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Functional specificity of local synaptic connections in neocortical networks

Author

Listed:
  • Ho Ko

    (Physiology and Pharmacology, University College London, 21 University Street)

  • Sonja B. Hofer

    (Physiology and Pharmacology, University College London, 21 University Street)

  • Bruno Pichler

    (Physiology and Pharmacology, University College London, 21 University Street
    Present address: MRC National Institute for Medical Research, The Ridgeway, Mill Hill, London NW7 1AA, UK.)

  • Katherine A. Buchanan

    (Physiology and Pharmacology, University College London, 21 University Street)

  • P. Jesper Sjöström

    (Physiology and Pharmacology, University College London, 21 University Street)

  • Thomas D. Mrsic-Flogel

    (Physiology and Pharmacology, University College London, 21 University Street)

Abstract

Making sense of connectivity In the sensory cortex, neurons are densely interconnected, but the logic by which functionally similar and dissimilar neurons are wired together is an open question. This technical tour de force combines in vivo two-photon calcium imaging and simultaneous whole-cell recording of multiple cells to show that neurons with similar stimulus preferences connect at higher rates than those with dissimilar preferences. This points to the existence of fine-scale sub-networks dedicated to processing related sensory information. Application of this new technique more widely should reveal more about how circuits supporting different sensory or motor functions are constructed in the brain.

Suggested Citation

  • Ho Ko & Sonja B. Hofer & Bruno Pichler & Katherine A. Buchanan & P. Jesper Sjöström & Thomas D. Mrsic-Flogel, 2011. "Functional specificity of local synaptic connections in neocortical networks," Nature, Nature, vol. 473(7345), pages 87-91, May.
    • Handle: RePEc:nat:nature:v:473:y:2011:i:7345:d:10.1038_nature09880
    DOI: 10.1038/nature09880
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    Cited by:

    1. Dimitri Yatsenko & Krešimir Josić & Alexander S Ecker & Emmanouil Froudarakis & R James Cotton & Andreas S Tolias, 2015. "Improved Estimation and Interpretation of Correlations in Neural Circuits," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-28, March.
    2. Bettina Voelcker & Ravi Pancholi & Simon Peron, 2022. "Transformation of primary sensory cortical representations from layer 4 to layer 2," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Gabriel Koch Ocker & Ashok Litwin-Kumar & Brent Doiron, 2015. "Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-40, August.
    4. Markus Helmer & Vladislav Kozyrev & Valeska Stephan & Stefan Treue & Theo Geisel & Demian Battaglia, 2016. "Model-Free Estimation of Tuning Curves and Their Attentional Modulation, Based on Sparse and Noisy Data," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-33, January.
    5. Ryan C Williamson & Benjamin R Cowley & Ashok Litwin-Kumar & Brent Doiron & Adam Kohn & Matthew A Smith & Byron M Yu, 2016. "Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-27, December.
    6. Jeyadarshan Jeyabalaratnam & Vishal Bharmauria & Lyes Bachatene & Sarah Cattan & Annie Angers & Stéphane Molotchnikoff, 2013. "Adaptation Shifts Preferred Orientation of Tuning Curve in the Mouse Visual Cortex," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-8, May.
    7. Sadra Sadeh & Stefan Rotter, 2015. "Orientation Selectivity in Inhibition-Dominated Networks of Spiking Neurons: Effect of Single Neuron Properties and Network Dynamics," PLOS Computational Biology, Public Library of Science, vol. 11(1), pages 1-17, January.
    8. Thaddeus R Cybulski & Edward S Boyden & George M Church & Keith E J Tyo & Konrad P Kording, 2017. "Nucleotide-time alignment for molecular recorders," PLOS Computational Biology, Public Library of Science, vol. 13(5), pages 1-22, May.
    9. Christopher Ebsch & Robert Rosenbaum, 2018. "Imbalanced amplification: A mechanism of amplification and suppression from local imbalance of excitation and inhibition in cortical circuits," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-28, March.
    10. Matteo Farinella & Daniel T Ruedt & Padraig Gleeson & Frederic Lanore & R Angus Silver, 2014. "Glutamate-Bound NMDARs Arising from In Vivo-like Network Activity Extend Spatio-temporal Integration in a L5 Cortical Pyramidal Cell Model," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-21, April.
    11. 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.
    12. Takafumi Arakaki & G Barello & Yashar Ahmadian, 2019. "Inferring neural circuit structure from datasets of heterogeneous tuning curves," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-38, April.
    13. Pierre Yger & Kenneth D Harris, 2013. "The Convallis Rule for Unsupervised Learning in Cortical Networks," PLOS Computational Biology, Public Library of Science, vol. 9(10), pages 1-16, October.
    14. Molan Li & Da Li & Junxing Zhang & Xuanlu Xiang & Di Zhao, 2023. "Dynamics of Optimal Cue Integration with Time-Varying Delay in the Insects’ Navigation System," Mathematics, MDPI, vol. 11(17), pages 1-17, August.
    15. Bartul Mimica & Tuçe Tombaz & Claudia Battistin & Jingyi Guo Fuglstad & Benjamin A. Dunn & Jonathan R. Whitlock, 2023. "Behavioral decomposition reveals rich encoding structure employed across neocortex in rats," Nature Communications, Nature, vol. 14(1), pages 1-20, December.

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