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Learning what matters: Synaptic plasticity with invariance to second-order input correlations.

Carlos Stein Naves de Brito1,2, Wulfram Gerstner1

  • 1École Polytechnique Fédérale de Lausanne, EPFL, Lusanne, Switzerland.

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This study introduces a new theory for sparse coding and synaptic plasticity, demonstrating how Hebbian long-term depression (LTD) helps neurons learn features from complex environmental data by ignoring irrelevant correlations.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Machine Learning

Background:

  • Cortical neurons form sparse representations reflecting environmental statistics.
  • Synaptic plasticity must distinguish true features from spurious input correlations for efficient coding.
  • Existing models struggle with omnipresent second-order correlations in cortical networks.

Purpose of the Study:

  • To develop a theory for sparse coding and synaptic plasticity invariant to second-order input correlations.
  • To explain the role of Hebbian long-term depression (LTD) in refining neural representations.
  • To enhance biologically plausible learning models for improved decoding and sparse coding.

Main Methods:

  • Developed a novel theoretical framework for sparse coding and synaptic plasticity.
  • Analyzed the functional form of excitatory plasticity, including Hebbian LTD.
  • Utilized a spiking neural network model with triplet spike-timing-dependent plasticity (STDP).

Main Results:

  • The theory demonstrates how LTD cancels sensitivity to second-order correlations, aligning receptive fields with higher-order statistics.
  • Invariance to second-order correlations improves decoding from noisy and spatially correlated inputs.
  • Individual neurons in the STDP model learned localized, oriented receptive fields without preprocessing or lateral inhibition.

Conclusions:

  • The developed theory advances understanding of local unsupervised learning in cortical circuits.
  • Hebbian LTD plays a crucial role in enabling neurons to learn relevant features from complex sensory data.
  • The findings offer new interpretations for models like Bienenstock-Cooper-Munro and triplet STDP, highlighting LTD's function in pyramidal neurons.