IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1003143.html
   My bibliography  Save this article

Inferring Nonlinear Neuronal Computation Based on Physiologically Plausible Inputs

Author

Listed:
  • James M McFarland
  • Yuwei Cui
  • Daniel A Butts

Abstract

The computation represented by a sensory neuron's response to stimuli is constructed from an array of physiological processes both belonging to that neuron and inherited from its inputs. Although many of these physiological processes are known to be nonlinear, linear approximations are commonly used to describe the stimulus selectivity of sensory neurons (i.e., linear receptive fields). Here we present an approach for modeling sensory processing, termed the Nonlinear Input Model (NIM), which is based on the hypothesis that the dominant nonlinearities imposed by physiological mechanisms arise from rectification of a neuron's inputs. Incorporating such ‘upstream nonlinearities’ within the standard linear-nonlinear (LN) cascade modeling structure implicitly allows for the identification of multiple stimulus features driving a neuron's response, which become directly interpretable as either excitatory or inhibitory. Because its form is analogous to an integrate-and-fire neuron receiving excitatory and inhibitory inputs, model fitting can be guided by prior knowledge about the inputs to a given neuron, and elements of the resulting model can often result in specific physiological predictions. Furthermore, by providing an explicit probabilistic model with a relatively simple nonlinear structure, its parameters can be efficiently optimized and appropriately regularized. Parameter estimation is robust and efficient even with large numbers of model components and in the context of high-dimensional stimuli with complex statistical structure (e.g. natural stimuli). We describe detailed methods for estimating the model parameters, and illustrate the advantages of the NIM using a range of example sensory neurons in the visual and auditory systems. We thus present a modeling framework that can capture a broad range of nonlinear response functions while providing physiologically interpretable descriptions of neural computation.Author Summary: Sensory neurons are capable of representing a wide array of computations on sensory stimuli. Such complex computations are thought to arise in large part from the accumulation of relatively simple nonlinear operations across the sensory processing hierarchies. However, models of sensory processing typically rely on mathematical approximations of the overall relationship between stimulus and response, such as linear or quadratic expansions, which can overlook critical elements of sensory computation and miss opportunities to reveal how the underlying inputs contribute to a neuron's response. Here we present a physiologically inspired nonlinear modeling framework, the ‘Nonlinear Input Model’ (NIM), which instead assumes that neuronal computation can be approximated as a sum of excitatory and suppressive ‘neuronal inputs’. We show that this structure is successful at explaining neuronal responses in a variety of sensory areas. Furthermore, model fitting can be guided by prior knowledge about the inputs to a given neuron, and its results can often suggest specific physiological predictions. We illustrate the advantages of the proposed model and demonstrate specific parameter estimation procedures using a range of example sensory neurons in both the visual and auditory systems.

