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Introducing principles of synaptic integration in the optimization of deep neural networks

Author

Listed:
  • Giorgia Dellaferrera

    (IBM Research - Zurich
    University of Zurich and ETH Zurich)

  • Stanisław Woźniak

    (IBM Research - Zurich)

  • Giacomo Indiveri

    (University of Zurich and ETH Zurich)

  • Angeliki Pantazi

    (IBM Research - Zurich)

  • Evangelos Eleftheriou

    (IBM Research - Zurich
    Axelera AI)

Abstract

Plasticity circuits in the brain are known to be influenced by the distribution of the synaptic weights through the mechanisms of synaptic integration and local regulation of synaptic strength. However, the complex interplay of stimulation-dependent plasticity with local learning signals is disregarded by most of the artificial neural network training algorithms devised so far. Here, we propose a novel biologically inspired optimizer for artificial and spiking neural networks that incorporates key principles of synaptic plasticity observed in cortical dendrites: GRAPES (Group Responsibility for Adjusting the Propagation of Error Signals). GRAPES implements a weight-distribution-dependent modulation of the error signal at each node of the network. We show that this biologically inspired mechanism leads to a substantial improvement of the performance of artificial and spiking networks with feedforward, convolutional, and recurrent architectures, it mitigates catastrophic forgetting, and it is optimally suited for dedicated hardware implementations. Overall, our work indicates that reconciling neurophysiology insights with machine intelligence is key to boosting the performance of neural networks.

Suggested Citation

  • Giorgia Dellaferrera & Stanisław Woźniak & Giacomo Indiveri & Angeliki Pantazi & Evangelos Eleftheriou, 2022. "Introducing principles of synaptic integration in the optimization of deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29491-2
    DOI: 10.1038/s41467-022-29491-2
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    References listed on IDEAS

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    1. Sébastien Royer & Denis Paré, 2003. "Conservation of total synaptic weight through balanced synaptic depression and potentiation," Nature, Nature, vol. 422(6931), pages 518-522, April.
    2. Gina G. Turrigiano & Kenneth R. Leslie & Niraj S. Desai & Lana C. Rutherford & Sacha B. Nelson, 1998. "Activity-dependent scaling of quantal amplitude in neocortical neurons," Nature, Nature, vol. 391(6670), pages 892-896, February.
    3. Timothy P. Lillicrap & Daniel Cownden & Douglas B. Tweed & Colin J. Akerman, 2016. "Random synaptic feedback weights support error backpropagation for deep learning," Nature Communications, Nature, vol. 7(1), pages 1-10, December.
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