1. Neuroscience

Sensory cortex is optimised for prediction of future input

  1. Yosef Singer
  2. Yayoi Teramoto
  3. Ben DB Willmore
  4. Andrew J King
  5. Jan W H Schnupp
  6. Nicol S Harper  Is a corresponding author
  1. University of Oxford, United Kingdom
https://doi.org/10.7554/eLife.31557
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  1. Yosef Singer
  2. Yayoi Teramoto
  3. Ben DB Willmore
  4. Andrew J King
  5. Jan W H Schnupp
  6. Nicol S Harper
(2018)
Sensory cortex is optimised for prediction of future input
eLife 7:e31557.
https://doi.org/10.7554/eLife.31557
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Abstract

Neurons in sensory cortex are tuned to diverse features in natural scenes. But what determines which features neurons become selective to? Here we explore the idea that neuronal selectivity is optimised to represent features in the recent sensory past that best predict immediate future inputs. We tested this hypothesis using simple feedforward neural networks, which were trained to predict the next few video or audio frames in clips of natural scenes. The networks developed receptive fields that closely matched those of real cortical neurons in different mammalian species, including the oriented spatial tuning of primary visual cortex, the frequency selectivity of primary auditory cortex and, most notably, their temporal tuning properties. Furthermore, the better a network predicted future inputs the more closely its receptive fields resembled those in the brain. This suggests that sensory processing is optimised to extract those features with the most capacity to predict future input.

Data availability

All custom code used in this study was implemented in MATLAB and Python. We have uploaded the code to a public Github repository. The raw auditory experimental data is available at  https://osf.io/ayw2p/. The movies and sounds used for training the models are all publicly available at the websites detailed in the Methods.

The following data sets were generated
    1. Jan Schnupp
    (2016) NetworkReceptiveFields
    Available at the Open Science Framework.
    https://osf.io/ayw2p/

Article and author information

Author details

  1. Yosef Singer

    Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.

  2. Yayoi Teramoto

    Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3419-0351

  3. Ben DB Willmore

    Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.

  4. Andrew J King

    Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
    Competing interests
    Andrew J King, Senior Editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5180-7179

  5. Jan W H Schnupp

    Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.

  6. Nicol S Harper

    Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
    For correspondence
    nicol.harper@dpag.ox.ac.uk
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7851-4840

Funding

Clarendon Fund

  • Yosef Singer
  • Yayoi Teramoto

University Of Oxford

  • Nicol S Harper

Action on Hearing Loss (PA07)

  • Nicol S Harper

Biotechnology and Biological Sciences Research Council (BB/H008608/1)

  • Nicol S Harper

Wellcome (WT10525/Z/14/Z)

  • Yayoi Teramoto

Wellcome (WT076508AIA)

  • Ben DB Willmore

Wellcome (WT108369/Z/2015/Z)

  • Ben DB Willmore

Wellcome (WT076508AIA)

  • Andrew J King

Wellcome (WT108369/Z/2015/Z)

  • Andrew J King

Wellcome (WT082692)

  • Nicol S Harper

Wellcome (WT076508AIA)

  • Nicol S Harper

Wellcome (WT108369/Z/2015/Z)

  • Nicol S Harper

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: Auditory RFs of neurons were recorded in the primary auditory cortex (A1) and anterior auditory field (AAF) of 5 pigmented ferrets of both sexes (all > 6 months of age) and used as a basis for comparison with the RFs of model units trained on auditory stimuli. These recordings were performed under license from the UK Home Office and were approved by the University of Oxford Committee on Animal Care and Ethical Review. Full details of the recording methods are described in earlier studies [45,90]. Briefly, we induced general anaesthesia with a single intramuscular dose of medetomidine (0.022 mg · kg−1 · h−1) and ketamine (5 mg · kg−1 · h−1), which was then maintained with a continuous intravenous infusion of medetomidine and ketamine in saline. Oxygen was supplemented with a ventilator, and we monitored vital signs (body temperature, end-tidal CO2, and the electrocardiogram) throughout the experiment. The temporal muscles were retracted, a head holder was secured to the skull surface, and a craniotomy and a durotomy were made over the auditory cortex. Extracellular recordings were made using silicon probe electrodes (Neuronexus Technologies) and acoustic stimuli were presented via Panasonic RPHV27 earphones, which were coupled to otoscope specula that were inserted into each ear canal, and driven by Tucker-Davis Technologies System III hardware (48 kHz sample rate).

Copyright

© 2018, Singer et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Yosef Singer
  2. Yayoi Teramoto
  3. Ben DB Willmore
  4. Andrew J King
  5. Jan W H Schnupp
  6. Nicol S Harper
(2018)
Sensory cortex is optimised for prediction of future input
eLife 7:e31557.
https://doi.org/10.7554/eLife.31557

Share this article

https://doi.org/10.7554/eLife.31557

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