

  5. Laughlin, R.B., Pines, D., Schmalian, J., Stojkovic, B.P. & Wolynes, P. The middle way. Proc. Natl. Acad. Sci. USA 97, 32–37 (2000).

    Article  CAS  Google Scholar 

  6. Carandini, M. et al. Do we know what the early visual system does? J. Neurosci. 25, 10577–10597 (2005).

    Article  CAS  Google Scholar 

  7. Depireux, D.A., Simon, J.Z., Klein, D.J. & Shamma, S.A. Spectro-temporal response field characterization with dynamic ripples in ferret primary auditory cortex. J. Neurophysiol. 85, 1220–1234 (2001).

    Article  CAS  Google Scholar 

  8. DiCarlo, J.J. & Johnson, K.O. Receptive field structure in cortical area 3b of the alert monkey. Behav. Brain Res. 135, 167–178 (2002).

    Article  Google Scholar 

  9. Bizzi, E., Giszter, S.F., Loeb, E., Mussa-Ivaldi, F.A. & Saltiel, P. Modular organization of motor behavior in the frog's spinal cord. Trends Neurosci. 18, 442–446 (1995).

    Article  CAS  Google Scholar 

  10. Carandini, M. & Heeger, D.J. Normalization as a canonical neural computation. Nat. Rev. Neurosci. 13, 51–62 (2011).

    Article  Google Scholar 

  11. Watson, A.B. & Ahumada, A.J. Jr. A standard model for foveal detection of spatial contrast. J. Vis. 5, 717–740 (2005).

    Article  Google Scholar 

  12. Graham, N.V.S. Visual Pattern Analyzers (Oxford University Press, 1989).

    Book  Google Scholar 

  13. Benucci, A., Ringach, D.L. & Carandini, M. Coding of stimulus sequences by population responses in visual cortex. Nat. Neurosci. 12, 1317–1324 (2009).

    Article  CAS  Google Scholar 

  14. Busse, L., Wade, A.R. & Carandini, M. Representation of concurrent stimuli by population activity in visual cortex. Neuron 64, 931–942 (2009).

    Article  CAS  Google Scholar 

  15. Priebe, N.J. & Ferster, D. Inhibition, spike threshold, and stimulus selectivity in primary visual cortex. Neuron 57, 482–497 (2008).

    Article  CAS  Google Scholar 

  16. Grothe, B. New roles for synaptic inhibition in sound localization. Nat. Rev. Neurosci. 4, 540–550 (2003).

    Article  CAS  Google Scholar 

  17. Koch, C. Biophysics of Computation (Oxford University Press, 1999).

    Google Scholar 

  18. Douglas, R.J., Koch, C., Mahowald, M., Martin, K.A.C. & Suarez, H.H. Recurrent excitation in neocortical circuits. Science 269, 981–985 (1995).

    Article  CAS  Google Scholar 

  19. Marr, D. Vision (W.H. Freeman & Co, 1982).

    Google Scholar 

  20. Buzsáki, G. Large-scale recording of neuronal ensembles. Nat. Neurosci. 7, 446–451 (2004).

    Article  Google Scholar 

  21. Harris, K.D. et al. How do neurons work together? Lessons from auditory cortex. Hear. Res. 271, 37–53 (2010).

    Article  Google Scholar 

  22. Marshel, J.H., Mori, T., Nielsen, K.J. & Callaway, E.M. Targeting single neuronal networks for gene expression and cell labeling in vivo. Neuron 67, 562–574 (2010).

    Article  CAS  Google Scholar 

  23. Seung, H.S. Neuroscience: towards functional connectomics. Nature 471, 170–172 (2011).

    Article  CAS  Google Scholar 

  24. Briggman, K.L., Helmstaedter, M. & Denk, W. Wiring specificity in the direction-selectivity circuit of the retina. Nature 471, 183–188 (2011).

    Article  CAS  Google Scholar 

  25. White, J.G., Southgate, E., Thomson, J.N. & Brenner, S. The structure of the nervous system of the nematode Caenorhabditis elegans. Philos. Trans. R. Soc. Lond. B Biol. Sci. 314, 1–340 (1986).

    Article  CAS  Google Scholar 

  26. Bower, J.M. & Beeman, D. The book of GENESIS (Springer, 1993).

    Google Scholar 

  27. Markram, H. The blue brain project. Nat. Rev. Neurosci. 7, 153–160 (2006).

    Article  CAS  Google Scholar 

  28. Waldrop, M.M. Computer modeling: brain in a box. Nature 482, 456–458 (2012).

    Article  CAS  Google Scholar 

  29. Bower, J.M. in The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System (eds. J.M. Bower & D. Beeman) Ch. 11 (Springer-Verlag, 1998).

    Book  Google Scholar 

  30. Sejnowski, T.J., Churchland, P.S. & Koch, C. Computational neuroscience. Science 241, 1299–1306 (1988).

    Article  CAS  Google Scholar 

  31. Prinz, A.A., Bucher, D. & Marder, E. Similar network activity from disparate circuit parameters. Nat. Neurosci. 7, 1345–1352 (2004).

    Article  CAS  Google Scholar 

  32. Mainen, Z.F. & Sejnowski, T.J. Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382, 363–366 (1996).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Most of these thoughts have arisen from conversations with colleagues, among them D.J. Heeger, J. Schmidhuber, R.I. Wilson, J.A. Movshon and the attendees of the 2009 meeting on Canonical Neural Computation (http://www.carandinilab.net/canonicalneuralcomputation2009). The author's research is supported by the UK Medical Research Council (grant G0800791) and by the European Research Council (project CORTEX). The author holds the GlaxoSmithKline/Fight for Sight Chair in Visual Neuroscience.

Author information

Authors and Affiliations

  1. Matteo Carandini is at University College London, London, UK.,

    Matteo Carandini

Authors
  1. Matteo Carandini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matteo Carandini.

Ethics declarations

Competing interests

The author declares no competing financial interests.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Carandini, M. From circuits to behavior: a bridge too far?. Nat Neurosci 15, 507–509 (2012). https://doi.org/10.1038/nn.3043

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.3043

This article is cited by

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