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Attractor dynamics of network UP states in the neocortex

Abstract

The cerebral cortex receives input from lower brain regions, and its function is traditionally considered to be processing that input through successive stages to reach an appropriate output1,2. However, the cortical circuit contains many interconnections, including those feeding back from higher centres3,4,5,6, and is continuously active even in the absence of sensory inputs7,8,9. Such spontaneous firing has a structure that reflects the coordinated activity of specific groups of neurons10,11,12. Moreover, the membrane potential of cortical neurons fluctuates spontaneously between a resting (DOWN) and a depolarized (UP) state11,13,14,15,16, which may also be coordinated. The elevated firing rate in the UP state follows sensory stimulation16 and provides a substrate for persistent activity, a network state that might mediate working memory17,18,19,20,21. Using two-photon calcium imaging, we reconstructed the dynamics of spontaneous activity of up to 1,400 neurons in slices of mouse visual cortex. Here we report the occurrence of synchronized UP state transitions (‘cortical flashes’) that occur in spatially organized ensembles involving small numbers of neurons. Because of their stereotyped spatiotemporal dynamics, we conclude that network UP states are circuit attractors—emergent features of feedback neural networks22 that could implement memory states or solutions to computational problems.

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Figure 1: Two-photon calcium imaging of persistent activity in large neuronal populations.
Figure 2: Spatial structure of synchronous neuronal ensembles.
Figure 3: Spatiotemporal dynamics of synchronous activity.
Figure 4: Synchronous activity is mediated by network UP states.

References

  1. Mountcastle, V. B. Perceptual Neuroscience: The Cerebral Cortex (Harvard Univ. Press, Cambridge, Massachusetts, 1998)

    Google Scholar 

  2. Hubel, D. H. & Wiesel, T. N. Functional architecture of the macaque monkey visual cortex. Proc. R. Soc. Lond. B 198, 1–59 (1977)

    ADS  CAS  Article  Google Scholar 

  3. Lorente de Nó, R. Analysis of the activity of the chains of internuncial neurons. J. Neurophysiol. 1, 207–244 (1938)

    Article  Google Scholar 

  4. Gilbert, C. & Wiesel, T. N. Morphology and intracortical projections of functionally characterised neurons in the cat visual cortex. Nature 280, 120–125 (1979)

    ADS  CAS  Article  Google Scholar 

  5. Douglas, R. J. & Martin, K. A. C. in The Synaptic Organization of the Brain (ed. Shepherd, G. M.) 459–511 (Oxford Univ. Press, Oxford, 1998)

    Google Scholar 

  6. Somogyi, P., Tamas, G., Lujan, R. & Buhl, E. Salient features of synaptic organisation in the cerebral cortex. Brain Res. Brain Res. Rev. 26, 113–135 (1998)

    CAS  Article  Google Scholar 

  7. Llinás, R. I of the Vortex: From Neurons to Self (MIT Press, Cambridge, Massachusetts, 2002)

    Google Scholar 

  8. Creutzfeldt, O. Cortex Cerebri (Oxford Univ. Press, Oxford, 1995)

    Google Scholar 

  9. Steriade, M., Contreras, D., Curro, D. R. & Nunez, A. The slow (< 1 Hz) oscillation in reticular thalamic and thalamocortical neurons: scenario of sleep rhythm generation in interacting thalamic and neocortical networks. J. Neurosci. 13, 3284–3299 (1993)

    CAS  Article  Google Scholar 

  10. Tsodyks, M., Kenet, T., Grinvald, A. & Arieli, A. Linking spontaneous activity of single cortical neurons and the underlying functional architecture. Science 286, 1943–1946 (1999)

    CAS  Article  Google Scholar 

  11. Sanchez-Vives, M. & McCormick, D. Cellular and network mechanisms of rhythmic recurrent activity in neocortex. Nature Neurosci. 3, 1027–1034 (2000)

    CAS  Article  Google Scholar 

  12. Mao, B. Q., Hamzei-Sichani, F., Aronov, D., Froemke, R. C. & Yuste, R. Dynamics of spontaneous activity in neocortical slices. Neuron 32, 883–898 (2001)

