IDEAS home Printed from https://ideas.repec.org/a/plo/pbio00/2002459.html
   My bibliography  Save this article

Rate, not selectivity, determines neuronal population coding accuracy in auditory cortex

Author

Listed:
  • Wensheng Sun
  • Dennis L Barbour

Abstract

The notion that neurons with higher selectivity carry more information about external sensory inputs is widely accepted in neuroscience. High-selectivity neurons respond to a narrow range of sensory inputs, and thus would be considered highly informative by rejecting a large proportion of possible inputs. In auditory cortex, neuronal responses are less selective immediately after the onset of a sound and then become highly selective in the following sustained response epoch. These 2 temporal response epochs have thus been interpreted to encode first the presence and then the content of a sound input. Contrary to predictions from that prevailing theory, however, we found that the neural population conveys similar information about sound input across the 2 epochs in spite of the neuronal selectivity differences. The amount of information encoded turns out to be almost completely dependent upon the total number of population spikes in the read-out window for this system. Moreover, inhomogeneous Poisson spiking behavior is sufficient to account for this property. These results imply a novel principle of sensory encoding that is potentially shared widely among multiple sensory systems.Author summary: Neurons act together to encode information such as the nature of a sensory stimulus. The number of neurons used for individual stimuli and the nature of the encoding used are not well understood. Higher sensory areas have been observed to respond sparsely to sensory stimuli, meaning that only a relatively small number of neurons fire action potentials (or “spikes”) when any particular stimulus is present. Sparse spiking activity is present in primary auditory cortex but only after a sound has been playing for some period of time. Dense spiking, however, is present at stimulus onset. We found that each action potential in primary auditory cortex contributed approximately the same amount of information about a tone stimulus, which resulted in more accurate encoding from the dense onset spiking. The later sparse spiking retained stimulus information but required a longer time to read it out. This arrangement allows sensory stimuli to be identified rapidly yet represented efficiently for extended periods, while neurons still retain sensitivity to novel stimuli. Dense and sparse coding may therefore work together dynamically in order to represent complex, temporally overlapping sensory content.

Suggested Citation

  • Wensheng Sun & Dennis L Barbour, 2017. "Rate, not selectivity, determines neuronal population coding accuracy in auditory cortex," PLOS Biology, Public Library of Science, vol. 15(11), pages 1-22, November.
  • Handle: RePEc:plo:pbio00:2002459
    DOI: 10.1371/journal.pbio.2002459
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2002459
    Download Restriction: no

