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Synaptic high-frequency jumping synchronises vision to high-speed behaviour.

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  1. [1] § Results › Photomechanical interactions accentuate predictive hyperacute responses ↔ model/MultipleRhabdomerewithScreenDot.m, the whole file · a weak match · score 0.61 · interommatidial angle, quantal sampling, receptive field, LMC responses, clipping, centre
  2. [2] § Results › Photomechanical interactions accentuate predictive hyperacute responses ↔ model/CombinedAfterLatency.m, the whole file · a weak match · score 0.57 · Quasi classical, R1 R6 photoreceptors, LMC responses, ratio, simulated, quantal

Paper

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The authors' code

MATLAB · 132 lines · 5.5 KB · GPL-3.0 · 1 match

  1. %Simulate photorecptor light absorbtion on 6 ommatidium for two dot
  2. %stimulus
  3. %@Jouni Takalo
  5. %centre lens position and normal
  6. Diameters = [1.5 1.3 1.5 1.3 1.3 1.5 1.1];% (um) Rhabdomere diameters
  8. lenspos =[0 0 0;0 0 0 ;0 0 0; 0 0 0; 0 0 0;0 0 0;0 0 0];% (um) Lens positions
  9. lensnormal = [0 0 1; 0 0 1; 0 0 1; 0 0 1;0 0 1; 0 0 1;0 0 1];%Lens normals
  11. ommatidiumpairs =[1,0;1,-1;1,-2;0,-1;-1,0;-1,1;0,0];%R1-R7/R8 pairings
  13. %parameters of the lens array
  14. lensdist = 22.2;% (um) Lens diameter
  15. lensangle= 3.1/180*pi;% (rad) Interommatidial angle
  16. lensanglexx=lensangle*sqrt(3)/2;
  17. lensanglexy=lensangle/2;
  18. lensnormal =lensnormal./repmat(vecnorm(lensnormal,2,2),1,3);
  19. lensnormbefore = lensnormal;
  20. lensradius = lensdist/lensangle; %radius of eye
  22. %Rhabdomere parameters in rest
  23. rotangle = atan2(0.1*6,1.48);
  24. RotM =[cos(rotangle) -sin(rotangle); sin(rotangle) cos(rotangle)];
  25. degperum =-1.86; %(deg/um position relation to angle)
  26. MU = RotM*[1.19 -1.86; 1.47 -0.19; 1.65 1.44; 0.1 1.48; -1.37 1.73; -1.61 0.13; 0 0]'*degperum;% (um) Rhabdomere angles
  27. modelparam.MU =MU';
  29. modelparam.AngleScale=[-1.86/sqrt(2) -1.86/sqrt(2)];%receptive field micrsaccade movements
  30. modelparam.hw = [2.62 2.10 2.62 2.10 2.13 2.57 2.34];% receptive field half widths
  31. modelparam.hwmd =[0.17 0.16 0.17 0.16 0.16 0.17 0.33];%receptive field halfwidths relation to microsaccades
  32. modelparam.amplitude = [41.7; 38.9; 41.7;38.9;35.5;43.2;33.0 ];%Maximal absorbtion amplitude
  33. modelparam.amplitudemd =[2.7 3.7 2.7 3.7 3.7 2.7 7.3];%Maximal absorbtion amplitude relation to microsaccades
  36. %Virtual screen parameters
  37. %Rays casted to Screen
  38. modelparam.xdim =-10:0.5:10; %(deg)
  39. modelparam.ydim =-10:0.5:10;%(deg)
  41. %3 deg field
  42. modelparam.distmult =0; %Multipier moving closer and farther
  43. possift =7600;% um For fitting clipping projector data
  44. %(um) Screen position
  45. modelparam.mappos =[-15000/2+possift 15000/2 50000 ;15000/2+possift 15000/2 50000; -15000/2+possift -15000/2 50000]/2^modelparam.distmult;
  47. modelparam.mapsize =[150 150];%Number of pixels in screen
  48. %Dot positions
  49. modelparam.xstartpos =75-6-32*6+2;%dot starting position
  50. modelparam.barspeed =0.1832;%dot speed 42 deg/s
  51. modelparam.barpos=[0 75-3 6 6; -24 75-3 6 6]; %two 0.7 deg dots with 2.1 deg distance between
  52. modelparam.LightScale = 4.3333e+04;%Dot intensity correction for fitting projector data 10 mV LMC response
