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Neural heterogeneity promotes robust learning

Author

Listed:
  • Nicolas Perez-Nieves

    (Imperial College London)

  • Vincent C. H. Leung

    (Imperial College London)

  • Pier Luigi Dragotti

    (Imperial College London)

  • Dan F. M. Goodman

    (Imperial College London)

Abstract

The brain is a hugely diverse, heterogeneous structure. Whether or not heterogeneity at the neural level plays a functional role remains unclear, and has been relatively little explored in models which are often highly homogeneous. We compared the performance of spiking neural networks trained to carry out tasks of real-world difficulty, with varying degrees of heterogeneity, and found that heterogeneity substantially improved task performance. Learning with heterogeneity was more stable and robust, particularly for tasks with a rich temporal structure. In addition, the distribution of neuronal parameters in the trained networks is similar to those observed experimentally. We suggest that the heterogeneity observed in the brain may be more than just the byproduct of noisy processes, but rather may serve an active and important role in allowing animals to learn in changing environments.

Suggested Citation

  • Nicolas Perez-Nieves & Vincent C. H. Leung & Pier Luigi Dragotti & Dan F. M. Goodman, 2021. "Neural heterogeneity promotes robust learning," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26022-3
    DOI: 10.1038/s41467-021-26022-3
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    Cited by:

    1. Marcello, Salustri & Shunra, Yoshida & Ruggero, Micheletto, 2023. "Neural and axonal heterogeneity improves information transmission," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    2. Hanle Zheng & Zhong Zheng & Rui Hu & Bo Xiao & Yujie Wu & Fangwen Yu & Xue Liu & Guoqi Li & Lei Deng, 2024. "Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    3. Michalis Pagkalos & Spyridon Chavlis & Panayiota Poirazi, 2023. "Introducing the Dendrify framework for incorporating dendrites to spiking neural networks," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    4. Filippo Costa & Eline V. Schaft & Geertjan Huiskamp & Erik J. Aarnoutse & Maryse A. van’t Klooster & Niklaus Krayenbühl & Georgia Ramantani & Maeike Zijlmans & Giacomo Indiveri & Johannes Sarnthein, 2024. "Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    5. Becker-Ritterspach, Florian A.A. & Lange, Knut S.G. & Allen, Matthew M.C., 2022. "Dominant modes of economic coordination and varieties of firm internationalization support," International Business Review, Elsevier, vol. 31(3).
    6. Michele N. Insanally & Badr F. Albanna & Jade Toth & Brian DePasquale & Saba Shokat Fadaei & Trisha Gupta & Olivia Lombardi & Kishore Kuchibhotla & Kanaka Rajan & Robert C. Froemke, 2024. "Contributions of cortical neuron firing patterns, synaptic connectivity, and plasticity to task performance," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    7. Simone D’Agostino & Filippo Moro & Tristan Torchet & Yiğit Demirağ & Laurent Grenouillet & Niccolò Castellani & Giacomo Indiveri & Elisa Vianello & Melika Payvand, 2024. "DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    8. Roxana Zeraati & Yan-Liang Shi & Nicholas A. Steinmetz & Marc A. Gieselmann & Alexander Thiele & Tirin Moore & Anna Levina & Tatiana A. Engel, 2023. "Intrinsic timescales in the visual cortex change with selective attention and reflect spatial connectivity," Nature Communications, Nature, vol. 14(1), pages 1-19, December.

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