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 the connectome to brain function

In this Historical Perspective, we ask what information is needed beyond connectivity diagrams to understand the function of nervous systems. Informed by invertebrate circuits whose connectivities are known, we highlight the importance of neuronal dynamics and neuromodulation, and the existence of parallel circuits. The vertebrate retina has these features in common with invertebrate circuits, suggesting that they are general across animals. Comparisons across these systems suggest approaches to study the functional organization of large circuits based on existing knowledge of small circuits.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Connectivity of two well-studied invertebrate circuits.
Figure 2: C. elegans neurons essential for avoidance of light touch.
Figure 3: Similar changes in circuit dynamics can arise from three entirely different circuit mechanisms.
Figure 4: Two views of a multifunctional C. elegans circuit.
Figure 5: Changing either intrinsic neuronal properties or synaptic properties can alter network function.

References

  1. 1

    Bock, D.D. et al. Network anatomy and in vivo physiology of visual cortical neurons. Nature 471, 177–182 (2011).

    CAS  Article  Google Scholar 

  2. 2

    Briggman, K.L. & Bock, D.D. Volume electron microscopy for neuronal circuit reconstruction. Curr. Opin. Neurobiol. 22, 154–161 (2012).

    CAS  Article  Google Scholar 

  3. 3

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

    CAS  Article  Google Scholar 

  4. 4

    Kleinfeld, D. et al. Large-scale automated histology in the pursuit of connectomes. J. Neurosci. 31, 16125–16138 (2011).

    CAS  Article  Google Scholar 

  5. 5

    Burrows, M. Monosynaptic connexions between wing stretch receptors and flight motoneurones of the locust. J. Exp. Biol. 62, 189–219 (1975).

    CAS  PubMed  Google Scholar 

  6. 6

    Fentress, J.C. Simpler Networks and Behavior (Sinauer Associates, 1976).

  7. 7

    Getting, P.A., Lennard, P.R. & Hume, R.I. Central pattern generator mediating swimming in Tritonia. I. Identification and synaptic interactions. J. Neurophysiol. 44, 151–164 (1980).

    CAS  Article  Google Scholar 

  8. 8

    Stretton, A.O. & Kravitz, E.A. Neuronal geometry: determination with a technique of intracellular dye injection. Science 162, 132–134 (1968).

    CAS  Article  Google Scholar 

  9. 9

    Remler, M., Selverston, A. & Kennedy, D. Lateral giant fibers of cray fish: location of somata by dye injection. Science 162, 281–283 (1968).

    CAS  Article  Google Scholar 

  10. 10

    Goodman, C.S. Isogenic grasshoppers: genetic variability in the morphology of identified neurons. J. Comp. Neurol. 182, 681–705 (1978).

    CAS  Article  Google Scholar 

  11. 11

    Maynard, E.A. Electron microscopy of the stomatogastric ganglion in the lobster, Homarus americanus. Tissue Cell 3, 137–160 (1971).

    CAS  Article  Google Scholar 

  12. 12

    King, D.G. Organization of crustacean neuropil. I. Patterns of synaptic connections in lobster stomatogastric ganglion. J. Neurocytol. 5, 207–237 (1976).

    CAS  Article  Google Scholar 

  13. 13

    King, D.G. Organization of crustacean neuropil. II. Distribution of synaptic contacts on identified motor neurons in lobster stomatogastric ganglion. J. Neurocytol. 5, 239–266 (1976).

    CAS  Article  Google Scholar 

  14. 14

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

    CAS  Article  Google Scholar 

  15. 15

    Seung, H.S. Reading the book of memory: sparse sampling versus dense mapping of connectomes. Neuron 62, 17–29 (2009).

    CAS  Article  Google Scholar 

  16. 16

    Meinertzhagen, I.A. & Lee, C.H. The genetic analysis of functional connectomics in Drosophila. Adv. Genet. 80, 99–151 (2012).

    Article  Google Scholar 

  17. 17

    Anderson, J.R. et al. Exploring the retinal connectome. Mol. Vis. 17, 355–379 (2011).

    PubMed  PubMed Central  Google Scholar 

  18. 18

    Lu, J., Tapia, J.C., White, O.L. & Lichtman, J.W. The interscutularis muscle connectome. PLoS Biol. 7, e32 (2009).

    Google Scholar 

  19. 19

    Marder, E. & Bucher, D. Understanding circuit dynamics using the stomatogastric nervous system of lobsters and crabs. Annu. Rev. Physiol. 69, 291–316 (2007).

    CAS  Article  Google Scholar 

  20. 20

    Nusbaum, M.P. & Beenhakker, M.P. A small-systems approach to motor pattern generation. Nature 417, 343–350 (2002).

    CAS  Article  Google Scholar 

  21. 21

    Thirumalai, V., Prinz, A.A., Johnson, C.D. & Marder, E. Red pigment concentrating hormone strongly enhances the strength of the feedback to the pyloric rhythm oscillator but has little effect on pyloric rhythm period. J. Neurophysiol. 95, 1762–1770 (2006).

    CAS  Article  Google Scholar 

  22. 22

    Chalfie, M. et al. The neural circuit for touch sensitivity in Caenorhabditis elegans. J. Neurosci. 5, 956–964 (1985).

    CAS  Article  Google Scholar 

  23. 23

    Gutierrez, G.J., O'Leary, T. & Marder, E. Multiple mechanisms switch an electrically coupled, synaptically inhibited neuron between competing rhythmic oscillators. Neuron 77, 845–858 (2013).

    CAS  Article  Google Scholar 

  24. 24

    Sharp, A.A., Skinner, F.K. & Marder, E. Mechanisms of oscillation in dynamic clamp constructed two-cell half-center circuits. J. Neurophysiol. 76, 867–883 (1996).

