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Sensory cortex is optimized for prediction of future input.

Yosef Singer1, Yayoi Teramoto1, Ben Db Willmore1

  • 1Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.

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|June 19, 2018
PubMed
Summary
This summary is machine-generated.

Sensory cortex neurons are tuned to predict future inputs from recent sensory data. This predictive coding principle shapes neuronal selectivity, mirroring receptive fields found in mammalian brains.

Keywords:
auditorycortexferretmodelneurosciencenormativeprediction

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Machine Learning

Background:

  • Neurons in the sensory cortex exhibit selectivity for diverse features in natural scenes.
  • The underlying principles determining this neuronal selectivity remain incompletely understood.

Purpose of the Study:

  • To investigate whether neuronal selectivity is optimized for representing features that predict immediate future sensory inputs.
  • To test the hypothesis that predictive coding shapes receptive field properties in the sensory cortex.

Main Methods:

  • Utilized simple feedforward neural networks trained on natural scene video and audio clips.
  • Trained networks to predict subsequent moments in sensory data streams.
  • Compared network-generated receptive fields with those of real cortical neurons across mammalian species.

Main Results:

  • Developed receptive fields in neural networks closely matched real cortical neurons.
  • Observed oriented spatial tuning (visual cortex) and frequency selectivity (auditory cortex).
  • Found that superior prediction accuracy correlated with more brain-like receptive fields, particularly temporal tuning.

Conclusions:

  • Neuronal selectivity in the sensory cortex is shaped by the need to predict future sensory inputs.
  • The brain optimizes sensory processing to extract features with high predictive power.
  • Predictive coding offers a unifying framework for understanding sensory cortical organization.