Suggested Citation

  • James M McFarland & Yuwei Cui & Daniel A Butts, 2013. "Inferring Nonlinear Neuronal Computation Based on Physiologically Plausible Inputs," PLOS Computational Biology, Public Library of Science, vol. 9(7), pages 1-18, July.
  • Handle: RePEc:plo:pcbi00:1003143
    DOI: 10.1371/journal.pcbi.1003143
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003143
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003143&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1003143?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. Anja L. Dorrn & Kexin Yuan & Alison J. Barker & Christoph E. Schreiner & Robert C. Froemke, 2010. "Developmental sensory experience balances cortical excitation and inhibition," Nature, Nature, vol. 465(7300), pages 932-936, June.
    2. Nicholas A Lesica & Chong Weng & Jianzhong Jin & Chun-I Yeh & Jose-Manuel Alonso & Garrett B Stanley, 2006. "Dynamic Encoding of Natural Luminance Sequences by LGN Bursts," PLOS Biology, Public Library of Science, vol. 4(7), pages 1-1, June.
    3. Michael Wehr & Anthony M. Zador, 2003. "Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex," Nature, Nature, vol. 426(6965), pages 442-446, November.
    4. Yujiao J. Sun & Guangying K. Wu & Bao-hua Liu & Pingyang Li & Mu Zhou & Zhongju Xiao & Huizhong W. Tao & Li I. Zhang, 2010. "Fine-tuning of pre-balanced excitation and inhibition during auditory cortical development," Nature, Nature, vol. 465(7300), pages 927-931, June.
    5. Daniel A. Butts & Chong Weng & Jianzhong Jin & Chun-I Yeh & Nicholas A. Lesica & Jose-Manuel Alonso & Garrett B. Stanley, 2007. "Temporal precision in the neural code and the timescales of natural vision," Nature, Nature, vol. 449(7158), pages 92-95, September.
    6. Timm Lochmann & Timothy J Blanche & Daniel A Butts, 2013. "Construction of Direction Selectivity through Local Energy Computations in Primary Visual Cortex," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-13, March.
    7. Jeffrey D Fitzgerald & Ryan J Rowekamp & Lawrence C Sincich & Tatyana O Sharpee, 2011. "Second Order Dimensionality Reduction Using Minimum and Maximum Mutual Information Models," PLOS Computational Biology, Public Library of Science, vol. 7(10), pages 1-9, October.
    8. Unknown, 2005. "Forward," 2005 Conference: Slovenia in the EU - Challenges for Agriculture, Food Science and Rural Affairs, November 10-11, 2005, Moravske Toplice, Slovenia 183804, Slovenian Association of Agricultural Economists (DAES).
    9. Donald R Cantrell & Jianhua Cang & John B Troy & Xiaorong Liu, 2010. "Non-Centered Spike-Triggered Covariance Analysis Reveals Neurotrophin-3 as a Developmental Regulator of Receptive Field Properties of ON-OFF Retinal Ganglion Cells," PLOS Computational Biology, Public Library of Science, vol. 6(10), pages 1-16, October.
    10. Ana Calabrese & Joseph W Schumacher & David M Schneider & Liam Paninski & Sarah M N Woolley, 2011. "A Generalized Linear Model for Estimating Spectrotemporal Receptive Fields from Responses to Natural Sounds," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-16, January.
    11. Mijung Park & Jonathan W Pillow, 2011. "Receptive Field Inference with Localized Priors," PLOS Computational Biology, Public Library of Science, vol. 7(10), pages 1-16, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Max F Burg & Santiago A Cadena & George H Denfield & Edgar Y Walker & Andreas S Tolias & Matthias Bethge & Alexander S Ecker, 2021. "Learning divisive normalization in primary visual cortex," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-31, June.
    2. Niru Maheswaranathan & David B Kastner & Stephen A Baccus & Surya Ganguli, 2018. "Inferring hidden structure in multilayered neural circuits," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-30, August.
    3. Ivar L Thorson & Jean Liénard & Stephen V David, 2015. "The Essential Complexity of Auditory Receptive Fields," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-33, December.
    4. Lucas Theis & Andrè Maia Chagas & Daniel Arnstein & Cornelius Schwarz & Matthias Bethge, 2013. "Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-9, November.
    5. Ross S Williamson & Maneesh Sahani & Jonathan W Pillow, 2015. "The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-31, April.
    6. Julian Rossbroich & Daniel Trotter & John Beninger & Katalin Tóth & Richard Naud, 2021. "Linear-nonlinear cascades capture synaptic dynamics," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-27, March.
    7. Johannes Burge & Priyank Jaini, 2017. "Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-32, February.
    8. Jian K Liu & Tim Gollisch, 2015. "Spike-Triggered Covariance Analysis Reveals Phenomenological Diversity of Contrast Adaptation in the Retina," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-30, July.
    9. Maxim Volgushev & Vladimir Ilin & Ian H Stevenson, 2015. "Identifying and Tracking Simulated Synaptic Inputs from Neuronal Firing: Insights from In Vitro Experiments," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-31, March.