    CAS  Article  Google Scholar 

  13. Wilson, C. J. & Groves, P. M. Spontaneous firing patterns of identified spiny neurons in the rat neostriatum. Brain Res. 220, 67–80 (1981)

    CAS  Article  Google Scholar 

  14. Cowan, R. L. & Wilson, C. J. Spontaneous firing patterns and axonal projections of single corticostriatal neurons in the rat medial agranular cortex. J. Neurophysiol. 71, 17–32 (1994)

    CAS  Article  Google Scholar 

  15. Steriade, M., Nuñez, A. & Amzica, F. A novel slow ( 1 Hz) oscillation of neocortical neurons in vivo: depolarizing and hyperpolarizing components. J. Neurosci. 13, 3252–3265 (1993)

    CAS  Article  Google Scholar 

  16. Anderson, J., Lampl, I., Reichova, I., Carandini, M. & Ferster, D. Stimulus dependence of two-state fluctuations of membrane potential in cat visual cortex. Nature Neurosci. 3, 617–621 (2000)

    CAS  Article  Google Scholar 

  17. Fuster, J. M. The prefrontal cortex and its relation to behavior. Prog. Brain Res. 87, 201–211 (1991)

    CAS  Article  Google Scholar 

  18. Goldman-Rakic, P. S. Cellular basis of working memory. Neuron 14, 477–485 (1995)

    CAS  Article  Google Scholar 

  19. Durstewitz, D., Seamans, J. K. & Sejnowski, T. J. Neurocomputational models of working memory. Nature Neurosci. 3 suppl., 1184–1191 (2000)

    CAS  Article  Google Scholar 

  20. Wang, X. J. Synaptic reverberation underlying mnemonic persistent activity. Trends Neurosci. 24, 455–463 (2001)

    CAS  Article  Google Scholar 

  21. Seung, H. S., Lee, D. D., Reis, B. Y. & Tank, D. W. Stability of the memory of eye position in a recurrent network of conductance-based model neurons. Neuron 26, 259–271 (2000)

    CAS  Article  Google Scholar 

  22. Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982)

    ADS  MathSciNet  CAS  Article  Google Scholar 

  23. Badea, T., Goldberg, J., Mao, B. Q. & Yuste, R. Calcium imaging of epileptiform events with single-cell resolution. J. Neurobiol. 48, 215–227 (2001)

    CAS  Article  Google Scholar 

  24. Shu, Y., Hasenstaub, A. & McCormick, D. A. Turning on and off recurrent balanced cortical activity. Nature 423, 288–293 (2003)

    ADS  CAS  Article  Google Scholar 

  25. Constantinidis, C., Franowicz, M. & Goldman-Rakic, P. Coding specificity in cortical microcircuits: a multiple-electrode analysis of primate prefrontal cortex. J. Neurosci. 21, 3646–3655 (2001)

    CAS  Article  Google Scholar 

  26. Ben-Yishai, R., Lev Bar-Or, R. & Sompolinsky, H. Orientation tuning by recurrent neural networks in visual cortex. Proc. Natl Acad. Sci. USA 92, 3844–3848 (1995)

    ADS  CAS  Article  Google Scholar 

  27. Hebb, D. O. The Organization of Behaviour (Wiley, New York, 1949)

    Google Scholar 

  28. Majewska, A., Yiu, G. & Yuste, R. A custom-made two-photon microscope and deconvolution system. Pflugers Arch. 441, 398–409 (2000)

    CAS  Article  Google Scholar 

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Acknowledgements

We thank D. McCormick for advice as well as J. Hopfield and members of our laboratory for comments. Supported by NEI, NINDS, FRM, the Human Frontier Science Program, the New York STAR Center for High Resolution Imaging of Functional Neural Circuits and the John Merck Fund.

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Correspondence to Rosa Cossart.

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Cossart, R., Aronov, D. & Yuste, R. Attractor dynamics of network UP states in the neocortex. Nature 423, 283–288 (2003). https://doi.org/10.1038/nature01614

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