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Sparse synaptic connectivity is required for decorrelation and pattern separation in feedforward networks

Author

Listed:
  • N. Alex Cayco-Gajic

    (University College London)

  • Claudia Clopath

    (Imperial College London)

  • R. Angus Silver

    (University College London)

Abstract

Pattern separation is a fundamental function of the brain. The divergent feedforward networks thought to underlie this computation are widespread, yet exhibit remarkably similar sparse synaptic connectivity. Marr-Albus theory postulates that such networks separate overlapping activity patterns by mapping them onto larger numbers of sparsely active neurons. But spatial correlations in synaptic input and those introduced by network connectivity are likely to compromise performance. To investigate the structural and functional determinants of pattern separation we built models of the cerebellar input layer with spatially correlated input patterns, and systematically varied their synaptic connectivity. Performance was quantified by the learning speed of a classifier trained on either the input or output patterns. Our results show that sparse synaptic connectivity is essential for separating spatially correlated input patterns over a wide range of network activity, and that expansion and correlations, rather than sparse activity, are the major determinants of pattern separation.

Suggested Citation

  • N. Alex Cayco-Gajic & Claudia Clopath & R. Angus Silver, 2017. "Sparse synaptic connectivity is required for decorrelation and pattern separation in feedforward networks," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-01109-y
    DOI: 10.1038/s41467-017-01109-y
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    Cited by:

    1. Stefano Recanatesi & Gabriel Koch Ocker & Michael A Buice & Eric Shea-Brown, 2019. "Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-29, July.
    2. Louis Kang & Taro Toyoizumi, 2024. "Distinguishing examples while building concepts in hippocampal and artificial networks," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    3. Ting-Feng Lin & Silas E. Busch & Christian Hansel, 2024. "Intrinsic and synaptic determinants of receptive field plasticity in Purkinje cells of the mouse cerebellum," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    4. A. Barri & M. T. Wiechert & M. Jazayeri & D. A. DiGregorio, 2022. "Synaptic basis of a sub-second representation of time in a neural circuit model," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    5. Daniel Müller-Komorowska & Baris Kuru & Heinz Beck & Oliver Braganza, 2023. "Phase information is conserved in sparse, synchronous population-rate-codes via phase-to-rate recoding," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    6. Zhiwei Xu & Erez Geron & Luis M. Pérez-Cuesta & Yang Bai & Wen-Biao Gan, 2023. "Generalized extinction of fear memory depends on co-allocation of synaptic plasticity in dendrites," Nature Communications, Nature, vol. 14(1), pages 1-17, December.

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