    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. Jian K Liu & Tim Gollisch, 2015. "Spike-Triggered Covariance Analysis Reveals Phenomenological Diversity of Contrast Adaptation in the Retina," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-30, July.
    2. David Pérez-González & Olga Hernández & Ellen Covey & Manuel S Malmierca, 2012. "GABAA-Mediated Inhibition Modulates Stimulus-Specific Adaptation in the Inferior Colliculus," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-14, March.
    3. Omer Mano & Damon A Clark, 2017. "Graphics Processing Unit-Accelerated Code for Computing Second-Order Wiener Kernels and Spike-Triggered Covariance," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-11, January.
    4. Ivar L Thorson & Jean Liénard & Stephen V David, 2015. "The Essential Complexity of Auditory Receptive Fields," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-33, December.
    5. Ross S Williamson & Maneesh Sahani & Jonathan W Pillow, 2015. "The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-31, April.
    6. Sean T Kelly & Jens Kremkow & Jianzhong Jin & Yushi Wang & Qi Wang & Jose-Manuel Alonso & Garrett B Stanley, 2014. "The Role of Thalamic Population Synchrony in the Emergence of Cortical Feature Selectivity," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-13, January.
    7. Eva R M Joosten & Shihab A Shamma & Christian Lorenzi & Peter Neri, 2016. "Dynamic Reweighting of Auditory Modulation Filters," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-23, July.
    8. 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.
    9. Pilar Lopez-Llompart & G. Mathias Kondolf, 2016. "Encroachments in floodways of the Mississippi River and Tributaries Project," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(1), pages 513-542, March.
    10. Cheng, Jianquan & Bertolini, Luca, 2013. "Measuring urban job accessibility with distance decay, competition and diversity," Journal of Transport Geography, Elsevier, vol. 30(C), pages 100-109.
    11. M. De Donno & M. Pratelli, 2006. "A theory of stochastic integration for bond markets," Papers math/0602532, arXiv.org.
    12. Prilly Oktoviany & Robert Knobloch & Ralf Korn, 2021. "A machine learning-based price state prediction model for agricultural commodities using external factors," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1063-1085, December.
    13. Michelle Sheran Sylvester, 2007. "The Career and Family Choices of Women: A Dynamic Analysis of Labor Force Participation, Schooling, Marriage and Fertility Decisions," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 10(3), pages 367-399, July.
    14. Henrekson, Magnus & Johansson, Dan, 2010. "Firm Growth, Institutions and Structural Transformation," Ratio Working Papers 150, The Ratio Institute.
    15. Karen K. Lewis, 2011. "Global Asset Pricing," Annual Review of Financial Economics, Annual Reviews, vol. 3(1), pages 435-466, December.
    16. DAVID M. BLAU & WILBERT van der KLAAUW, 2013. "What Determines Family Structure?," Economic Inquiry, Western Economic Association International, vol. 51(1), pages 579-604, January.
    17. Panagiota DIONYSOPOULOU & Georgios SVARNIAS & Theodore PAPAILIAS, 2021. "Total Quality Management In Public Sector, Case Study: Customs Service," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(1), pages 153-168, June.
    18. Afanasyev, Dmitriy O. & Fedorova, Elena A. & Popov, Viktor U., 2015. "Fine structure of the price–demand relationship in the electricity market: Multi-scale correlation analysis," Energy Economics, Elsevier, vol. 51(C), pages 215-226.
    19. Peter Viggo Jakobsen, 2009. "Small States, Big Influence: The Overlooked Nordic Influence on the Civilian ESDP," Journal of Common Market Studies, Wiley Blackwell, vol. 47(1), pages 81-102, January.
    20. Julie Holland Mortimer, 2007. "Price Discrimination, Copyright Law, and Technological Innovation: Evidence from the Introduction of DVDs," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(3), pages 1307-1350.

    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:pcbi00:1003143. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    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.