    CAS  Article  Google Scholar 

  25. 25

    Jang, H. et al. Neuromodulatory state and sex specify alternative behaviors through antagonistic synaptic pathways in C. elegans. Neuron 75, 585–592 (2012).

    CAS  Article  Google Scholar 

  26. 26

    Pereda, A.E. et al. Gap junction-mediated electrical transmission: regulatory mechanisms and plasticity. Biochim. Biophys. Acta 1828, 134–146 (2013).

    CAS  Article  Google Scholar 

  27. 27

    Neyton, J. & Trautmann, A. Physiological modulation of gap junction permeability. J. Exp. Biol. 124, 93–114 (1986).

    Google Scholar 

  28. 28

    Guo, Y.M. et al. Imaging dynamic cell-cell junctional coupling in vivo using Trojan-LAMP. Nat. Methods 5, 835–841 (2008).

    CAS  Article  Google Scholar 

  29. 29

    Marder, E. Neuromodulation of neuronal circuits: back to the future. Neuron 76, 1–11 (2012).

    CAS  Article  Google Scholar 

  30. 30

    Macosko, E.Z. et al. A hub-and-spoke circuit drives pheromone attraction and social behaviour in C. elegans. Nature 458, 1171–1175 (2009).

    CAS  Article  Google Scholar 

  31. 31

    Inagaki, H.K. et al. Visualizing neuromodulation in vivo: TANGO-mapping of dopamine signaling reveals appetite control of sugar sensing. Cell 148, 583–595 (2012).

    CAS  Article  Google Scholar 

  32. 32

    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).

    CAS  Article  Google Scholar 

  33. 33

    Kawaguchi, Y. & Kondo, S. Parvalbumin, somatostatin and cholecystokinin as chemical markers for specific GABAergic interneuron types in the rat frontal cortex. J. Neurocytol. 31, 277–287 (2002).

    Article  Google Scholar 

  34. 34

    Egorov, A.V., Hamam, B.N., Fransen, E., Hasselmo, M.E. & Alonso, A.A. Graded persistent activity in entorhinal cortex neurons. Nature 420, 173–178 (2002).

    CAS  Article  Google Scholar 

  35. 35

    Aston-Jones, G. & Cohen, J.D. An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. Annu. Rev. Neurosci. 28, 403–450 (2005).

    CAS  Article  Google Scholar 

  36. 36

    Zucker, R.S. & Regehr, W.G. Short-term synaptic plasticity. Annu. Rev. Physiol. 64, 355–405 (2002).

    CAS  Article  Google Scholar 

  37. 37

    Masland, R.H. The neuronal organization of the retina. Neuron 76, 266–280 (2012).

    CAS  Article  Google Scholar 

  38. 38

    Grimes, W.N., Zhang, J., Graydon, C.W., Kachar, B. & Diamond, J.S. Retinal parallel processors: more than 100 independent microcircuits operate within a single interneuron. Neuron 65, 873–885 (2010).

    CAS  Article  Google Scholar 

  39. 39

    Hosoya, T., Baccus, S.A. & Meister, M. Dynamic predictive coding by the retina. Nature 436, 71–77 (2005).

    CAS  Article  Google Scholar 

  40. 40

    Farrow, K. et al. Ambient illumination toggles a neuronal circuit switch in the retina and visual perception at cone threshold. Neuron 78, 325–338 (2013).

    CAS  Article  Google Scholar 

  41. 41

    Rivlin-Etzion, M., Wei, W. & Feller, M.B. Visual stimulation reverses the directional preference of direction-selective retinal ganglion cells. Neuron 76, 518–525 (2012).

    CAS  Article  Google Scholar 

  42. 42

    Witkovsky, P. Dopamine and retinal function. Doc. Ophthalmol. 108, 17–40 (2004).

    Article  Google Scholar 

  43. 43

    Ciocchi, S. et al. Encoding of conditioned fear in central amygdala inhibitory circuits. Nature 468, 277–282 (2010).

    CAS  Article  Google Scholar 

  44. 44

    Marder, E. & Taylor, A.L. Multiple models to capture the variability in biological neurons and networks. Nat. Neurosci. 14, 133–138 (2011).

    CAS  Article  Google Scholar 

  45. 45

    Milo, R. et al. Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002).

    CAS  Article  Google Scholar 

  46. 46

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

    CAS  Article  Google Scholar 

  47. 47

    Koch, C. & Reid, R.C. Neuroscience: Observatories of the mind. Nature 483, 397–398 (2012).

    CAS  Article  Google Scholar 

  48. 48

    Alivisatos, A.P. et al. The brain activity map project and the challenge of functional connectomics. Neuron 74, 970–974 (2012).

    CAS  Article  Google Scholar 

  49. 49

    Koch, C. Systems biology. Modular biological complexity. Science 337, 531–532 (2012).

    CAS  Article  Google Scholar 

  50. 50

    Varshney, L.R., Chen, B.L., Paniagua, E., Hall, D.H. & Chklovskii, D.B. Structural properties of the Caenorhabditis elegans neuronal network. PLoS Comput. Biol. 7, e1001066 (2011).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank M. Meister for sharing his knowledge of the retina. C.I.B. is funded by the Howard Hughes Medical Institute. Research in the Marder laboratory relevant to this piece is funded by the US National Institutes of Health (NS17813, NS 81013 and MH46742).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Cornelia I Bargmann.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Bargmann, C., Marder, E. From the connectome to brain function. Nat Methods 10, 483–490 (2013). https://doi.org/10.1038/nmeth.2451

Download citation

Further reading

Search

Quick links