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Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network

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  • Bruno Del Papa
  • Viola Priesemann
  • Jochen Triesch

Abstract

Many experiments have suggested that the brain operates close to a critical state, based on signatures of criticality such as power-law distributed neuronal avalanches. In neural network models, criticality is a dynamical state that maximizes information processing capacities, e.g. sensitivity to input, dynamical range and storage capacity, which makes it a favorable candidate state for brain function. Although models that self-organize towards a critical state have been proposed, the relation between criticality signatures and learning is still unclear. Here, we investigate signatures of criticality in a self-organizing recurrent neural network (SORN). Investigating criticality in the SORN is of particular interest because it has not been developed to show criticality. Instead, the SORN has been shown to exhibit spatio-temporal pattern learning through a combination of neural plasticity mechanisms and it reproduces a number of biological findings on neural variability and the statistics and fluctuations of synaptic efficacies. We show that, after a transient, the SORN spontaneously self-organizes into a dynamical state that shows criticality signatures comparable to those found in experiments. The plasticity mechanisms are necessary to attain that dynamical state, but not to maintain it. Furthermore, onset of external input transiently changes the slope of the avalanche distributions – matching recent experimental findings. Interestingly, the membrane noise level necessary for the occurrence of the criticality signatures reduces the model’s performance in simple learning tasks. Overall, our work shows that the biologically inspired plasticity and homeostasis mechanisms responsible for the SORN’s spatio-temporal learning abilities can give rise to criticality signatures in its activity when driven by random input, but these break down under the structured input of short repeating sequences.

Suggested Citation

  • Bruno Del Papa & Viola Priesemann & Jochen Triesch, 2017. "Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-21, May.
  • Handle: RePEc:plo:pone00:0178683
    DOI: 10.1371/journal.pone.0178683
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    References listed on IDEAS

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    1. Shree Hari Gautam & Thanh T Hoang & Kylie McClanahan & Stephen K Grady & Woodrow L Shew, 2015. "Maximizing Sensory Dynamic Range by Tuning the Cortical State to Criticality," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-15, December.
    2. Jonathan Touboul & Alain Destexhe, 2010. "Can Power-Law Scaling and Neuronal Avalanches Arise from Stochastic Dynamics?," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-14, February.
    3. Jeff Alstott & Ed Bullmore & Dietmar Plenz, 2014. "powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    4. Christian Meisel & Alexander Storch & Susanne Hallmeyer-Elgner & Ed Bullmore & Thilo Gross, 2012. "Failure of Adaptive Self-Organized Criticality during Epileptic Seizure Attacks," PLOS Computational Biology, Public Library of Science, vol. 8(1), pages 1-8, January.
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    Cited by:

    1. Menesse, Gustavo & Marin, Bóris & Girardi-Schappo, Mauricio & Kinouchi, Osame, 2022. "Homeostatic criticality in neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    2. Choi, Jaesung & Kim, Pilwon, 2020. "Reservoir computing based on quenched chaos," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Safaeesirat, Amin & Moghimi-Araghi, Saman, 2022. "Critical behavior at the onset of synchronization in a neuronal model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).

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