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Conflicting adaptations in an inhibitory feedback circuit.

Code ↔ Paper

12 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 12 matches · 10 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Computational modelling › Model generation and parameter tuning ↔ HomeostaticAdaptationModel/homeostaticAdaptationModel/propagateORNs2PNs.m, the whole file · a weak match · score 0.85 · maximum PN response, lateral inhibition, PN response function, firing rate, PN activity, ORN
  2. [2] § Methods › Statistics ↔ CorrelationAnalysis/testCorrValues.m, lines 15–61 · score 0.76 · gradient descent, log normal distribution, real correlations, exp, noise
  3. [3] § Methods › Para‐FlpTag analysis › Segmentation and skeletonisation ↔ paraFlpTag/normalizeSkeletonsFlpTagCalyxDivide.m, the whole file · a weak match · score 0.65 · horizontal lobe, vertical lobe, tip, junction, connecting, peduncle
  4. [4] § Methods › Para‐FlpTag analysis › Segmentation and skeletonisation ↔ multiActivityMaps/normalizeSkeletons.m, the whole file · a weak match · score 0.64 · horizontal lobe, vertical lobe, tip, junction, connecting, peduncle
  5. [5] § Methods › Statistics ↔ CorrelationAnalysis/MonteCarloKS2test.m, the whole file · a weak match · score 0.64 · Monte Carlo, divergent, fraction, simulation
  6. [6] § Methods › Computational modelling › Model generation and parameter tuning ↔ HomeostaticAdaptationModel/homeostaticAdaptationModel/generate_PN_odor_responses_NadasResampling.m, the whole file · a weak match · score 0.63 · generate PN, PN response, ORN, noise, max, Model
  7. [7] § Methods › Computational modelling › Model generation and parameter tuning ↔ HomeostaticAdaptationModel/homeostaticAdaptationModel/MBmodelBuilder.m, the whole file · a weak match · score 0.63 · PN KC, spike thresholds, connectivity, weight, Model, KCs
  8. [8] § Methods › Functional imaging ↔ activityMap/activityMapParams.m, the whole file · a weak match · score 0.63 · standard deviation, motion, respond, smooth, background, Gaussian
  9. [9] § Methods › Functional imaging ↔ activityMap/activityMapParams.m, the whole file · a weak match · score 0.63 · standard deviation, motion, respond, smooth, background, Gaussian
  10. [10] § Methods › Para‐FlpTag analysis › Spatial standardisation ↔ paraFlpTag/normalizeSkeletonsFlpTagCalyxDivide.m, the whole file · a weak match · score 0.58 · horizontal lobe, vertical lobe, peduncle, branch, calyx, skeleton
  11. [11] § Methods › Para‐FlpTag analysis › Spatial standardisation ↔ multiActivityMaps/normalizeSkeletons.m, the whole file · a weak match · score 0.57 · horizontal lobe, vertical lobe, peduncle, branch, skeleton, node
  12. [12] § Results › Model network reproduces heterogeneous net effects of conflicting adaptations ↔ HomeostaticAdaptationModel/homeostaticAdaptationModel/ModelGenerationOnly_Philippe.m, lines 7–31 · score 0.55 · Hallem Carlson, model instances, mushroom body, noisy, adaptations

