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The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks

View ORCID ProfileManu Srinath Halvagal, View ORCID ProfileFriedemann Zenke
doi: https://doi.org/10.1101/2022.03.17.484712
Manu Srinath Halvagal
1Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland
2Faculty of Natural Sciences, University of Basel, 4033 Basel, Switzerland
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  • ORCID record for Manu Srinath Halvagal
Friedemann Zenke
1Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland
2Faculty of Natural Sciences, University of Basel, 4033 Basel, Switzerland
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  • For correspondence: friedemann.zenke@fmi.ch
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Abstract

Discriminating distinct objects and concepts from sensory stimuli is essential for survival. Our brains accomplish this feat by forming disentangled internal representations in deep sensory networks shaped through experience-dependent synaptic plasticity. To elucidate the principles that underlie sensory representation learning, we derive a local plasticity model that shapes latent representations to predict future activity. This Latent Predictive Learning (LPL) rule conceptually extends Bienenstock-Cooper-Munro (BCM) theory by unifying Hebbian plasticity with predictive learning. We show that deep neural networks equipped with LPL develop disentangled object representations without supervision. The same rule accurately captures neuronal selectivity changes observed in the primate inferotemporal cortex in response to altered visual experience. Finally, our model generalizes to spiking neural networks and naturally accounts for several experimentally observed properties of synaptic plasticity, including metaplasticity and spike-timing-dependent plasticity (STDP). We thus provide a plausible normative theory of representation learning in the brain while making concrete testable predictions.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • This manuscript version now contains additional results on Latent Predictive Learning in spiking neural networks in which lateral inhibition and inhibitory plasticity implement neuronal decorrelation.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 25, 2022.
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The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks
Manu Srinath Halvagal, Friedemann Zenke
bioRxiv 2022.03.17.484712; doi: https://doi.org/10.1101/2022.03.17.484712
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The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks
Manu Srinath Halvagal, Friedemann Zenke
bioRxiv 2022.03.17.484712; doi: https://doi.org/10.1101/2022.03.17.484712

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