Sensory cortex is optimised for prediction of future input

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.

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).

Reviewing Editor

  1. Jack L Gallant, University of California, Berkeley, United States

Publication history

  1. Received: August 25, 2017
  2. Accepted: June 16, 2018
  3. Accepted Manuscript published: June 18, 2018 (version 1)
  4. Accepted Manuscript updated: June 22, 2018 (version 2)
  5. Version of Record published: August 24, 2018 (version 3)
  6. Version of Record updated: September 18, 2018 (version 4)

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.

Metrics

  • 6,963
    Page views
  • 1,032
    Downloads
  • 19
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Further reading

    1. Developmental Biology
    2. Neuroscience
    Ashtyn T Wiltbank et al.
    Research Article

    Efficient neurotransmission is essential for organism survival and is enhanced by myelination. However, the genes that regulate myelin and myelinating glial cell development have not been fully characterized. Data from our lab and others demonstrates that cd59, which encodes for a small GPI-anchored glycoprotein, is highly expressed in developing zebrafish, rodent, and human oligodendrocytes (OLs) and Schwann cells (SCs), and that patients with CD59 dysfunction develop neurological dysfunction during early childhood. Yet, the function of Cd59 in the developing nervous system is currently undefined. In this study, we demonstrate that cd59 is expressed in a subset of developing SCs. Using cd59 mutant zebrafish, we show that developing SCs proliferate excessively and nerves may have reduced myelin volume, altered myelin ultrastructure, and perturbed node of Ranvier assembly. Finally, we demonstrate that complement activity is elevated in cd59 mutants and that inhibiting inflammation restores SC proliferation, myelin volume, and nodes of Ranvier to wildtype levels. Together, this work identifies Cd59 and developmental inflammation as key players in myelinating glial cell development, highlighting the collaboration between glia and the innate immune system to ensure normal neural development.

    1. Neuroscience
    Arefeh Sherafati et al.
    Research Article Updated

    Cochlear implants are neuroprosthetic devices that can restore hearing in people with severe to profound hearing loss by electrically stimulating the auditory nerve. Because of physical limitations on the precision of this stimulation, the acoustic information delivered by a cochlear implant does not convey the same level of acoustic detail as that conveyed by normal hearing. As a result, speech understanding in listeners with cochlear implants is typically poorer and more effortful than in listeners with normal hearing. The brain networks supporting speech understanding in listeners with cochlear implants are not well understood, partly due to difficulties obtaining functional neuroimaging data in this population. In the current study, we assessed the brain regions supporting spoken word understanding in adult listeners with right unilateral cochlear implants (n=20) and matched controls (n=18) using high-density diffuse optical tomography (HD-DOT), a quiet and non-invasive imaging modality with spatial resolution comparable to that of functional MRI. We found that while listening to spoken words in quiet, listeners with cochlear implants showed greater activity in the left prefrontal cortex than listeners with normal hearing, specifically in a region engaged in a separate spatial working memory task. These results suggest that listeners with cochlear implants require greater cognitive processing during speech understanding than listeners with normal hearing, supported by compensatory recruitment of the left prefrontal cortex.