  53. modelparam.barangle=0;%Dots direction
  54. datalength =round((300-modelparam.xstartpos)/modelparam.barspeed);% Simulation duration
  56. modelparam.N_micro = round(54000*Diameters/mean(Diameters(1:6)));%Number of microvilli
  57. modelparam.Fs =2000;%Samplerate
  58. samprate =modelparam.Fs;
  60. %Movement model
  61. %Activation model
  62. modelparam.Activation_Force_n=1;%Activation force n
  63. modelparam.Activation_Force_max =9.8068e-04; %Maximal activation force
  64. % modelparam.Activation_Force_max =0; %No activation force
  66. modelparam.Activation_Force_1_point =15.3; %Activation half point
  68. modelparam.actdiv =7; %Single or 7 photoreceptors activate
  69. xposinitial =0;%(um) Initial microsaccade position
  70. %Dampener force
  71. modelparam.Dampener_coef =0.00024;
  72. modelparam.Dampener_base = 2;
  73. modelparam.Dampener_exponent = 2100;
  74. % %Spring force
  75. modelparam.spring_0 =0.0001; % without activation spring constant
  76. modelparam.spring_coef =0.001;%spring constant activation coeffisiant
  77. modelparam.spring_1_point =15.3;%half activation value for spring constant
  78. modelparam.spring_n =1;%Spring constant multiplier
  80. %Stocastic parameters in case of taking original photoreceptor
  81. %Latency parameters
  82. modelparam.LatencyDis = [10.4 0.00037];
  83. %Refraction parameters
  84. modelparam.BumpRefracDis =[10.5,0.0034];
  86. %Voltage bump params
  87. modelparam.BumpParam =[0.0011,2.367,7.08e-04];
  89. %Final data stucture
  90. Data = [];
  92. for i = 1:6
  93. %Ommatidium position
  94. dlensy =ommatidiumpairs(i,2);
  95. dlensx =ommatidiumpairs(i,1);
  96. index =num2str(i);
  97. %Name in cell
  98. ci = ['l' index];
  99. %Lens array position
  100. Data.(ci).dlensy =ommatidiumpairs(i,2);
  101. Data.(ci).dlensx =ommatidiumpairs(i,1);
  104. %Bursty timeseries taking account Poisson nature of light
  106. Data.(ci).light_series = zeros(datalength,7);
  107. datalength =length(Data.(ci).light_series);
  108. Data.(ci).Mapvalue = zeros(length(Data.(ci).light_series),7);
  109. %Various paramenters for lens
  111. Data.(ci).Rx = [cos(lensanglexx*dlensx) 0 sin(lensanglexx*dlensx); 0 1 0; -sin(lensanglexx*dlensx) 0 cos(lensanglexx*dlensx)];
  112. Data.(ci).Ry =[1 0 0; 0 cos(lensangle*dlensy+lensanglexy*dlensx) -sin(lensangle*dlensy+lensanglexy*dlensx); 0 sin(lensangle*dlensy+lensanglexy*dlensx) cos(lensangle*dlensy+lensanglexy*dlensx)];
  113. Data.(ci).lensnormalc = Data.(ci).Ry* Data.(ci).Rx*lensnormal';
  114. Data.(ci).lensnormalc = Data.(ci).lensnormalc';
  115. Data.(ci).lensposc = lenspos+lensradius*( Data.(ci).lensnormalc-lensnormbefore);
  116. %Calculate initial values for receptive fields
  118. [ Data.(ci).Map] = MultipleFields(modelparam.MU,modelparam.hw,modelparam.amplitude,modelparam.xdim,modelparam.ydim,Data.(ci).lensposc, Data.(ci).lensnormalc,modelparam.mappos, modelparam.mapsize);
  119. end
  121. AfterLatency_a =zeros(6,datalength);%Number of quantal samples after latency
  122. Datafields =fieldnames(Data);
  123. Data = struct2cell(Data);
  124. parfor k =1:6
  125. index =num2str(k);
  126. ci = ['l' index];
  127. Data{k} =Lightmodelsimplewithfeedback2dot(modelparam,Data{k});% Simulate
  128. AfterLatency_a(k,:) =Data{k}.AfterLatency(1:datalength,k);
  129. % end
  130. end
  132. Data = cell2struct(Data,Datafields,1);

