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REVIEW 4 major objections 5 minor 128 references

A Bio-Inspired Research Paradigm of Collision Perception Neurons Enabling Neuro-Robotic Integration: The LGMD Case

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A half-century of studying one locust collision neuron has produced a mature cycle in which biology inspires robots and robot behavior feeds back into neuroscience.

desk verdict Useful review of LGMD collision-detection research, but the 'robots validate biology' claim is overstated relative to the paper's own caveats. read the letter →

arxiv 2501.02982 v2 pith:AB4SPX72 submitted 2025-01-06 cs.NE cs.AIq-bio.NC

Ziyan Qin , Jigen Peng , Shigang Yue , Qinbing Fu This is my paper · ORCID
classification cs.NEcs.AIq-bio.NC
keywords LGMDloomingdetectioncollisionavoidancebio-inspiredroboticsinsectvisionneuromorphicspikefrequencyadaptationneuro-roboticintegration
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This review argues that the locust's lobula giant movement detectors (LGMDs), large collision-selective neurons in the optic lobe, now anchor a complete research loop: anatomy and physiology inspired computational models, those models became low-power collision detectors on ground and aerial robots, and the robots' real-world behavior is claimed to validate the biology while generating new hypotheses for neuroscientists. The authors present this as a mature paradigm that works across neuroscience, modeling, and robotics, and as a template for other motion-sensitive neurons. A sympathetic reader should take away that half a century of work on one large, accessible neuron has produced parsimonious, data-free collision detection that competes with deep-learning approaches, while the review itself also shows how much of the locust's circuitry remains unknown.

What carries the argument

The load-bearing machinery is the LGMD neuron pair. LGMD1, with three dendritic fields, responds to approaching objects under both brightening and darkening contrast and sends spikes to the descending contralateral movement detector that triggers escape; LGMD2, with a single large dendrite, responds selectively to dark approaching objects. The core functional identity is the excitation-inhibition race: as an object expands on the retina, excitation encoding angular velocity grows while delayed inhibition, including feed-forward inhibition that encodes angular size, trails behind, so the neuron fires strongly only for objects on a collision course. The computational workhorses are the η-function $\eta(t) = C \theta'(t-\delta) \exp(-\alpha \theta(t-\delta))$ for the single-neuron tradition and the four-layer network of critical image cues (P), excitation (E), lateral inhibition (I), and feed-forward inhibition (F) units for the video-capable tradition. In robotics these networks run as compact embedded vision systems, usually augmented with ON/OFF pathways, spike frequency adaptation, and feedback loops, converting camera frames directly into avoidance commands.

What would settle it

One decisive check is systematic: present looming squares over a wide range of size-to-speed ratios, including the regime with $l/|v| \leq 5$ ms, record LGMD1 spikes in a locust, run the same image sequences through the η-function and the four-layer network, and compare the peak firing time relative to the moment of maximum retinal image size; the paper itself notes that the η-function peaks after the maximum in that regime while some neuron recordings peak before it, so a reliable mismatch there would undercut the claim that robotic success validates the biological models.

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Extended reading notes

Core claim

The central claim is that LGMD research has closed the loop between brain and machine. Biological studies since the 1970s produced two modeling traditions: the η-function, which treats the neuron as a single computation multiplying a signal for angular velocity by a decaying exponential of angular size and peaking at a fixed delay after a threshold retinal size; and the four-layer neural network, which generates looming selectivity from a race between excitation and delayed lateral and feed-forward inhibition and can read camera video directly. When these models are embedded in wheeled robots, quadcopters, and event-camera systems, their demonstrated collision-free navigation in unconstrained settings is presented as direct evidence that the models capture the neuron's looming selectivity. The paper further claims that the model's shortcomings, such as the need for artificial grouping or denoising layers, point toward undiscovered biological mechanisms rather than invalidating the approach.

Load-bearing premise

The load-bearing premise is that when a robot driven by an LGMD model dodges obstacles, that success counts as evidence that the model faithfully captures how the locust's neuron actually works, and nearly every claim that the paradigm feeds back into neuroscience rests on this identification.

Editorial extensions

If this is right

  • LGMD-based detectors can run on small, low-power robots with ordinary cameras, avoiding the map building, object recognition, and large training sets that deep-learning collision detectors require.
  • Combining LGMD1 and LGMD2 yields collision detection across both dark and bright environments because LGMD2 specifically sees dark approaching objects while LGMD1 covers both contrast polarities.
  • Each biologically discovered feature that is added to the models, such as ON/OFF competition or spike frequency adaptation, improves real-world selectivity and thereby strengthens the claim that the models track the real circuit.
  • The same closed research loop should transfer to other insect motion neurons with large, accessible dendrites, such as lobula plate tangential cells and small-target motion detectors, which already inform optic-flow and target-tracking methods.
  • Event-based cameras and thermal cameras extend LGMD models to low-light and high-speed conditions, pointing toward neuromorphic collision sensors for autonomous vehicles.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If robot success is accepted as biological validation, the same logic implies that model failures in noisy, dynamic scenes should keep predicting specific missing mechanisms in the locust circuit; a stronger version would predict new anatomical connections before the connectome confirms them.
  • The paper's own concession that current models 'fall short' of biological adaptability suggests that the mature stage is best read as an engineering loop, not proof of biological fidelity, and that closing that gap probably requires a locust connectome of the kind already built for the fruit fly.
  • The η-function and the four-layer network may be closer than they appear: the former is a reduced mathematical description of the same excitation-inhibition race the latter implements, so a unifying model using leaky integrate-and-fire dynamics could bridge the two traditions.
  • The conditions that let LGMD research close its loop are identifiable large neurons, reliable looming stimuli in the laboratory, and a real-world application matching the neuron's selectivity; other candidate neurons should be screened for these three properties before the paradigm is transplanted.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This paper is a review of research on the locust lobula giant movement detectors (LGMD1 and LGMD2), spanning neuroscience, computational modeling, and robotics. The authors survey the biological morphology and response properties of LGMD neurons, the classic η-function and four-layer neural network models, and the many robotic implementations (ground, aerial, neuromorphic, event-based). They argue that this body of work constitutes a mature 'bio-inspired research paradigm' in which robotic embodiments validate the biological models and generate feedback that advances neuroscience. The review also discusses open controversies in LGMD circuitry and proposes a hypothetical processing pathway with explicitly marked unknowns.

Significance. As a survey, the paper is useful and largely accurate: it covers the recent literature well, includes a candid account of unresolved debates (Section 2.3), and identifies concrete gaps between models and biology. The strongest contribution would be the demonstration that a single identified neuron's principles yield parsimonious, low-power collision detectors while also informing neuroscience. That contribution is not yet secured: the central inference from robot success to biological fidelity is asserted rather than argued with evidence, and the paper's own discussion undermines it. The review also contains noticeable factual and presentation errors (e.g., 'invertebrates' for 'vertebrates'), which should be corrected.

