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Neurons: The Axon01:21

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Neurons as Canonical Correlation Analyzers.

Cengiz Pehlevan1, Xinyuan Zhao2, Anirvan M Sengupta3

  • 1John A. Paulson School of Engineering and Applied Sciences and Center for Brain Science, Harvard University, Cambridge, MA, United States.

Frontiers in Computational Neuroscience
|July 23, 2020
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Summary
This summary is machine-generated.

This study models pyramidal neuron networks using online Canonical Correlation Analysis (CCA), offering a new framework for understanding how these neurons integrate multiple inputs through synaptic plasticity.

Keywords:
Canonical Correlation Analysis (CCA)Hebbian plasticitybiologically plausible learningneural networkspyramidal neuron

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Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Mathematical Biology

Background:

  • Normative models simplify complex neural computation.
  • Previous models used online Principal Component Analysis (PCA) for single-compartment neurons.
  • Biological synaptic plasticity is influenced by dendritic integration, not just somatic current.

Purpose of the Study:

  • To model pyramidal neuronal networks using online Canonical Correlation Analysis (CCA).
  • To develop a normative framework for understanding synaptic plasticity in dendritic compartments.
  • To demonstrate how neuronal networks integrate multiple input sources.

Main Methods:

  • Utilized online Canonical Correlation Analysis (CCA) for modeling neuronal networks.
  • Derived online gradient-based optimization algorithms from single-channel and multi-channel CCA objective functions.
  • Extended the model to multi-compartment neurons using multi-view CCA objectives.

Main Results:

  • Developed an online gradient-based algorithm interpretable as pyramidal neuron operation.
  • Introduced a multi-channel CCA objective for modeling pyramidal neuron networks.
  • Confirmed network functionality through numerical simulations.

Conclusions:

  • Presents a simplified abstraction of learning in pyramidal neuron networks.
  • Demonstrates the capability of these networks to integrate multiple input sources.
  • Highlights the utility of CCA in modeling biologically plausible neural computation.