    File URL: https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.2002459&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pbio.2002459?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Diego A. Gutnisky & Valentin Dragoi, 2008. "Adaptive coding of visual information in neural populations," Nature, Nature, vol. 452(7184), pages 220-224, March.
    2. Debajit Saha & Chao Li & Steven Peterson & William Padovano & Nalin Katta & Baranidharan Raman, 2015. "Behavioural correlates of combinatorial versus temporal features of odour codes," Nature Communications, Nature, vol. 6(1), pages 1-13, November.
    3. Xiaoqin Wang & Thomas Lu & Ross K. Snider & Li Liang, 2005. "Sustained firing in auditory cortex evoked by preferred stimuli," Nature, Nature, vol. 435(7040), pages 341-346, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ashok Litwin-Kumar & Anne-Marie M Oswald & Nathaniel N Urban & Brent Doiron, 2011. "Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains," PLOS Computational Biology, Public Library of Science, vol. 7(12), pages 1-14, December.
    2. Michael A Carlin & Mounya Elhilali, 2013. "Sustained Firing of Model Central Auditory Neurons Yields a Discriminative Spectro-temporal Representation for Natural Sounds," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-18, March.
    3. Lanlan Ma & Xuhui Tai & Liye Su & Lijuan Shi & Enhua Wang & Ling Qin, 2013. "The Neuronal Responses to Repetitive Acoustic Pulses in Different Fields of the Auditory Cortex of Awake Rats," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-10, May.
    4. Tomáš Hromádka & Michael R DeWeese & Anthony M Zador, 2008. "Sparse Representation of Sounds in the Unanesthetized Auditory Cortex," PLOS Biology, Public Library of Science, vol. 6(1), pages 1-14, January.
    5. Sunny Nigam & Russell Milton & Sorin Pojoga & Valentin Dragoi, 2023. "Adaptive coding across visual features during free-viewing and fixation conditions," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    6. Jerome Carriot & Graham McAllister & Hamed Hooshangnejad & Isabelle Mackrous & Kathleen E. Cullen & Maurice J. Chacron, 2022. "Sensory adaptation mediates efficient and unambiguous encoding of natural stimuli by vestibular thalamocortical pathways," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    7. Julie E Elie & Frédéric E Theunissen, 2019. "Invariant neural responses for sensory categories revealed by the time-varying information for communication calls," PLOS Computational Biology, Public Library of Science, vol. 15(9), pages 1-43, September.
    8. Ru-Yuan Zhang & Xue-Xin Wei & Kendrick Kay, 2020. "Understanding multivariate brain activity: Evaluating the effect of voxelwise noise correlations on population codes in functional magnetic resonance imaging," PLOS Computational Biology, Public Library of Science, vol. 16(8), pages 1-29, August.
    9. Geoffrey Terral & Evan Harrell & Gabriel Lepousez & Yohan Wards & Dinghuang Huang & Tiphaine Dolique & Giulio Casali & Antoine Nissant & Pierre-Marie Lledo & Guillaume Ferreira & Giovanni Marsicano & , 2024. "Endogenous cannabinoids in the piriform cortex tune olfactory perception," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    10. Roohollah Massoudi & Marc M Van Wanrooij & Huib Versnel & A John Van Opstal, 2015. "Spectrotemporal Response Properties of Core Auditory Cortex Neurons in Awake Monkey," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-30, February.
    11. Qianli Yang & Edgar Walker & R. James Cotton & Andreas S. Tolias & Xaq Pitkow, 2021. "Revealing nonlinear neural decoding by analyzing choices," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    12. Rishabh Chandak & Baranidharan Raman, 2023. "Neural manifolds for odor-driven innate and acquired appetitive preferences," Nature Communications, Nature, vol. 14(1), pages 1-21, December.
    13. Daniel Bendor, 2015. "The Role of Inhibition in a Computational Model of an Auditory Cortical Neuron during the Encoding of Temporal Information," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-25, April.
    14. Christopher F. Angeloni & Wiktor Młynarski & Eugenio Piasini & Aaron M. Williams & Katherine C. Wood & Linda Garami & Ann M. Hermundstad & Maria N. Geffen, 2023. "Dynamics of cortical contrast adaptation predict perception of signals in noise," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    15. Inga Petelski & Yannick Günzel & Sercan Sayin & Susanne Kraus & Einat Couzin-Fuchs, 2024. "Synergistic olfactory processing for social plasticity in desert locusts," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    16. Rava Azeredo da Silveira & Michael J Berry II, 2014. "High-Fidelity Coding with Correlated Neurons," PLOS Computational Biology, Public Library of Science, vol. 10(11), pages 1-25, November.
    17. Arno Onken & Valentin Dragoi & Klaus Obermayer, 2012. "A Maximum Entropy Test for Evaluating Higher-Order Correlations in Spike Counts," PLOS Computational Biology, Public Library of Science, vol. 8(6), pages 1-12, June.
    18. Laurence Aitchison & Máté Lengyel, 2016. "The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-24, December.
    19. Elaine Tring & Mario Dipoppa & Dario L. Ringach, 2023. "A power law describes the magnitude of adaptation in neural populations of primary visual cortex," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    20. Vikranth R Bejjanki & Rava Azeredo da Silveira & Jonathan D Cohen & Nicholas B Turk-Browne, 2017. "Noise correlations in the human brain and their impact on pattern classification," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-23, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pbio00:2002459. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosbiology (email available below). General contact details of provider: https://journals.plos.org/plosbiology/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.