major comments (4)
  1. [Abstract; §4; §5] The paper's central claim—that the LGMD paradigm 'has reached a mature stage' and that robot behavior 'has validated the established neuroscience and anatomical findings'—is not supported by the evidence presented. The manuscript itself states in §5 that 'these models have succeeded in validating some neuroscience findings, they ultimately fall short in providing the adaptability and robustness of biological systems' and 'leave neuro-scientists with more questions than answers.' The review needs to either lower the epistemic claim to 'hypothesis generation' or provide specific validation evidence (e.g., quantitative model–neuron comparisons under matched stimuli). Without this, the abstract and §4 overstate what the cited work establishes.
  2. [§3.1; §3.2; §4; §5] The load-bearing inference that robotic systems 'directly demonstrated a robustness in looming selectivity comparable to that observed in biological LGMD neurons' (§4) is undermined by the non-biological mechanisms that the models incorporate. The Yue–Rind network adds an artificial grouping layer [62]; the LGMD2 model replaces FFI with adaptive inhibition [32]; and §5 mentions average-value denoising layers. These engineered components can plausibly account for the task performance even if the biological hypotheses are wrong. To validate the biological fidelity claim, the authors would need to cite ablation experiments showing that these mechanisms are unnecessary for performance in conditions that distinguish the biological model from alternatives, or compare internal model variables with intracellular recordings. No such evidence is provided.
  3. [§2.3; §4] The assertion that the strongest feedback to neuroscience from the LGMD2 model is 'the probable existence of ON/OFF-contrast encoding neurons or neural pathways prior to the dendrites of LGMDs' is presented as an established result, but the paper's own §2.3 states that 'there is very limited evidence for whether/where the ON/OFF channels exist in locust's visual circuitry,' and Fig. 4 marks these pathways with question marks. This should be framed as a testable prediction from modeling, which is a legitimate but significantly weaker form of feedback than 'validation.' The current framing conflates hypothesis generation with empirical confirmation.
  4. [§3.1; §4] The 'mature paradigm' claim depends on a set of robotic demonstrations that are largely from the authors' own group (e.g., [13, 32, 68, 83]); the review does not document any independent replication of the key results, nor does it report negative or failed cases. For a field claimed to be mature, the absence of independent confirmation is a significant gap. The review should explicitly acknowledge this limitation or temper the maturity claim.
minor comments (5)
  1. [Introduction] The sentence 'a significantly smaller number than invertebrates' should read 'vertebrates'; the comparison is between the locust's ~150,000 neurons and vertebrate brains, not invertebrates.
  2. [§2.2] There is a typo 'umanned aerial vehicles' and a grammatical error in 'the η-function can serves as the intrinsic computational mechanism'; both should be corrected.
  3. [§5] 'Drosophlia' is misspelled in three places; it should be 'Drosophila'.
  4. [§2.2] The phrase 'the first four-layered LGMD1 neural network proposed by Rind et al.' cites [8], which is authored by Rind and Simmons; please verify the attribution.
  5. [§3.1 and §2.3] The term 'UA Vs' is written with an inconsistent space; use 'UAVs' throughout. Also, in §2.3 the sentence 'the proposed four layers may not anatomically correspond to the four layers of the locust's stratified optic lobe' is repetitive and should be reworded.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the review's neuro-robotic loop claim is an interpretive summary, not a derivation, and the paper itself concedes the models' non-biological mechanisms.

full rationale

This is a review paper rather than a derivation chain, and no load-bearing result reduces to its own inputs by construction. The closest passage is Section 4's claim that 'The performance of LGMD-based robotic systems during complex real-world interaction tasks has directly demonstrated a robustness in looming selectivity comparable to that observed in biological LGMD neurons.' That is an empirical and interpretive inference, not a definitional identity: robot obstacle-avoidance behavior is an externally observed system behavior, and the paper does not define 'biological fidelity' as 'robot success.' Moreover, the paper itself undercuts any hidden circularity by conceding in Section 5 that 'LGMD-based models often achieve timely and accurate collision detection through supplementary mechanisms... they still fall short of replicating the adaptability and robustness exhibited by real LGMD neurons,' and by acknowledging artificial grouping layers, average-value denoising layers, and adaptive inhibition with no clear biological counterpart. The main self-citation pattern is structural: the claim that LGMD2 modeling provides 'the strongest feedback to neuroscience' cites [24,32], both authored by Fu and co-workers, but it is explicitly framed as 'the probable existence of ON/OFF-contrast encoding neurons or neural pathways' and as a 'strong hypothesis to neuroscience', not as an established result used to close a derivation. No uniqueness theorem is imported from the authors, no ansatz is smuggled in via citation, no fitted parameter is relabeled as a prediction, and no known result is merely renamed. The single point reflects the review's heavy reliance on the authors' own prior modeling and robotic work, but that reliance is not load-bearing circularity in the sense required here.

Assumptions & free parameters 0 free parameters · 3 assumptions · 2 invented entities

The central claim is a review-level interpretation rather than a derivation, so it has no fitted parameters of its own. Its support rests on domain assumptions about whether robot behavior validates biological models and whether Drosophila connectomics can be projected onto locusts, plus two hypothesized entities explicitly flagged as uncertain in the paper.

assumptions (3)
  • domain assumption Successful collision avoidance by LGMD-based robots demonstrates that the models capture the biological mechanisms underlying LGMD looming selectivity.
    Section 4 first paragraph asserts robot performance 'has directly demonstrated a robustness in looming selectivity comparable to that observed in biological LGMD neurons.' The paper later concedes models rely on non-biological mechanisms, so this is an assumed transfer.
  • domain assumption The Drosophila connectome can be transferred to locusts through homology to explain LGMD circuitry.
    Section 5 states anatomical similarities between locust and Drosophila optic lobes make the fly connectome 'a valuable foundation for understanding the complete neural signaling pathway to the LGMDs.' This extrapolation is unverified.
  • domain assumption Classic LGMD models (eta-function, Rind four-layer network) remain valid descriptions of the neuron's computation.
    Section 2.2 presents Hatsopoulos's eta-function and Rind and Simmons's network as established, relying on prior literature [7,8,27,34-38]. Section 2.3 notes open controversies, so the models may not be complete.
invented entities (2)
  • Separate neuron in the lamina layer mediating global inhibition
    purpose: Assumed in the hypothesized processing diagram (Fig. 4) to normalize LGMD excitation.
    Section 2.3 and Fig. 4: 'we assume that this inhibition is mediated by a separate neuron in the lamina layer.' No direct physiological evidence is cited; modeling work [22] motivates it.
  • ON/OFF-contrast encoding neurons or pathways prior to LGMD dendrites
    purpose: Proposed as 'the strongest feedback to neuroscience' from LGMD2 modeling.
    Section 4: 'the strongest feedback to neuroscience is the probable existence of ON/OFF-contrast encoding neurons or neural pathways prior to the dendrites of LGMDs [24,32].' Not yet confirmed in locust physiology.

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Cite this review

Pith. "Pith review of A Bio-Inspired Research Paradigm of Collision Perception Neurons Enabling Neuro-Robotic Integration: The LGMD Case." pith.science (2026). https://pith.science/paper/AB4SPX72

@misc{pith2026250102982,
  author       = {Pith},
  title        = {Pith review of: A Bio-Inspired Research Paradigm of Collision Perception Neurons Enabling Neuro-Robotic Integration: The LGMD Case},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AB4SPX72}},
  note         = {Machine review of arXiv:2501.02982}
}
read the original abstract

Compared to human vision, locust visual systems excel at rapid and precise collision detection, despite relying on only hundreds of thousands of neurons organized through a few neuropils. This efficiency makes them an attractive model system for developing artificial collision-detecting systems. Specifically, researchers have identified collision-selective neurons in the locust's optic lobe, called lobula giant movement detectors (LGMDs), which respond specifically to approaching objects. Research upon LGMD neurons began in the early 1970s. Initially, due to their large size, these neurons were identified as motion detectors, but their role as looming detectors was recognized over time. Since then, progress in neuroscience, computational modeling of LGMD's visual neural circuits, and LGMD-based robotics have advanced in tandem, each field supporting and driving the others. Today, with a deeper understanding of LGMD neurons, LGMD-based models have significantly improved collision-free navigation in mobile robots including ground and aerial robots. This review highlights recent developments in LGMD research from the perspectives of neuroscience, computational modeling, and robotics. It emphasizes a biologically plausible research paradigm, where insights from neuroscience inform real-world applications, which would in turn validate and advance neuroscience. With strong support from extensive research and growing application demand, this paradigm has reached a mature stage and demonstrates versatility across different areas of neuroscience research, thereby enhancing our understanding of the interconnections between neuroscience, computational modeling, and robotics. Furthermore, this paradigm would shed light upon the modeling and robotic research into other motion-sensitive neurons or neural circuits.

