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Multiplicative computation in a visual neuron sensitive to looming

Author

Listed:
  • Fabrizio Gabbiani

    (California Institute of Technology
    Baylor College of Medicine)

  • Holger G. Krapp

    (California Institute of Technology
    University of Cambridge)

  • Christof Koch

    (California Institute of Technology)

  • Gilles Laurent

    (California Institute of Technology)

Abstract

Multiplicative operations are important in sensory processing1,2,3,4,5, but their biophysical implementation remains largely unknown5,6,7,8,9,10. We investigated an identified neuron (the lobula giant movement detector, LGMD, of locusts) whose output firing rate in response to looming visual stimuli has been described by two models, one of which involves a multiplication. In this model, the LGMD multiplies postsynaptically two inputs (one excitatory, one inhibitory) that converge onto its dendritic tree11,12; in the other model, inhibition is presynaptic to the LGMD13,14. By using selective activation and inactivation of pre- and postsynaptic inhibition, we show that postsynaptic inhibition has a predominant role, suggesting that multiplication is implemented within the neuron itself. Our pharmacological experiments and measurements of firing rate versus membrane potential also reveal that sodium channels act both to advance the response of the LGMD in time and to map membrane potential to firing rate in a nearly exponential manner. These results are consistent with an implementation of multiplication based on dendritic subtraction of two converging inputs encoded logarithmically, followed by exponentiation through active membrane conductances.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:nature:v:420:y:2002:i:6913:d:10.1038_nature01190
    DOI: 10.1038/nature01190
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    Citations

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    Cited by:

    1. 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.
    2. 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).
    3. 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.
    4. 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.
    5. 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.
    6. Xaq Pitkow & Haim Sompolinsky & Markus Meister, 2007. "A Neural Computation for Visual Acuity in the Presence of Eye Movements," PLOS Biology, Public Library of Science, vol. 5(12), pages 1-14, December.
    7. Matthias S Keil, 2015. "Dendritic Pooling of Noisy Threshold Processes Can Explain Many Properties of a Collision-Sensitive Visual Neuron," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-17, October.
    8. Jules Tagne Fossi & Vandi Deli & Hélène Carole Edima & Zeric Tabekoueng Njitacke & Florent Feudjio Kemwoue & Jacques Atangana, 2022. "Phase synchronization between two thermo-photoelectric neurons coupled through a Josephson Junction," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(4), pages 1-17, April.

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