Paper

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The authors' code

MATLAB · 105 lines · 4.3 KB · MIT · 2 matches

  1. function skeletons = normalizeSkeletonsFlpTagCalyxDivide(filenames, spacing)
  3. % Let's start with filenames as an cell array of strings specifying the .mat
  4. % files
  6. % calling function must supply the value of 'spacing' which is the node
  7. % spacing on the 'standardized' skeleton
  9. % The purpose of this function is normalize the lengths of the vertical
  10. % lobe, peduncle, and horizontal lobes to single "standard" lengths
  11. % each file will be a .mat file containing multiple activityMap objects -
  12. % they would all have the same skeleton but would be different movies (e.g.
  13. % odor, odor+ATP, ATP alone, and the pipette in different locations)
  15. % Let's try returning the array of skeleton objects ('skeletons')
  17. numFiles = length(filenames);
  19. % length will store the length of each branch in each file
  20. lengths = zeros(numFiles,4); % 1 = c, 2 = p, 3 = h, 4 = v
  21. % the length
  22. peduncleLength = zeros(numFiles);
  23. % junctionIndices will store the index of the node on each skeleton that is
  24. % the junction
  25. junctionIndices = zeros(numFiles,1);
  27. for i=1:numFiles
  28. i
  29. % loop through all files
  30. % read in .mat file
  31. load(filenames{i}); % assumes the .mat file contains a variable called s which has the skeleton
  32. skeletons(i) = s;
  33. skeletons(i) = skeletons(i).divideSkeleton; % use the normal vector to divide the calyx from the peduncle
  35. % find the node that has >2 connections - that's the junction
  36. nodeLinks = {skeletons(i).nodes.links};
  37. numLinksPerNode = zeros(length(nodeLinks),1);
  38. for j=1:length(nodeLinks)
  39. numLinksPerNode(j) = length(nodeLinks{j});
  40. end
  42. [maxLinks,junctionIndices(i)] = max(numLinksPerNode);
  43. if maxLinks~=3
  44. error('the node with the most links does not have 3 links!');
  45. end
  46. % to measure its peduncle, vertical lobe and
  47. % horizontal lobe
  48. % just measure the distance along the skeleton between each end point and
  49. % the junction
  50. % Whereas in Amin et al. 2020, the skeleton is always drawn from vertical lobe tip to
  51. % calyx, then the horizontal lobe is added from junction to horizontal
  52. % lobe tip last [so the endNodes were in the order: vertical, calyx,
  53. % horizontal]
  54. % In contrast, here we have drawn the skeletons first calyx to
  55. % horizontal lobe, then the vertical lobe is added from the junction to
  56. % the vertical tip.
  57. % Thus endNodes are in the order: calyx, horizontal, vertical
  58. endNodes = skeletons(i).findEndNodes();
  60. calyx = endNodes(1);
  61. horEnd = endNodes(2);
  62. vertEnd = endNodes(3);
  63. % Get the distances from the calyx end to all nodes in the skeleton
  64. [distancesFromCalyx,~] = skeletons(i).getDistances(calyx);
  66. % Get the distances from the junction node to all nodes in the skeleton
  67. [distancesFromJunction,~] = skeletons(i).getDistances(junctionIndices(i));
  69. if length(endNodes)~=3
  70. error('number of endNodes ~= 3!');
  71. end
  73. calyxLength = distancesFromCalyx(skeletons(i).dividingNode); % the dividingNode-th node is the division between calyx and peduncle
  74. peduncleLength = distancesFromJunction(skeletons(i).dividingNode);
  75. horLength = distancesFromJunction(horEnd);
  76. vertLength = distancesFromJunction(vertEnd);
  77. lengths(i,:) = [calyxLength, peduncleLength, horLength, vertLength];
  79. end
  80. meanLengths = mean(lengths,1);
  81. disp('mean lengths =');
  82. meanLengths
  84. % Divide every length by the mean length for that lobe
  85. stretchFactors = lengths./repmat(meanLengths, numFiles, 1);
  87. % Loop over all files again
  88. % For each set of 9 skeletons (each file), modify the skeleton
  89. for i=1:numFiles
  90. % label the branches (divide the skeleton into segments)
  91. % here it should be calyx = 1, peduncle = 2, horizontal = 3, vertical = 4
  92. % Note this is different from Amin et al 2020 or from the other
  93. % normalize function which doesn't divide calyx from peduncle
  94. skeletons(i) = skeletons(i).labelBranchesSepCalyx();
  95. skeletons(i).drawDividedSkeleton; %%%%%%%%%%%%%%%%%%%COMMENT OUT - FOR DEBUGGING ONLY
  97. % Re-draw the spaced nodes, emanating from the junction
  98. % Set the starting point to be the junction
  99. skeletons(i).userStartingPoint = skeletons(i).nodes(junctionIndices(i)).realCoords;
  100. % skeletons(i).userStartingPoint = skeletons(i).nodes(1).realCoords; % for debugging
  101. skeletons(i) = skeletons(i).createSpacedNodes(spacing*stretchFactors(i,:));
  102. skeletons(i) = skeletons(i).createVoronoiMask();
  103. end
  105. end

normalizeSkeletonsFlpTagCalyxDivide.m at commit 0b2da3f, under MIT · at the source

Overview

Authors: Gregor A Bergmann1,2, Melissa W Tan1,2, Katie Greenin‐Whitehead1,2, Philippe J Fischer1,2, Thomas C Cozens1,2, Andrew C Lin1,2
ORCID iDs: Gregor A Bergmann, Melissa W Tan, Katie Greenin‐Whitehead, Philippe J Fischer, Thomas C Cozens, Andrew C Lin
  1. School of Biosciences, University of Sheffield, Firth Court, Western Bank, Sheffield, United Kingdom
  2. Neuroscience Institute, University of Sheffield, Firth Court, Western Bank, Sheffield, United Kingdom
Institutions: University of Sheffield (United Kingdom)
Journal: The Journal of physiology, volume 604, issue 7, pages 3005-3031
Dates: received 27 October 2025; accepted 4 February 2026; published online 5 March 2026; in print 1 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1113/jp290394 · PMID 41784532 · PMCID PMC13039246 · OpenAlex W7133781893
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: drosophila (organism), systems (subfield)
Methods: Spectral & time-frequency, Statistics, Evoked potentials, Connectivity, Machine learning, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Drosophila, feedback inhibition, homeostatic plasticity, mushroom body, olfaction
MeSH: Adaptation, Physiological*, Feedback, Physiological*, Mushroom Bodies*, Nerve Net*, Neural Inhibition*, Neurons*, Animals, Drosophila, Drosophila melanogaster, Homeostasis, Neuronal Plasticity (* major topic)
Topic: Neurobiology and Insect Physiology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Biotechnology and Biological Sciences Research Council (BB/T007222/1, BB/S016031/1, BB/M011151/1, BB/X014568/1, BB/X000273/1); European Research Council (639489, 225814); Wellcome Trust (225814/Z/22/Z)
Citations: not cited yet (Europe PMC); 75 references in the paper