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Reference graph

Works this paper leans on

128 extracted references · 62 canonical work pages

  1. [62]

    IEEE Transactions on Neural Networks 17(3), 705–716 (2006) https://doi.org/10.1109/TNN.2006

    Yue, S., Rind, F.C.: Collision detection in complex dynamic scenes using an lgmd-based visual neural network with feature enhancement. IEEE Transactions on Neural Networks 17(3), 705–716 (2006) https://doi.org/10.1109/TNN.2006. 873286

    work page doi:10.1109/tnn.2006 2006

  2. [32]

    IEEE Trans- actions on Cybernetics 50(12), 5074–5088 (2020) https://doi.org/10.1109/tcyb

    Fu, Q., Hu, C., Peng, J., Rind, F.C., Yue, S.: A robust collision perception visual neural network with specific selectivity to darker objects. IEEE Trans- actions on Cybernetics 50(12), 5074–5088 (2020) https://doi.org/10.1109/tcyb. 2019.2946090

    arXiv 2020

  3. [1]

    Canadian Journal of Zoology 55(8), 1372–1376 (1977) https://doi.org/10.1139/z77-179

    Schlotterer, G.R.: Response of the locust descending movement detector neuron to rapidly approaching and withdrawing visual stimuli. Canadian Journal of Zoology 55(8), 1372–1376 (1977) https://doi.org/10.1139/z77-179

    work page doi:10.1139/z77-179 1977

  4. [2]

    Journal of Neurophysiology 77(2), 1029–1033 (1997) https: //doi.org/10.1152/jn.1997.77.2.1029 https://doi.org/10.1152/jn.1997.77.2.1029

    Rind, F.C., Simmons, P.J.: Signaling of object approach by the dcmd neu- ron of the locust. Journal of Neurophysiology 77(2), 1029–1033 (1997) https: //doi.org/10.1152/jn.1997.77.2.1029 https://doi.org/10.1152/jn.1997.77.2.1029. PMID: 9065867

    work page doi:10.1152/jn.1997.77.2.1029 1997

  5. [3]

    Rowell, C.H.F., O’shea, M.: The neuronal basis of a sensory analyser, the acri- did movement detector system. i. effects of simple incremental and decremental stimuli in light and dark adapted animals. Journal of Experimental Biology 65(2), 273–288 (1976) https://doi.org/10.1242/jeb.65.2.273

    work page doi:10.1242/jeb.65.2.273 1976

  6. [4]

    O’Shea, M., Rowell, C.H.: The neuronal basis of a sensory analyser, the acri- did movement detector system. ii. response decrement, convergence, and the nature of the excitatory afferents to the fan-like dendrites of the lgmd. Journal of Experimental Biology 65(2), 289–308 (1976) https://doi.org/10.1242/jeb.65. 2.289 22

    work page doi:10.1242/jeb.65 1976

  7. [5]

    Fraser Rowell, C.H., O’Shea, M.: Neuronal basis of a sensory analyser, the acridid movement detector system. iii. control of response amplitude by tonic lateral inhibition. Journal of Experimental Biology 65(3), 617–25 (1976) https://doi. org/10.1242/jeb.65.3.617

    work page doi:10.1242/jeb.65.3.617 1976

  8. [6]

    Fraser Rowell, C.H., O’Shea, M., Williams, J.L.: The neuronal basis of a sensory analyser, the acridid movement detector system. iv. the preference for small field stimuli. Journal of Experimental Biology 68, 157–85 (1977) https://doi.org/10. 1242/jeb.68.1.157

    1977

Show all 128 references
  1. [7]

    Science 270(5238), 1000–1003 (1995) https://doi.org/10.1126/science.270.5238.1000

    Hatsopoulos, N., Gabbiani, F., Laurent, G.: Elementary computation of object approach by a wide-field visual neuron. Science 270(5238), 1000–1003 (1995) https://doi.org/10.1126/science.270.5238.1000

    1995

  2. [8]

    Journal of Neurophysiology 75(3), 967–985 (1996) https://doi.org/10.1152/jn.1996.75.3.967

    Rind, F.C., Bramwell, D.I.: Neural network based on the input organization of an identified neuron signaling impending collision. Journal of Neurophysiology 75(3), 967–985 (1996) https://doi.org/10.1152/jn.1996.75.3.967

    1996 doi

  3. [9]

    Journal of Comparative Physiology A 180(3), 203–214 (1997) https://doi.org/10.1007/ s003590050041

    Simmons, P.J., Rind, F.C.: Responses to object approach by a wide field visual neurone, the lgmd2 of the locust: Characterization and image cues. Journal of Comparative Physiology A 180(3), 203–214 (1997) https://doi.org/10.1007/ s003590050041

    1997

  4. [10]

    Robotics and Autonomous Systems 30(1), 17–38 (2000) https://doi.org/10.1016/S0921-8890(99)00063-9

    Blanchard, M., Rind, F.C., Verschure, P.F.M.J.: Collision avoidance using a model of the locust lgmd neuron. Robotics and Autonomous Systems 30(1), 17–38 (2000) https://doi.org/10.1016/S0921-8890(99)00063-9

    2000 doi

  5. [11]

    Developmental Neurobiology 74(11), 1078–1095 (2014) https://doi.org/10.1002/dneu.22184 https://onlinelibrary.wiley.com/doi/pdf/10.1002/dneu.22184

    Sztarker, J., Rind, F.C.: A look into the cockpit of the develop- ing locust: Looming detectors and predator avoidance. Developmental Neurobiology 74(11), 1078–1095 (2014) https://doi.org/10.1002/dneu.22184 https://onlinelibrary.wiley.com/doi/pdf/10.1002/dneu.22184

    2014 doi

  6. [12]

    Journal of Neurophysiology 94(3), 2150–2161 (2005) https://doi.org/10.1152/jn.00411.2005

    Gabbiani, F., Cohen, I., Laurent, G.: Time-dependent activation of feed-forward inhibition in a looming-sensitive neuron. Journal of Neurophysiology 94(3), 2150–2161 (2005) https://doi.org/10.1152/jn.00411.2005

    2005

  7. [14]

    IEEE Access 8, 108896–108912 (2020) https://doi.org/10.1109/ACCESS.2020

    Fu, Q., Wang, H., Peng, J., Yue, S.: Improved collision perception neuronal system model with adaptive inhibition mechanism and evolutionary learning. IEEE Access 8, 108896–108912 (2020) https://doi.org/10.1109/ACCESS.2020. 3001396

    2020 doi

  8. [15]

    IEEE Transactions on Neural Networks and Learning Systems 34(5), 2539–2553 (2023) https://doi.org/10.1109/TNNLS.2021.3106946

    Zhao, J., Wang, H., Bellotto, N., Hu, C., Peng, J., Yue, S.: Enhancing lgmd’s 23 looming selectivity for uav with spatial–temporal distributed presynaptic con- nections. IEEE Transactions on Neural Networks and Learning Systems 34(5), 2539–2553 (2023) https://doi.org/10.1109/T...

    2023

  9. [16]

    eLife 11, 79772 (2022) https://doi.org/10.7554/eLife.79772

    Dewell, R.B., Zhu, Y., Eisenbrandt, M., Morse, R., Gabbiani, F.: Contrast polarity-specific mapping improves efficiency of neuronal computation for colli- sion detection. eLife 11, 79772 (2022) https://doi.org/10.7554/eLife.79772

    2022 doi

  10. [17]

    Journal of Comparative Neurology 530(2), 518–536 (2022) https://doi.org/10.1002/cne

    Wernitznig, S., Rind, F.C., Zankel, A., Bock, E., G¨ utl, D., Hobusch, U., Nikolic, M., Pargger, L., Pritz, E., Radulovi´ c, S., Sele, M., Summerauer, S., P¨ olt, P., Leitinger, G.: The complex synaptic pathways onto a looming-detector neu- ron revealed using serial block-face...

    2022 doi

  11. [18]

    Journal of Neurophysiology 93(4), 2240–2253 (2005) https://doi.org/10.1152/jn.00965.2004

    Krapp, H.G., Gabbiani, F.: Spatial distribution of inputs and local receptive field properties of a wide-field, looming sensitive neuron. Journal of Neurophysiology 93(4), 2240–2253 (2005) https://doi.org/10.1152/jn.00965.2004

    2005

  12. [19]

    Nature Neuroscience 12(3), 318–326 (2009) https://doi.org/10.1038/nn.2259

    Peron, S., Gabbiani, F.: Spike frequency adaptation mediates looming stimulus selectivity in a collision-detecting neuron. Nature Neuroscience 12(3), 318–326 (2009) https://doi.org/10.1038/nn.2259

    2009 doi

  13. [20]

    eLife 7 (2018) https://doi.org/10.7554/eLife.34238

    Dewell, R.B., Gabbiani, F.: Biophysics of object segmentation in a collision- detecting neuron. eLife 7 (2018) https://doi.org/10.7554/eLife.34238

    2018 doi

  14. [21]

    PLOS Computational Biology 6(3), 1000701 (2010) https://doi.org/10.1371/journal.pcbi.1000701

    Badia, S., Bernardet, U., Verschure, P.F.M.J.: Non-linear neuronal responses as an emergent property of afferent networks: A case study of the locust lobula giant movement detector. PLOS Computational Biology 6(3), 1000701 (2010) https://doi.org/10.1371/journal.pcbi.1000701

