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A normative framework for learning top-down predictions through synaptic plasticity in apical dendrites

View ORCID ProfileArjun Rao, View ORCID ProfileRobert Legenstein, View ORCID ProfileAnand Subramoney, View ORCID ProfileWolfgang Maass
doi: https://doi.org/10.1101/2021.03.04.433822
Arjun Rao
1Institute for Theoretical Computer Science, Graz University of Technology, 8010, Austria
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Robert Legenstein
1Institute for Theoretical Computer Science, Graz University of Technology, 8010, Austria
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Anand Subramoney
1Institute for Theoretical Computer Science, Graz University of Technology, 8010, Austria
2Institute for Neural Computation, Ruhr University Bochum, Germany
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Wolfgang Maass
1Institute for Theoretical Computer Science, Graz University of Technology, 8010, Austria
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  • For correspondence: maass@igi.tugraz.at
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Abstract

Predictive coding has been identified as a key aspect of computation and learning in cortical microcircuits. But we do not know how synaptic plasticity processes install and maintain predictive coding capabilites in these neural circuits. Predictions are inherently uncertain, and learning rules that aim at discriminating linearly separable classes of inputs – such as the perceptron learning rule – do not perform well if the goal is learning to predict. We show that experimental data on synaptic plasticity in apical dendrites of pyramidal cells support another learning rule that is suitable for learning to predict. More precisely, it enables a spike-based approximation to logistic regression, a well-known gold standard for probabilistic prediction. We also show that data-based interactions between apical dendrites support learning of predictions for more complex probability distributions than those that can be handled by single dendrites. The resulting learning theory for top-down inputs to pyramidal cells provides a normative framework for evaluating experimental data, and suggests further experiments for tracking the emergence of predictive coding through synaptic plasticity in apical dendrites.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* First authors

  • The results corresponding to the prediction of the moving object have been clarified to show how the target distributions are accurately predicted. Figure 3 has been correspondingly revised. The methods sections have been updated to include missing parameters and to specify the precise implementation of the learning rule using input spikes. Title has been changed.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted May 05, 2021.
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A normative framework for learning top-down predictions through synaptic plasticity in apical dendrites
Arjun Rao, Robert Legenstein, Anand Subramoney, Wolfgang Maass
bioRxiv 2021.03.04.433822; doi: https://doi.org/10.1101/2021.03.04.433822
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A normative framework for learning top-down predictions through synaptic plasticity in apical dendrites
Arjun Rao, Robert Legenstein, Anand Subramoney, Wolfgang Maass
bioRxiv 2021.03.04.433822; doi: https://doi.org/10.1101/2021.03.04.433822

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