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Attractor dynamics of network UP states in the neocortex

Author

Listed:
  • Rosa Cossart

    (Columbia University)

  • Dmitriy Aronov

    (Columbia University)

  • Rafael Yuste

    (Columbia University)

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.

Suggested Citation

  • Rosa Cossart & Dmitriy Aronov & Rafael Yuste, 2003. "Attractor dynamics of network UP states in the neocortex," Nature, Nature, vol. 423(6937), pages 283-288, May.
  • Handle: RePEc:nat:nature:v:423:y:2003:i:6937:d:10.1038_nature01614
    DOI: 10.1038/nature01614
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    Citations

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    Cited by:

    1. Emili Balaguer-Ballester & Christopher C Lapish & Jeremy K Seamans & Daniel Durstewitz, 2011. "Attracting Dynamics of Frontal Cortex Ensembles during Memory-Guided Decision-Making," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-19, May.
    2. Suchin S Gururangan & Alexander J Sadovsky & Jason N MacLean, 2014. "Analysis of Graph Invariants in Functional Neocortical Circuitry Reveals Generalized Features Common to Three Areas of Sensory Cortex," PLOS Computational Biology, Public Library of Science, vol. 10(7), pages 1-12, July.
    3. Milena Raffi & Ralph M Siegel, 2007. "A Functional Architecture of Optic Flow in the Inferior Parietal Lobule of the Behaving Monkey," PLOS ONE, Public Library of Science, vol. 2(2), pages 1-19, February.
    4. Roberto F Galán, 2008. "On How Network Architecture Determines the Dominant Patterns of Spontaneous Neural Activity," PLOS ONE, Public Library of Science, vol. 3(5), pages 1-10, May.
    5. Andreas Steimer & Kaspar Schindler, 2015. "Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-26, July.
    6. Sreedhar S Kumar & Jan Wülfing & Samora Okujeni & Joschka Boedecker & Martin Riedmiller & Ulrich Egert, 2016. "Autonomous Optimization of Targeted Stimulation of Neuronal Networks," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-22, August.
    7. Martinez-Saito, Mario, 2022. "Discrete scaling and criticality in a chain of adaptive excitable integrators," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    8. Jesús Pérez-Ortega & Alejandro Akrouh & Rafael Yuste, 2024. "Stimulus encoding by specific inactivation of cortical neurons," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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