    2010 doi

  15. [22]

    Biological Cybernetics 115(3), 245–265 (2021) https://doi.org/10.1007/ s00422-021-00876-8

    Olson, E.G.N., Wiens, T.K., Gray, J.R.: A model of feedforward, global, and lateral inhibition in the locust visual system predicts responses to looming stimuli. Biological Cybernetics 115(3), 245–265 (2021) https://doi.org/10.1007/ s00422-021-00876-8

    2021

  16. [23]

    Artificial Life 25(3), 263–311 (2019) https://doi.org/10.1162/artl a 00297

    Fu, Q., Wang, H., Hu, C., Yue, S.: Towards computational models and appli- cations of insect visual systems for motion perception: A review. Artificial Life 25(3), 263–311 (2019) https://doi.org/10.1162/artl a 00297

    2019 doi

  17. [24]

    Neural Networks 165, 1–18 (2023) https://doi.org/10.1016/j.neunet.2023.05.031

    Fu, Q.: Motion perception based on on/off channels: A survey. Neural Networks 165, 1–18 (2023) https://doi.org/10.1016/j.neunet.2023.05.031

    2023 doi

  18. [25]

    Journal of comparative physiology 91(3), 257–266 (1974) https://doi.org/10

    O’Shea, M., Williams, J.L.D.: The anatomy and output connection of a locust visual interneurone; the lobular giant movement detector (LGMD) neurone. Journal of comparative physiology 91(3), 257–266 (1974) https://doi.org/10. 1007/BF00698057 24

    1974

  19. [26]

    Microscopy Research and Technique 56(4), 256–269 (2002) https://doi

    Rind, F.C.: Motion detectors in the locust visual system: From biology to robot sensors. Microscopy Research and Technique 56(4), 256–269 (2002) https://doi. org/10.1002/jemt.10029

    2002 doi

  20. [27]

    Annual Review of Neuroscience 34(1), 1–19 (2011) https://doi.org/ 10.1146/annurev-neuro-061010-113632

    Fotowat, H., Gabbiani, F.: Collision detection as a model for sensory-motor integration. Annual Review of Neuroscience 34(1), 1–19 (2011) https://doi.org/ 10.1146/annurev-neuro-061010-113632

    2011 doi

  21. [28]

    Trends in Neurosciences 22(5), 215–220 (1999) https://doi.org/10

    Rind, F.C., Simmons, P.J.: Seeing what is coming: building collision-sensitive neurones. Trends in Neurosciences 22(5), 215–220 (1999) https://doi.org/10. 1016/S0166-2236(98)01332-0

    1999

  22. [29]

    Simmons, P.J., Rind, F.C.: Orthopteran dcmd neuron: a reevaluation of responses to moving objects. ii. critical cues for detecting approaching objects. Journal of Neurophysiology 68(5), 1667–1682 (1992) https://doi.org/10.1152/ jn.1992.68.5.1667

    1992

  23. [30]

    Physiological Reports 4(10) (2016) https://doi.org/10.14814/phy2

    Yakubowski, J.M., McMillan, G.A., Gray, J.R.: Background visual motion affects responses of an insect motion-sensitive neuron to objects deviating from a colli- sion course. Physiological Reports 4(10) (2016) https://doi.org/10.14814/phy2. 12801

    2016 doi

  24. [31]

    In: 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), pp

    Fu, Q., Yue, S.: Modelling lgmd2 visual neuron system. In: 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6. https://doi.org/10.1109/MLSP.2015.7324313

    2015

  25. [33]

    Current Opinion in Insect Science 63, 101180 (2024) https://doi.org/10.1016/j.cois.2024.101180

    Rind, F.C.: Recent advances in insect vision in a 3D world: looming stimuli and escape behaviour. Current Opinion in Insect Science 63, 101180 (2024) https://doi.org/10.1016/j.cois.2024.101180

    2024

  26. [34]

    The Journal of Neuroscience 19(3), 1122 (1999) https://doi.org/10.1523/JNEUROSCI.19-03-01122.1999

    Gabbiani, F., Krapp, H.G., Laurent, G.: Computation of object approach by a wide-field, motion-sensitive neuron. The Journal of Neuroscience 19(3), 1122 (1999) https://doi.org/10.1523/JNEUROSCI.19-03-01122.1999

    1999

  27. [35]

    The Journal of Neuroscience 21(1), 314–329 (2001) https://doi.org/10.1523/jneurosci.21-01-00314.2001

    Gabbiani, F., Mo, C., Laurent, G.: Invariance of angular threshold computation in a wide-field looming-sensitive neuron. The Journal of Neuroscience 21(1), 314–329 (2001) https://doi.org/10.1523/jneurosci.21-01-00314.2001

    2001

  28. [36]

    Nature 420(6913), 320–324 (2002) https: //doi.org/10.1038/nature01190 25

    Gabbiani, F., Krapp, H.G., Koch, C., Laurent, G.: Multiplicative computation in a visual neuron sensitive to looming. Nature 420(6913), 320–324 (2002) https: //doi.org/10.1038/nature01190 25

    2002 doi

  29. [37]

    Journal of Physiology-Paris 98(1-3), 19–34 (2004) https://doi.org/10.1016/j.jphysparis

    Gabbiani, F., Krapp, H.G., Hatsopoulos, N., Mo, C.-H., Koch, C., Laurent, G.: Multiplication and stimulus invariance in a looming-sensitive neuron. Journal of Physiology-Paris 98(1-3), 19–34 (2004) https://doi.org/10.1016/j.jphysparis. 2004.03.001

    2004 doi

  30. [38]

    The Journal of Neuroscience 32(14), 4923 (2012) https://doi.org/10.1523/JNEUROSCI.5777-11.2012

    Jones, P.W., Gabbiani, F.: Logarithmic compression of sensory signals within the dendritic tree of a collision-sensitive neuron. The Journal of Neuroscience 32(14), 4923 (2012) https://doi.org/10.1523/JNEUROSCI.5777-11.2012

    2012 doi

  31. [39]

    Biological Cybernetics 117(1), 129–142 (2023) https://doi.org/10.1007/ s00422-023-00961-0

    Gabbiani, F., Preuss, T., Dewell, R.B.: Approaching object acceleration differentially affects the predictions of neuronal collision avoidance mod- els. Biological Cybernetics 117(1), 129–142 (2023) https://doi.org/10.1007/ s00422-023-00961-0

    2023

  32. [40]

    The Journal of Physiology 601(19), 4355–4373 (2023) https://doi.org/10.1113/JP284022

    Dewell, R.B., Carroll-Mikhail, T., Eisenbrandt, M.R., Mendoza, A.F., Halder, B., Preuss, T., Gabbiani, F.: Convergent escape behavior from distinct visual processing of impending collision in fish and grasshoppers. The Journal of Physiology 601(19), 4355–4373 (2023) https://do...

    2023 doi

  33. [41]

    IEEE Transactions on Neural Networks and Learning Systems 34(11), 8362–8376 (2023) https:// doi.org/10.1109/TNNLS.2022.3149832

    Lei, F., Peng, Z., Liu, M., Peng, J., Cutsuridis, V., Yue, S.: A robust visual system for looming cue detection against translating motion. IEEE Transactions on Neural Networks and Learning Systems 34(11), 8362–8376 (2023) https:// doi.org/10.1109/TNNLS.2022.3149832

    2023

  34. [42]

    Trends Neurosci 22(10), 437–8 (1999) https: //doi.org/10.1016/s0166-2236(99)01478-2

    Gabbiani, F., Laurent, G., Hatsopoulos, N., Krapp, H.G.: The many ways of building collision-sensitive neurons. Trends Neurosci 22(10), 437–8 (1999) https: //doi.org/10.1016/s0166-2236(99)01478-2

    1999 doi

  35. [43]

    Journal of comparative physiology 125(1), 13–27 (1978) https://doi.org/10.1007/BF00656827

    Lillywhite, P.G.: Coupling between locust photoreceptors revealed by a study of quantum bumps. Journal of comparative physiology 125(1), 13–27 (1978) https://doi.org/10.1007/BF00656827

    1978 doi

  36. [44]

    Cell and Tissue Research 195(2), 205–226 (1978) https://doi.org/ 10.1007/BF00236720

    Wilson, M., Garrard, P., McGinness, S.: The unit structure of the locust com- pound eye. Cell and Tissue Research 195(2), 205–226 (1978) https://doi.org/ 10.1007/BF00236720