MultipleRhabdomerewithScreenDot.m at commit 2699a82, under GPL-3.0 · at the source

Overview

Authors: Neveen Mansour1,2, Jouni Takalo1,2,3, Joni Kemppainen1,2, Alice D Bridges1,2, HaDi MaBouDi1,2, Ali Asgar Bohra1,2, Kaja Anielska1,2, Vera Vasas1,2, Théo Robert1,2, Bruce Yi Bu4, Shashwat Shukla4, Yiyin Zhou5, Maike Kittelmann6, Joke Ouwendijk7, Judith Mantell7, Matthew Lawson8, Gonzalo de Polavieja9, Elizabeth Duke8, Aurel A Lazar4, Paul Verkade7, Lars Chittka3, Mikko Juusola1,2
ORCID iDs: Neveen Mansour, Jouni Takalo, Alice D Bridges, HaDi MaBouDi, Vera Vasas, Théo Robert, Yiyin Zhou, Maike Kittelmann, Judith Mantell, Matthew Lawson, Gonzalo de Polavieja, Aurel A Lazar, Paul Verkade, Lars Chittka, Mikko Juusola
  1. School of Biosciences, University of Sheffield, Sheffield, UK
  2. Neuroscience Institute, University of Sheffield, Sheffield, UK
  3. School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
  4. Bionet Group, Department of Electrical Engineering, Columbia University, New York, NY USA
  5. Department of Computer and Information Science, Fordham University, New York, NY USA
  6. School of Biological and Medical Sciences, Oxford Brookes University, Oxford, UK
  7. School of Biochemistry, University of Bristol, Bristol, UK
  8. European Molecular Biology Laboratory, Hamburg Unit c/o DESY, Hamburg, Germany
  9. Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
Institutions: University of Sheffield (United Kingdom); Queen Mary University of London (United Kingdom); Columbia University (United States); Fordham University (United States); Oxford Brookes University (United Kingdom); University of Bristol (United Kingdom); Deutsches Elektronen-Synchrotron DESY (Germany); European Molecular Biology Laboratory (Germany); Champalimaud Foundation (Portugal)
Journal: Nature communications, volume 17, issue 1, article 3863
Dates: received 7 October 2025; accepted 15 April 2026; published online 5 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-72509-2 · PMID 42086540 · PMCID PMC13144400 · OpenAlex W4413434665
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.08.20.671248
Status: code verified
Categories: cellular / molecular (subfield)
Methods: Preprocessing, Spectral & time-frequency, Physiology & signal measures
Keywords: Synaptic transmission, Visual system, Computational biophysics, Neurophysiology, Biophysical models
MeSH: Houseflies*, Motion Perception*, Synapses*, Synaptic Transmission*, Vision, Ocular*, Animals, Behavior, Animal, Photic Stimulation, Photoreceptor Cells, Invertebrate, Saccades (* major topic)
Topic: Advanced Memory and Neural Computing (Electrical and Electronic Engineering, Engineering), according to OpenAlex
Funding: RCUK | Engineering and Physical Sciences Research Council (EP/P006094/1); Horizon Europe Framework Programme grant NimbleAI - Ultra energy-efficient and secure neuromorphic sensing and processing at the endpoint; Biotechnology and Biological Sciences Research Council (BB/F012071/1, BB/D001900/1 and BB/H013849/1, BB/D001900/1, BB/H013849/1); Leverhulme Trust (RPG-2024-016); RCUK | Biotechnology and Biological Sciences Research Council (BB/F012071/1, BB/D001900/1 and BB/H013849/1); Jane and Aatos Erkko Foundation Fellowships
Citations: not cited yet (Europe PMC); 114 references in the paper

