IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1002625.html
   My bibliography  Save this article

Unifying Time to Contact Estimation and Collision Avoidance across Species

Author

Listed:
  • Matthias S Keil
  • Joan López-Moliner

Abstract

The -function and the -function are phenomenological models that are widely used in the context of timing interceptive actions and collision avoidance, respectively. Both models were previously considered to be unrelated to each other: is a decreasing function that provides an estimation of time-to-contact (ttc) in the early phase of an object approach; in contrast, has a maximum before ttc. Furthermore, it is not clear how both functions could be implemented at the neuronal level in a biophysically plausible fashion. Here we propose a new framework – the corrected modified Tau function – capable of predicting both -type (“”) and -type (“”) responses. The outstanding property of our new framework is its resilience to noise. We show that can be derived from a firing rate equation, and, as , serves to describe the response curves of collision sensitive neurons. Furthermore, we show that predicts the psychophysical performance of subjects determining ttc. Our new framework is thus validated successfully against published and novel experimental data. Within the framework, links between -type and -type neurons are established. Therefore, it could possibly serve as a model for explaining the co-occurrence of such neurons in the brain. Author Summary: In 1957, Sir Fred Hoyle published a science fiction novel in which he described humanity's encounter with an extraterrestrial life form. It came in the shape of a huge black cloud which approached the earth. Hoyle proposed a formula (“”) for computing the remaining time until contact (“ttc”) of the cloud with the earth. Nowadays in real science, serves as a model for ttc -perception for animals and humans, although it is not entirely undisputed. For instance, seems to be incompatible with a collision-sensitive neuron in locusts (the Lobula Giant Movement Detector or LGMD neuron). LGMD neurons are instead better described by the -function, which differs from . Here we propose a generic model (“”) that contains and as special cases. The validity of the model was confirmed with a psychophysical experiment. Also, we fitted many published response curves of LGMD neurons with our new model and with the -function. Both models fit these response curves well, and we thus can conclude that and possibly result from a generic neuronal circuit template such as it is described by .

Suggested Citation

  • Matthias S Keil & Joan López-Moliner, 2012. "Unifying Time to Contact Estimation and Collision Avoidance across Species," PLOS Computational Biology, Public Library of Science, vol. 8(8), pages 1-1, August.
  • Handle: RePEc:plo:pcbi00:1002625
    DOI: 10.1371/journal.pcbi.1002625
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002625
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002625&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1002625?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. Fabrizio Gabbiani & Holger G. Krapp & Christof Koch & Gilles Laurent, 2002. "Multiplicative computation in a visual neuron sensitive to looming," Nature, Nature, vol. 420(6913), pages 320-324, November.
    2. Christopher M. Harris & Daniel M. Wolpert, 1998. "Signal-dependent noise determines motor planning," Nature, Nature, vol. 394(6695), pages 780-784, August.
    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. Shogo Yonekura & Yasuo Kuniyoshi, 2017. "Bodily motion fluctuation improves reaching success rate in a neurophysical agent via geometric-stochastic resonance," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-16, December.
    2. Shih-Wei Wu & Maria F Dal Martello & Laurence T Maloney, 2009. "Sub-Optimal Allocation of Time in Sequential Movements," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-13, December.
    3. Max Berniker & Megan K O’Brien & Konrad P Kording & Alaa A Ahmed, 2013. "An Examination of the Generalizability of Motor Costs," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-11, January.
    4. Xie, Ying & Zhou, Ping & Yao, Zhao & Ma, Jun, 2022. "Response mechanism in a functional neuron under multiple stimuli," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    5. Yajiao Tang & Junkai Ji & Yulin Zhu & Shangce Gao & Zheng Tang & Yuki Todo, 2019. "A Differential Evolution-Oriented Pruning Neural Network Model for Bankruptcy Prediction," Complexity, Hindawi, vol. 2019, pages 1-21, August.
    6. Lionel Rigoux & Emmanuel Guigon, 2012. "A Model of Reward- and Effort-Based Optimal Decision Making and Motor Control," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-13, October.
    7. Yanhao Ren & Qiang Luo & Wenlian Lu, 2023. "Synchronization Analysis of Linearly Coupled Systems with Signal-Dependent Noises," Mathematics, MDPI, vol. 11(10), pages 1-15, May.
    8. Christopher J Hasson & Zhaoran Zhang & Masaki O Abe & Dagmar Sternad, 2016. "Neuromotor Noise Is Malleable by Amplifying Perceived Errors," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-28, August.
    9. Seth W. Egger & Stephen G. Lisberger, 2022. "Neural structure of a sensory decoder for motor control," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    10. Ashesh Vasalya & Gowrishankar Ganesh & Abderrahmane Kheddar, 2018. "More than just co-workers: Presence of humanoid robot co-worker influences human performance," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-19, November.
    11. Josh Merel & Donald M Pianto & John P Cunningham & Liam Paninski, 2015. "Encoder-Decoder Optimization for Brain-Computer Interfaces," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-25, June.
    12. Yan Wang & Yue Gong & Shenming Huang & Xuechao Xing & Ziyu Lv & Junjie Wang & Jia-Qin Yang & Guohua Zhang & Ye Zhou & Su-Ting Han, 2021. "Memristor-based biomimetic compound eye for real-time collision detection," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    13. Nidhi Seethapathi & Barrett C. Clark & Manoj Srinivasan, 2024. "Exploration-based learning of a stabilizing controller predicts locomotor adaptation," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    14. Maxime Teremetz & Isabelle Amado & Narjes Bendjemaa & Marie-Odile Krebs & Pavel G Lindberg & Marc A Maier, 2014. "Deficient Grip Force Control in Schizophrenia: Behavioral and Modeling Evidence for Altered Motor Inhibition and Motor Noise," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-11, November.
    15. Frederic Danion & Raoul M Bongers & Reinoud J Bootsma, 2014. "The Trade-Off between Spatial and Temporal Variabilities in Reciprocal Upper-Limb Aiming Movements of Different Durations," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-10, May.
    16. Wei Zhang & Sasha Reschechtko & Barry Hahn & Cynthia Benson & Elias Youssef, 2019. "Force-stabilizing synergies can be retained by coordinating sensory-blocked and sensory-intact digits," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-17, December.
    17. Julian J Tramper & Bart van den Broek & Wim Wiegerinck & Hilbert J Kappen & Stan Gielen, 2012. "Time-Integrated Position Error Accounts for Sensorimotor Behavior in Time-Constrained Tasks," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-10, March.
    18. Konrad P Körding & Izumi Fukunaga & Ian S Howard & James N Ingram & Daniel M Wolpert, 2004. "A Neuroeconomics Approach to Inferring Utility Functions in Sensorimotor Control," PLOS Biology, Public Library of Science, vol. 2(10), pages 1-1, September.
    19. Pierre Morel & Philipp Ulbrich & Alexander Gail, 2017. "What makes a reach movement effortful? Physical effort discounting supports common minimization principles in decision making and motor control," PLOS Biology, Public Library of Science, vol. 15(6), pages 1-23, June.
    20. Sergi Bermúdez i Badia & Ulysses Bernardet & Paul F M J Verschure, 2010. "Non-Linear Neuronal Responses as an Emergent Property of Afferent Networks: A Case Study of the Locust Lobula Giant Movement Detector," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-15, March.

    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:pcbi00:1002625. 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.