    1978 doi

  37. [45]

    Cell and Tissue Research 225(3), 595–617 (1982) https: //doi.org/10.1007/BF00214807

    Williams, D.S.: Ommatidial structure in relation to turnover of photoreceptor membrane in the locust. Cell and Tissue Research 225(3), 595–617 (1982) https: //doi.org/10.1007/BF00214807

    1982 doi

  38. [46]

    Journal of Comparative Physiology A 156(5), 645–652 (1985) https://doi.org/10.1007/BF00619113

    French, A.S., K¨ uster, J.E.: Nonlinearities in locust photoreceptors during trans- duction of small numbers of photons. Journal of Comparative Physiology A 156(5), 645–652 (1985) https://doi.org/10.1007/BF00619113

    1985 doi

  39. [47]

    Biological Cybernetics 52(5), 333–337 (1985) https://doi.org/10.1007/BF00355755 26

    Kuster, J.E., French, A.S.: Changes in the dynamic properties of locust photore- ceptors at three levels of light adaptation. Biological Cybernetics 52(5), 333–337 (1985) https://doi.org/10.1007/BF00355755 26

    1985 doi

  40. [48]

    Zeitschrift f¨ ur vergleichende Physiologie55(2), 167–182 (1967) https://doi.org/10.1007/BF00342252

    Tunstall, J., Horridge, G.A.: Electrophysiological investigation of the optics of the locust retina. Zeitschrift f¨ ur vergleichende Physiologie55(2), 167–182 (1967) https://doi.org/10.1007/BF00342252

    1967 doi

  41. [49]

    Journal of Comparative Physiology 97(4), 323–328 (1975) https://doi.org/10

    Wilson, M.: Angular sensitivity of light and dark adapted locust retinula cells. Journal of Comparative Physiology 97(4), 323–328 (1975) https://doi.org/10. 1007/BF00631968

    1975

  42. [50]

    Current Biology 28(10), 1509–15213 (2018) https://doi.org/10.1016/j.cub.2018.04.007

    Wang, H., Dewell, R.B., Zhu, Y., Gabbiani, F.: Feedforward inhibition con- veys time-varying stimulus information in a collision detection circuit. Current Biology 28(10), 1509–15213 (2018) https://doi.org/10.1016/j.cub.2018.04.007

    2018 doi

  43. [51]

    Cell Reports 23(8), 2365–2378 (2018) https://doi.org/10.1016/j.celrep

    Zhu, Y., Dewell, R.B., Wang, H., Gabbiani, F.: Pre-synaptic muscarinic exci- tation enhances the discrimination of looming stimuli in a collision-detection neuron. Cell Reports 23(8), 2365–2378 (2018) https://doi.org/10.1016/j.celrep. 2018.04.079

    2018 doi

  44. [52]

    Journal of Comparative Neurology 523(2), 298–312 (2014) https://doi.org/10

    Wernitznig, S., Rind, F.C., P¨ olt, P., Zankel, A., Pritz, E., Kolb, D., Bock, E., Leitinger, G.: Synaptic connections of first-stage visual neurons in the locust schistocerca gregaria extend evolution of tetrad synapses back 200 million years. Journal of Comparative Neurology...

    2014

  45. [53]

    Journal of Comparative Neurology 395(3), 405–15 (1998)

    Rind, F.C., Simmons, P.J.: Local circuit for the computation of object approach by an identified visual neuron in the locust. Journal of Comparative Neurology 395(3), 405–15 (1998)

    1998

  46. [54]

    Rind, C., Wernitznig, S., Leitinger, G., P¨ olt, P., Zankel, A.: Collision Detect- ing Neurons in the Locust Lobula and Their Input Connectomes. (2014). 11th International Congress of Neuroethology ; Conference date: 28-07-2014 Through 01-08-2014

    2014

  47. [55]

    Nature Neuroscience 18(12), 1713–1721 (2015) https://doi.org/10.1038/nn.4157

    Stuart, G.J., Spruston, N.: Dendritic integration: 60 years of progress. Nature Neuroscience 18(12), 1713–1721 (2015) https://doi.org/10.1038/nn.4157

    2015 doi

  48. [56]

    Neuroscience 368, 115–131 (2018) https://doi.org/10.1016/j

    Hu, H., Vervaeke, K.: Synaptic integration in cortical inhibitory neu- ron dendrites. Neuroscience 368, 115–131 (2018) https://doi.org/10.1016/j. neuroscience.2017.06.065

    2018 doi

  49. [57]

    Scientific Reports 6(1) (2016) https://doi.org/10.1038/srep35525

    Rind, F.C., Wernitznig, S., P¨ olt, P., Zankel, A., G¨ utl, D., Sztarker, J., Leitinger, G.: Two identified looming detectors in the locust: ubiquitous lateral connections among their inputs contribute to selective responses to looming objects. Scientific Reports 6(1) (2016) h...

    2016 doi

  50. [58]

    In: Advances in Neural Information Processing Systems 24, pp

    Keil, M.S.: Emergence of multiplication in a biophysical model of a wide-field visual neuron for computing object approaches: Dynamics, peaks, & fits. In: Advances in Neural Information Processing Systems 24, pp. 469–477 (2011) 27

    2011

  51. [59]

    PLOS Computational Biology 11(10), 1004479 (2015) https://doi.org/10.1371/journal.pcbi.1004479

    Keil, M.S.: Dendritic pooling of noisy threshold processes can explain many properties of a collision-sensitive visual neuron. PLOS Computational Biology 11(10), 1004479 (2015) https://doi.org/10.1371/journal.pcbi.1004479

    2015 doi

  52. [60]

    In: Proc

    Keil, M.S., Rodriguez-Vazquez, A.: Toward a computational approach for colli- sion avoidance with real-world scenes. In: Proc. SPIE 5119, Bioengineered and Bioinspired Systems (2003). https://doi.org/10.1117/12.499054

    2003 doi

  53. [61]

    In: Proceedings of the Fourth IASTED International Conference on Visualization, Imaging, and Image Processing, pp

    Keil, M.S., Roca-Moreno, E., Rodriguez-Vazquez, A.: A neural model of the locust visual system for detection of object approaches with real-world scenes. In: Proceedings of the Fourth IASTED International Conference on Visualization, Imaging, and Image Processing, pp. 340–345 (2004)

    2004

  54. [63]

    Neurocomputing 69(13), 1591–1598 (2006) https://doi.org/10.1016/j.neucom.2005.06.017

    Yue, S., Rind, F.C., Keil, M.S., Cuadri, J., Stafford, R.: A bio-inspired visual collision detection mechanism for cars: Optimisation of a model of a locust neuron to a novel environment. Neurocomputing 69(13), 1591–1598 (2006) https://doi.org/10.1016/j.neucom.2005.06.017

    2006 doi

  55. [64]

    In: 2009 2nd IEEE International Conference on Computer Science and Information Technology, pp

    Shigang, Y., Rind, F.C.: Near range path navigation using lgmd visual neural networks. In: 2009 2nd IEEE International Conference on Computer Science and Information Technology, pp. 105–109. https://doi.org/10.1109/ICCSIT.2009. 5234439

    2009 doi

  56. [65]

    Autonomous Robots 28(2), 151–167 (2010) https://doi.org/10.1007/s10514-009-9157-4

    Yue, S., Santer, R.D., Yamawaki, Y., Rind, F.C.: Reactive direction control for a mobile robot: a locust-like control of escape direction emerges when a bilateral pair of model locust visual neurons are integrated. Autonomous Robots 28(2), 151–167 (2010) https://doi.org/10.100...

    2010 doi

  57. [66]

    In: 2009 International Joint Conference on Neural Networks, pp

    Meng, H., Yue, S., Hunter, A., Appiah, K., Hobden, M., Priestley, N., Hob- den, P., Pettit, C.: A modified neural network model for lobula giant movement detector with additional depth movement feature. In: 2009 International Joint Conference on Neural Networks, pp. 2078–2083 ...

    2009

  58. [67]

    (eds.) A Modified LGMD Based Neural Network for Automatic Collision Detection, pp

    Silva, A.C., Silva, J., Santos, C.P.d.: In: Ferrier, J.-L., Bernard, A., Gusikhin, O., Madani, K. (eds.) A Modified LGMD Based Neural Network for Automatic Collision Detection, pp. 217–233. Springer, Cham (2014). https://doi.org/10. 1007/978-3-319-03500-0 14 . https://doi.org/...