Abstract

During high-speed behaviour, animals must synchronise perception and action despite rapid environmental and self-generated motion. How neural systems achieve such precision remains unclear. Here we show how the housefly (Musca domestica) maintains visual accuracy during fast motion. Using intracellular and photomechanical recordings during saccade-like stimulation, we traced information flow from photoreceptors to large monopolar cells (LMCs). Visual neurons achieved record-high information sampling (~2500 bits·s-1) and synaptic transmission (~4100 bits·s-1), far exceeding previous estimates. We identify a previously unknown mechanism - synaptic high-frequency jumping - in which photoreceptor-LMC synapses dynamically shift transmission toward higher frequencies during saccades, extending visual bandwidth to ~1000 Hz, effectively eliminating synaptic delays, and quadrupling classical flicker-fusion limits (~230 Hz). Behavioural experiments show flies respond synchronously within ~13-20 ms, even before photoreceptor responses peak. A biophysically realistic model reveals how photomechanical-stochastic-refractory quantal sampling and synaptic transmission co-adapt with saccadic behaviour: through self-motion, flies efficiently translate image motion into temporally-precise, predictive high-speed vision.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

JuusolaLab/High-frequency-jumping

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 2699a82138d43d626e20b4e5330e865f2f3366f6, 23 April 2026
Languages: MATLAB (17), Python (1)
Size: 411 files, 18 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file), Matplotlib (1 file), NumPy (1 file), SciPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
20 files
At the source: github.com/JuusolaLab/High-frequency-jumping

JuusolaLab

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: github.com/JuusolaLab

Code availability

All the software code, including the complete Musca morphodynamic neural superposition model, can be downloaded from: https://github.com/JuusolaLab/High-frequency-jumping and https://github.com/JuusolaLab.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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No dataset and no data link were found in the paper.

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The data supporting the findings of this study are available from the corresponding authors upon request.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 22 authors, 5 keywords, 10 MeSH terms, 6 funders, 98 references.

Cite

This paper

Mansour, N., Takalo, J., Kemppainen, J., Bridges, A. D., MaBouDi, H., Asgar Bohra, A., Anielska, K., Vasas, V., Robert, T., Yi Bu, B., Shukla, S., Zhou, Y., Kittelmann, M., Ouwendijk, J., Mantell, J., Lawson, M., de Polavieja, G., Duke, E., Lazar, A. A., . . . Juusola, M. (2026). Synaptic high-frequency jumping synchronises vision to high-speed behaviour. Nature communications, 17(1), 3863. https://doi.org/10.1038/s41467-026-72509-2

BibTeX

@article{mansour2026synaptic,
author = {Mansour, Neveen and Takalo, Jouni and Kemppainen, Joni and Bridges, Alice D and MaBouDi, HaDi and Asgar Bohra, Ali and Anielska, Kaja and Vasas, Vera and Robert, Théo and Yi Bu, Bruce and Shukla, Shashwat and Zhou, Yiyin and Kittelmann, Maike and Ouwendijk, Joke and Mantell, Judith and Lawson, Matthew and de Polavieja, Gonzalo and Duke, Elizabeth and Lazar, Aurel A and Verkade, Paul and Chittka, Lars and Juusola, Mikko},
title = {{Synaptic high-frequency jumping synchronises vision to high-speed behaviour}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {3863},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72509-2},
url = {https://doi.org/10.1038/s41467-026-72509-2},
pmid = {42086540},
pmcid = {PMC13144400}
}

RIS

TY - JOUR
AU - Mansour, Neveen
AU - Takalo, Jouni
AU - Kemppainen, Joni
AU - Bridges, Alice D
AU - MaBouDi, HaDi
AU - Asgar Bohra, Ali
AU - Anielska, Kaja
AU - Vasas, Vera
AU - Robert, Théo
AU - Yi Bu, Bruce
AU - Shukla, Shashwat
AU - Zhou, Yiyin
AU - Kittelmann, Maike
AU - Ouwendijk, Joke
AU - Mantell, Judith
AU - Lawson, Matthew
AU - de Polavieja, Gonzalo
AU - Duke, Elizabeth
AU - Lazar, Aurel A
AU - Verkade, Paul
AU - Chittka, Lars
AU - Juusola, Mikko
TI - Synaptic high-frequency jumping synchronises vision to high-speed behaviour
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/05
VL - 17
IS - 1
SP - 3863
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72509-2
UR - https://doi.org/10.1038/s41467-026-72509-2
LA - en
ER -

CSL-JSON

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