Abstract

Abstract: Neural networks maintain stable activity levels by compensating for perturbations through homeostatic plasticity. However, homeostatic mechanisms operating at different levels may conflict with each other. For example, in inhibitory feedback circuits, if inhibitory neurons receive excess excitation, compensation at a ‘local’ level (e.g. reducing inhibitory neurons’ activity) could conflict with ‘network‐level’ compensation (e.g. suppressing the excitatory neurons responsible for overexciting the inhibitory neurons). We studied this problem in the Drosophila mushroom body, where excitatory Kenyon cells (KCs) receive feedback inhibition from the anterior paired lateral (APL) neuron. Dual‐colour calcium imaging revealed that prolonged (24 h) artificial activation of KCs causes APL to become less sensitive to KC activity. Meanwhile, KCs compensate for their excess activity by reducing excitation, yet this change is opposed by reduced inhibition from APL. This conflict meant that KCs did not consistently show the expected homeostatic reduction in odour responses. Our findings show that neurons sometimes adapt their activity locally in a way that counteracts broader adaptations in the network.

Key points: Neural networks maintain stable activity levels through homeostatic plasticity – but what physiological variables are stabilised?

In inhibitory feedback circuits, local and network‐level compensation might conflict. For example, if excitatory neurons are overactive, they might compensate by becoming less excitable. But if inhibitory neurons compensate for the excess excitation by also becoming less excitable, this would decrease inhibition onto the excitatory neurons and increase their activity.

We tested this idea in the fruit fly brain, where excitatory Kenyon cells (KCs) get negative feedback from an inhibitory neuron called anterior paired lateral (APL).

After overactivation of KCs, APL becomes less sensitive to KCs. The resulting loss of inhibition onto KCs counteracts KCs’ attempts to reduce their activity.

These results show that adaptation at local and network levels can conflict with each other.

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 12 matches between paragraphs and lines of code.

aclinlab/calcium-imaging

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: a963384aba0e6f9b62b84aae20d0f8f60548a581, 16 February 2025
Languages: MATLAB (30)
Size: 49 files, 30 scripts
Software Heritage: archived
Found in: “Data availability statement”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (3 files), Image Processing Toolbox (2 files), shadedErrorBar (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
32 files
At the source: github.com/aclinlab/calcium-imaging

aclinlab/bergmann-et-al

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 0b2da3f9c865626f8858881a468394c8eff68fa7, 14 May 2026
Languages: MATLAB (51)
Size: 72 files, 51 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (8 files), Image Processing Toolbox (3 files), Parallel Computing Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
53 files
At the source: github.com/aclinlab/bergmann-et-al

The paper's code and data availability statement is in the Data section.

Tracing map

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What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 81 scripts, each with its path and the digest of its content;
  • 12 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

No dataset and no data link were found in the paper.

Data availability statement

All data necessary to reproduce our findings and figures are included in Dataset S1. Analysis code is available on GitHub at https://github.com/aclinlab/calcium‐imaging (https://github.com/aclinlab/calcium-imaging) and https://github.com/aclinlab/bergmann‐et‐al (https://github.com/aclinlab/bergmann-et-al).

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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 11 MeSH terms, 3 funders, 74 references.

Cite

This paper

Bergmann, G. A., Tan, M. W., Greenin‐Whitehead, K., Fischer, P. J., Cozens, T. C., & Lin, A. C. (2026). Conflicting adaptations in an inhibitory feedback circuit. The Journal of physiology, 604(7), 3005-3031. https://doi.org/10.1113/jp290394

BibTeX

@article{bergmann2026conflicting,
author = {Bergmann, Gregor A and Tan, Melissa W and Greenin‐Whitehead, Katie and Fischer, Philippe J and Cozens, Thomas C and Lin, Andrew C},
title = {{Conflicting adaptations in an inhibitory feedback circuit}},
journal = {The Journal of physiology},
year = {2026},
month = mar,
volume = {604},
number = {7},
pages = {3005--3031},
publisher = {Wiley},
issn = {0022-3751},
doi = {10.1113/jp290394},
url = {https://doi.org/10.1113/jp290394},
pmid = {41784532},
pmcid = {PMC13039246}
}

RIS

TY - JOUR
AU - Bergmann, Gregor A
AU - Tan, Melissa W
AU - Greenin‐Whitehead, Katie
AU - Fischer, Philippe J
AU - Cozens, Thomas C
AU - Lin, Andrew C
TI - Conflicting adaptations in an inhibitory feedback circuit
T2 - The Journal of physiology
J2 - J Physiol
PY - 2026
DA - 2026/03/05
VL - 604
IS - 7
SP - 3005
EP - 3031
SN - 0022-3751
PB - Wiley
DO - 10.1113/jp290394
UR - https://doi.org/10.1113/jp290394
LA - en
ER -

CSL-JSON

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