    2014 doi

  59. [68]

    IEEE Transactions on Cognitive and Developmental Systems 9(3), 241–254 (2017) https://doi.org/10.1109/TCDS

    Hu, C., Arvin, F., Xiong, C., Yue, S.: Bio-inspired embedded vision system for autonomous micro-robots: The lgmd case. IEEE Transactions on Cognitive and Developmental Systems 9(3), 241–254 (2017) https://doi.org/10.1109/TCDS. 28 2016.2574624

    2017

  60. [69]

    Neural Networks 166, 22–37 (2023) https://doi.org/10.1016/j.neunet.2023.06.039

    Chang, Z., Fu, Q., Chen, H., Li, H., Peng, J.: A look into feedback neu- ral computation upon collision selectivity. Neural Networks 166, 22–37 (2023) https://doi.org/10.1016/j.neunet.2023.06.039

    2023 doi

  61. [70]

    Neurocomputing 598, 128110 (2024)

    Chang, Z., Fu, Q., Hua, M., Peng, J.: Feedback neural computation in collision perception: Towards diverse selectivity. Neurocomputing 598, 128110 (2024)

    2024

  62. [71]

    In: 2017 International Joint Conference on Neural Networks (IJCNN), pp

    ˇC ´ ıˇ zek, P., Miliˇ cka, P., Faigl, J.: Neural based obstacle avoidance with cpg con- trolled hexapod walking robot. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 650–656 (2017). https://doi.org/10.1109/IJCNN.2017. 7965914

    2017 doi

  63. [72]

    In: 2018 International Joint Conference on Neu- ral Networks (IJCNN), pp

    Zhao, J., Hu, C., Zhang, C., Wang, Z., Yue, S.: A bio-inspired collision detector for small quadcopter. In: 2018 International Joint Conference on Neu- ral Networks (IJCNN), pp. 1–7 (2018). https://doi.org/10.1109/IJCNN.2018. 8489298

    2018 doi

  64. [73]

    In: MacIntyre, J., Maglogiannis, I., Iliadis, L., Pimenidis, E

    Zhao, J., Ma, X., Fu, Q., Hu, C., Yue, S.: An lgmd based competitive col- lision avoidance strategy for uav. In: MacIntyre, J., Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) Artificial Intelligence Applications and Innovations, pp. 80–91. Springer, Cham (2019)

    2019

  65. [74]

    Biomimetics 9(1), 22 (2024) https://doi.org/10.3390/ biomimetics9010022

    Zhao, J., Xie, Q., Shuang, F., Yue, S.: An angular acceleration based looming detector for moving uavs. Biomimetics 9(1), 22 (2024) https://doi.org/10.3390/ biomimetics9010022

    2024

  66. [75]

    In: The 2022 International Joint Conference on Neural Networks (2022)

    Wu, C., Shuang, F., Wang, H., Zhao, J., Yue, S.: Dynamic powerlines detection for uavs by attention fused looming detector. In: The 2022 International Joint Conference on Neural Networks (2022)

    2022

  67. [76]

    In: 2004 IEEE Interna- tional Joint Conference on Neural Networks (IEEE Cat

    Badia, S., Verschure, P.F.M.J.: A collision avoidance model based on the lobula giant movement detector (LGMD) neuron of the locust. In: 2004 IEEE Interna- tional Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), vol. 3, pp. 1757–17613 (2004). https://doi.org/10.11...

    2004 arXiv

  68. [77]

    The Interna- tional Journal of Robotics Research26, 759–772 (2007) https://doi.org/10.1177/ 0278364907080253

    Badia, S., Pyk, P., Verschure, P.: A fly-locust based neuronal control system applied to an unmanned aerial vehicle: The invertebrate neuronal principles for course stabilization, altitude control and collision avoidance. The Interna- tional Journal of Robotics Research26, 759...

    2007

  69. [78]

    Bioinspiration & Biomimetics 14 (2019) https://doi.org/10.1088/1748-3190/ab1a9c 29

    ˇC ´ ıˇ zek, P., Faigl, J.: Self-supervised learning of the biologically-inspired obstacle avoidance of hexapod walking robot. Bioinspiration & Biomimetics 14 (2019) https://doi.org/10.1088/1748-3190/ab1a9c 29

    2019 doi

  70. [79]

    In: Proceedings of the 2017 IEEE International Symposium on Circuits and Systems (ISCAS), pp

    Salt, L., Indiveri, G., Sandamirskaya, Y.: Obstacle avoidance with LGMD neu- ron: Towards a neuromorphic uav implementation. In: Proceedings of the 2017 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–4 (2017)

    2017

  71. [80]

    Artificial Life 13(2), 93–122 (2007) https://doi.org/10.1162/artl.2007.13.2.93

    Yue, S., Rind, F.C.: A synthetic vision system using directionally selective motion detectors to recognize collision. Artificial Life 13(2), 93–122 (2007) https://doi.org/10.1162/artl.2007.13.2.93

    2007 doi

  72. [81]

    The Innova- tion 4(1) (2023) https://doi.org/10.1016/j.xinn.2022.100368

    Xu, G.J.W., Guo, K., Park, S.H., Sun, P.Z.H., Song, A.: Bio-inspired vision mimetics toward next-generation collision-avoidance automation. The Innova- tion 4(1) (2023) https://doi.org/10.1016/j.xinn.2022.100368

    2023

  73. [82]

    Frontiers in Neuroscience 17 (2023) https://doi.org/10.3389/fnins.2023.1247227

    Zheng, Y., Wang, Y., Wu, G., Li, H., Peng, J.: Enhancing lgmd-based model for collision prediction via binocular structure. Frontiers in Neuroscience 17 (2023) https://doi.org/10.3389/fnins.2023.1247227

    2023

  74. [83]

    In: British Machine Vision Conference, pp

    Fu, Q., Yue, S., Hu, C.: Bio-inspired collision detector with enhanced selectivity for ground robotic vision system. In: British Machine Vision Conference, pp. 1–13 (2016)

    2016

  75. [84]

    In: 2017 IEEE/RSJ Inter- national Conference on Intelligent Robots and Systems (IROS), pp

    Fu, Q., Hu, C., Liu, T., Yue, S.: Collision selective lgmds neuron models research benefits from a vision-based autonomous micro robot. In: 2017 IEEE/RSJ Inter- national Conference on Intelligent Robots and Systems (IROS), pp. 3996–4002 (2017). https://doi.org/10.1109/IROS.201...

    2017

  76. [85]

    In: 2023 International Joint Confer- ence on Neural Networks (IJCNN), pp

    Li, J., Sun, X., Li, H., Peng, J., Fu, Q.: On the ensemble of collision perception neuron models towards ultra-selectivity. In: 2023 International Joint Confer- ence on Neural Networks (IJCNN), pp. 1–8 (2023). https://doi.org/10.1109/ IJCNN54540.2023.10191446

    2023

  77. [86]

    Frontiers in Robotics and AI 8, 529872 (2021) https://doi.org/10.3389/frobt.2021.529872

    Fu, Q., Sun, X., Liu, T., Hu, C., Yue, S.: Robustness of bio-inspired visual systems for collision prediction in critical robot traffic. Frontiers in Robotics and AI 8, 529872 (2021) https://doi.org/10.3389/frobt.2021.529872

    2021

  78. [87]

    Neural Networks 165, 106–118 (2023) https://doi.org/10.1016/j.neunet

    Sun, X., Fu, Q., Peng, J., Yue, S.: An insect-inspired model facilitating autonomous navigation by incorporating goal approaching and collision avoid- ance. Neural Networks 165, 106–118 (2023) https://doi.org/10.1016/j.neunet. 2023.05.033

    2023 doi

  79. [89]

    30 In: 2024 International Joint Conference on Neural Networks (IJCNN), pp

    Feng, L., Sun, X., Fu, Q., Peng, J., Li, H.: Improving the performance of an insect-inspired navigation model using directional selective collision detection. 30 In: 2024 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2024). https://doi.org/10.1109/IJCNN60...

    2024

  80. [90]

    In: Proceedings of the 2023 International Conference on Neuromorphic Systems

    Ghosh, A., Nowotny, T., Knight, J.C.: Insect-inspired spatio-temporal downsam- pling of event-based input. In: Proceedings of the 2023 International Conference on Neuromorphic Systems. ICONS ’23. Association for Computing Machin- ery, New York, NY, USA (2023). https://doi.org/...

    2023

  81. [91]

    Machine Vision and Applications 35(5), 103 (2024) https://doi.org/10.1007/s00138-024-01582-8

    Wang, H., Wang, S., Qian, L.: An insect vision-inspired neuromorphic vision sys- tems in low-light obstacle avoidance for intelligent vehicles. Machine Vision and Applications 35(5), 103 (2024) https://doi.org/10.1007/s00138-024-01582-8

    2024 doi

  82. [92]

    IEEE Transactions on Biomedical Engineering 71(10), 2978–2990 (2024) https: //doi.org/10.1109/TBME.2024.3404976

    Deng, Y., Ruan, H., He, S., Yang, T., Guo, D.: A biomimetic visual detection model: Event-driven lgmds implemented with fractional spiking neuron circuits. IEEE Transactions on Biomedical Engineering 71(10), 2978–2990 (2024) https: //doi.org/10.1109/TBME.2024.3404976

    2024

  83. [93]

    IEEE Transactions on Neural Networks and Learning Systems 31(9), 3305–3318 (2020)

    Salt, L., Howard, D., Indiveri, G., Sandamirskaya, Y.: Parameter optimization and learning in a spiking neural network for uav obstacle avoidance targeting neuromorphic processors. IEEE Transactions on Neural Networks and Learning Systems 31(9), 3305–3318 (2020)

    2020

  84. [94]

    Zhang, Y., Han, H., Luan, H., Hua, M., Liu, M., Cuayahuitl, H., Yue, S.: An opponent colour lgmd neural network for collision detection at night with thermal camera (2023) https://doi.org/10.2139/ssrn.4453241

    2023 doi

  85. [95]

    Webb, B.: Can robots make good models of biological behaviour? Behav- ioral and Brain Sciences 24(6), 1033–1050 (2001) https://doi.org/10.1017/ S0140525X01000127

    2001

  86. [96]

    Journal of Neuroscience 39(46), 9173–9184 (2019) https://doi.org/10.1523/JNEUROSCI.0638-19.2019 https://www.jneurosci.org/content/39/46/9173.full.pdf

    Poleg-Polsky, A.: Dendritic spikes expand the range of well toler- ated population noise structures. Journal of Neuroscience 39(46), 9173–9184 (2019) https://doi.org/10.1523/JNEUROSCI.0638-19.2019 https://www.jneurosci.org/content/39/46/9173.full.pdf

    2019 doi

  87. [97]

    Array 17, 100272 (2023) https://doi.org/10.1016/j.array.2022

    Fu, Q., Li, Z., Peng, J.: Harmonizing motion and contrast vision for robust loom- ing detection. Array 17, 100272 (2023) https://doi.org/10.1016/j.array.2022. 100272

    2023 doi

  88. [98]

    Nature Reviews Neuroscience 13(1), 51–62 (2012) https://doi.org/10.1038/ nrn3136

    Carandini, M., Heeger, D.J.: Normalization as a canonical neural computa- tion. Nature Reviews Neuroscience 13(1), 51–62 (2012) https://doi.org/10.1038/ nrn3136

    2012

  89. [99]

    In: 2017 Workshop on Research, Education and Development of Unmanned Aerial 31 Systems (RED-UAS), pp

    Rojas-Perez, L.O., Martinez-Carranza, J.: Metric monocular slam and colour segmentation for multiple obstacle avoidance in autonomous flight. In: 2017 Workshop on Research, Education and Development of Unmanned Aerial 31 Systems (RED-UAS), pp. 234–239 (2017). https://doi.org/1...

    2017

  90. [100]

    In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR W), pp

    Cigla, C., Brockers, R., Matthies, L.: Image-based visual perception and rep- resentation for collision avoidance. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR W), pp. 421–429 (2017). https://doi.org/10.1109/CVPR W.2017.57

    2017 doi

  91. [101]

    In: 2020 International Joint Conference on Neural Networks (IJCNN), pp

    Macias-Garcia, E., Galeana-Perez, D., Bayro-Corrochano, E.: Cnn based percep- tion system for collision avoidance in mobile robots using stereo vision. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–7 (2020). https://doi.org/10.1109/IJCNN48605.2020.9206747

    2020

  92. [102]

    IET Radar, Sonar & Naviga- tion 14(9), 1287–1296 (2020) https://doi.org/10.1049/iet-rsn.2019.0409 https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rsn.2019.0409

    Reich, G.M., Antoniou, M., Baker, C.J.: Memory-enhanced cognitive radar for autonomous navigation. IET Radar, Sonar & Naviga- tion 14(9), 1287–1296 (2020) https://doi.org/10.1049/iet-rsn.2019.0409 https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rsn.2019.0409

    2020

  93. [103]

    Physical Communica- tion 54, 101815 (2022) https://doi.org/10.1016/j.phycom.2022.101815

    Zhao, T., Yao, J., Gong, C., Wang, Y.: Wireless ultraviolet light mimo assisted uav direction perception and collision avoidance method. Physical Communica- tion 54, 101815 (2022) https://doi.org/10.1016/j.phycom.2022.101815

    2022

  94. [104]

    Nature 551(7679), 237–241 (2017) https://doi.org/10.1038/ nature24626

    Klapoetke, N.C., Nern, A., Peek, M.Y., Rogers, E.M., Breads, P., Rubin, G.M., Reiser, M.B., Card, G.M.: Ultra-selective looming detection from radial motion opponency. Nature 551(7679), 237–241 (2017) https://doi.org/10.1038/ nature24626

    2017

  95. [105]

    Current Biology 29(6), 1073–10814 (2019) https://doi.org/10.1016/j.cub.2019.01.079

    Ache, J.M., Polsky, J., Alghailani, S., Parekh, R., Breads, P., Peek, M.Y., Bock, D.D., von Reyn, C.R., Card, G.M.: Neural basis for looming size and velocity encoding in the drosophila giant fiber escape pathway. Current Biology 29(6), 1073–10814 (2019) https://doi.org/10.101...

    2019 doi

  96. [106]

    Current Biology 20(22), 2052–2057 (2010)

    Jones, P.W., Gabbiani, F.: Synchronized neural input shapes stimulus selectivity in a collision-detecting neuron. Current Biology 20(22), 2052–2057 (2010)

    2010

  97. [107]

    Journal of Neurophysiology 97(1), 159–177 (2007) https://doi.org/10.1152/jn

    Peron, S.P., Krapp, H.G., Gabbiani, F.: Influence of electrotonic structure and synaptic mapping on the receptive field properties of a collision-detecting neuron. Journal of Neurophysiology 97(1), 159–177 (2007) https://doi.org/10.1152/jn. 00660.2006

    2007

  98. [108]

    Journal of Neurophysiology 115(6), 3101–3112 (2016) https://doi.org/10.1152/jn.00044

    Zhu, Y., Gabbiani, F.: Fine and distributed subcellular retinotopy of excita- tory inputs to the dendritic tree of a collision-detecting neuron. Journal of Neurophysiology 115(6), 3101–3112 (2016) https://doi.org/10.1152/jn.00044. 2016

    2016 doi

  99. [109]

    Journal of Neurophysiology 120(4), 2049–2058 (2018) https://doi.org/10

    Wang, H., Dewell, R.B., Ehrengruber, M.U., Segev, E., Reimer, J., Roukes, M.L., Gabbiani, F.: Optogenetic manipulation of medullary neurons in the locust optic 32 lobe. Journal of Neurophysiology 120(4), 2049–2058 (2018) https://doi.org/10. 1152/jn.00356.2018 https://doi.org/1...

    2018

  100. [110]

    Journal of Neurophysiology 120(4), 1753–1764 (2018) https://doi.org/10.1152/jn.00363.2018

    Dewell, R.B., Gabbiani, F.: M current regulates firing mode and spike reliability in a collision-detecting neuron. Journal of Neurophysiology 120(4), 1753–1764 (2018) https://doi.org/10.1152/jn.00363.2018

    2018

  101. [111]

    Journal of Neurophysiology 122(2), 691–706 (2019) https://doi.org/10.1152/jn.00048.2019 https://doi.org/10.1152/jn.00048.2019

    Dewell, R.B., Gabbiani, F.: Active membrane conductances and morphology of a collision detection neuron broaden its impedance profile and improve discrim- ination of input synchrony. Journal of Neurophysiology 122(2), 691–706 (2019) https://doi.org/10.1152/jn.00048.2019 https:...

    2019

  102. [112]

    Journal of Comparative Physiology A 198(5), 389–395 (2012) https://doi.org/10.1007/ s00359-012-0716-3

    Schnell, B., Raghu, S.V., Nern, A., Borst, A.: Columnar cells necessary for motion responses of wide-field visual interneurons in drosophila. Journal of Comparative Physiology A 198(5), 389–395 (2012) https://doi.org/10.1007/ s00359-012-0716-3

    2012

  103. [113]

    Current Biology 23(18), 851–853 (2013) https://doi.org/10.1016/j.cub.2013.08.018

    Gilbert, C.: Brain connectivity: Revealing the fly visual motion circuit. Current Biology 23(18), 851–853 (2013) https://doi.org/10.1016/j.cub.2013.08.018

    2013 doi

  104. [114]

    Arthropod Structure and Development 46(5), 703–717 (2017) https: //doi.org/10.1016/j.asd.2017.06.003

    Serres, J.R., Ruffier, F.: Optic flow-based collision-free strategies: From insects to robots. Arthropod Structure and Development 46(5), 703–717 (2017) https: //doi.org/10.1016/j.asd.2017.06.003 . From Insects to Robots

    2017 doi

  105. [115]

    Neural Networks 135, 13–28 (2021) https://doi.org/10.1016/j.neunet.2020.11.018

    Li, L., Zhang, Z., Lu, J.: Artificial fly visual joint perception neural network inspired by multiple-regional collision detection. Neural Networks 135, 13–28 (2021) https://doi.org/10.1016/j.neunet.2020.11.018

    2021 doi

  106. [116]

    Journal of Comparative Physiology 99(1), 1–66 (1975) https://doi

    Collett, T.S., Land, M.F.: Visual control of flight behaviour in the hoverflysyritta pipiens l. Journal of Comparative Physiology 99(1), 1–66 (1975) https://doi. org/10.1007/BF01464710

    1975 doi

  107. [117]

    Nature 362(6420), 541– 543 (1993) https://doi.org/10.1038/362541a0

    O’Carroll, D.: Feature-detecting neurons in dragonflies. Nature 362(6420), 541– 543 (1993) https://doi.org/10.1038/362541a0

    1993 doi

  108. [118]

    PLOS ONE 3(7), 2784 (2008) https://doi.org/10.1371/journal.pone.0002784

    Wiederman, S.D., Shoemaker, P.A., O’Carroll, D.C.: A model for the detection of moving targets in visual clutter inspired by insect physiology. PLOS ONE 3(7), 2784 (2008) https://doi.org/10.1371/journal.pone.0002784

    2008 doi

  109. [119]

    In: 2011 Seventh International Conference on Intelligent Sensors, Sensor Net- works and Information Processing, pp

    Halupka, K.J., Wiederman, S.D., Cazzolato, B.S., O’Carroll, D.C.: Discrete implementation of biologically inspired image processing for target detection. In: 2011 Seventh International Conference on Intelligent Sensors, Sensor Net- works and Information Processing, pp. 143–148...

    2011

  110. [120]

    Journal of Neural Engineering 14(4), 046030 (2017) https://doi.org/10.1088/1741-2552/aa776c

    Bagheri, Z.M., Cazzolato, B.S., Grainger, S., O’Carroll, D.C., Wiederman, S.D.: 33 An autonomous robot inspired by insect neurophysiology pursues moving fea- tures in natural environments. Journal of Neural Engineering 14(4), 046030 (2017) https://doi.org/10.1088/1741-2552/aa776c

    2017 doi

  111. [121]

    IEEE Transactions on Cybernetics 53(10), 6340–6352 (2023) https://doi.org/10.1109/ TCYB.2022.3170699

    Wang, H., Zhao, J., Wang, H., Hu, C., Peng, J., Yue, S.: Attention and prediction-guided motion detection for low-contrast small moving targets. IEEE Transactions on Cybernetics 53(10), 6340–6352 (2023) https://doi.org/10.1109/ TCYB.2022.3170699

    2023

  112. [122]

    Current Biology 32(15), 847–849 (2022) https://doi.org/10.1016/ j.cub.2022.06.051

    Vashistha, H., Clark, D.A.: Feature maps: How the insect visual system organizes information. Current Biology 32(15), 847–849 (2022) https://doi.org/10.1016/ j.cub.2022.06.051

    2022

  113. [123]

    Advanced Intel- ligent Systems 6(9), 2400198 (2024) https://doi.org/10.1002/aisy.202400198 https://onlinelibrary.wiley.com/doi/pdf/10.1002/aisy.202400198

    Gu, B., Feng, J., Song, Z.: Looming detection in complex dynamic visual scenes by interneuronal coordination of motion and feature pathways. Advanced Intel- ligent Systems 6(9), 2400198 (2024) https://doi.org/10.1002/aisy.202400198 https://onlinelibrary.wiley.com/doi/pdf/10.10...

    2024 doi

  114. [124]

    In: Proceedings of the 2020 5th International Conference on Advanced Robotics and Mechatronics (ICARM), pp

    Fu, Q., Yue, S.: Complementary visual neuronal systems model for collision sensing. In: Proceedings of the 2020 5th International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 609–615 (2020)

    2020

  115. [125]

    Shuang, F., Zhu, Y., Xie, Y., Zhao, L., Xie, Q., Zhao, J., Yue, S.: OppLoD- the opponency based looming detector, model extension of looming sensitivity from lgmd to LPLC2 (2023) https://doi.org/10.48550/arXiv.2302.10284

    2023 doi arXiv

  116. [126]

    Nature 634(8032), 139–152 (2024) https://doi.org/10.1038/s41586-024-07686-5

    Schlegel, P., Yin, Y., Bates, A.S., Dorkenwald, S., Eichler, K., Brooks, P., Han, D.S., Gkantia, M., Santos, M., Munnelly, E.J., Badalamente, G., Ser- ratosa Capdevila, L., Sane, V.A., Fragniere, A.M.C., Kiassat, L., Pleijzier, M.W., St¨ urner, T., Tamimi, I.F.M., Dunne, C.R.,...

    2024

  117. [127]

    Nature 634(8032), 124–138 (2024) https://doi.org/10.1038/s41586-024-07558-y

    Dorkenwald, S., Matsliah, A., Sterling, A.R., Schlegel, P., Yu, S.-c., McKellar, 34 C.E., Lin, A., Costa, M., Eichler, K., Yin, Y., Silversmith, W., Schneider-Mizell, C., Jordan, C.S., Brittain, D., Halageri, A., Kuehner, K., Ogedengbe, O., Morey, R., Gager, J., Kruk, K., Perl...

    2024

  118. [128]

    Nature 634(8032), 166–180 (2024) https://doi.org/10.1038/ s41586-024-07981-1

    Matsliah, A., Yu, S.-c., Kruk, K., Bland, D., Burke, A.T., Gager, J., Hebditch, J., Silverman, B., Willie, K.P., Willie, R., Sorek, M., Sterling, A.R., Kind, E., Garner, D., Sancer, G., Wernet, M.F., Kim, S.S., Murthy, M., Seung, H.S., David, C., Joroff, J., Kristiansen, A., S...

    2024

  119. [129]

    Nature 634(8032), 210–219 (2024) https: //doi.org/10.1038/s41586-024-07763-9

    Shiu, P.K., Sterne, G.R., Spiller, N., Franconville, R., Sandoval, A., Zhou, J., Simha, N., Kang, C.H., Yu, S., Kim, J.S., Dorkenwald, S., Matsliah, A., Schlegel, P., Yu, S.-c., McKellar, C.E., Sterling, A., Costa, M., Eichler, K., Bates, A.S., Eckstein, N., Funke, J., Jefferi...

    2024 doi

  120. [130]

    Nature 634(8032), 181–190 (2024) https://doi.org/10.1038/s41586-024-07967-z 35

    Garner, D., Kind, E., Lai, J.Y.H., Nern, A., Zhao, A., Houghton, L., Sancer, G., Wolff, T., Rubin, G.M., Wernet, M.F., Kim, S.S.: Connectomic reconstruction predicts visual features used for navigation. Nature 634(8032), 181–190 (2024) https://doi.org/10.1038/s41586-024-07967-z 35

    2024 doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.