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Synapse-like specializations at dopamine release sites orchestrate efficient and precise neuromodulatory signaling.

Code ↔ Paper

5 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 5 matches
  1. [1] § MATERIALS AND METHODS › Data acquisition and analysis of EASI-FISH volume ↔ CNNs_GaussianNoiseAdder.py, lines 97 to 171 · score 0.80 · ReLU, fully connected, max pooling, layer, convolutional, padding · Report a mismatch
  2. [2] § MATERIALS AND METHODS › Data acquisition and analysis for neuronal activity imaging ↔ summarizeMiniData.m, lines 67 to 147 · score 0.69 · leaky cumulative minimum, smooth F0, sparsity, traces, filtering · Report a mismatch
  3. [3] § MATERIALS AND METHODS › Data acquisition and analysis for neuronal activity imaging ↔ processMiniData.m, lines 133 to 173 · score 0.58 · contiguity constraints, sparsity, overlap, NNMF, traces, filtering · Report a mismatch
  4. [4] § MATERIALS AND METHODS › Data acquisition and analysis of EASI-FISH volume ↔ Gui_vesicle_detection.py, lines 602 to 653 · score 0.55 · connected component, Euclidean distance, algorithm, threshold, classified · Report a mismatch
  5. [5] § MATERIALS AND METHODS › Data acquisition and analysis for neuronal activity imaging ↔ correlationAnalysis/correlationsBetweenBsnDf.m, lines 1 to 52 · score 0.52 · dopamine activity, NiNox, body, channel, protein, neurons · Report a mismatch

Paper

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The authors' code

Python · 186 lines · 6.4 KB · no license · 1 match

  1. # -*- coding: utf-8 -*-
  2. """
  3. Created on Wed Nov 20 15:08:53 2019
  5. Convolutional neural networks (MultiClass and MultiClassPost) used for
  6. detecting synaptic vesicles and class to add Gaussian Noise to tensors.
  8. @author: imbroscb
  9. """
  11. import torch
  12. from torch import nn
  13. import torch.nn.functional as F
  15. # %%
  18. class MultiClass(nn.Module):
  20. def __init__(self, conv_out1=16, conv_out2=32, conv_out3=64, conv_out4=128,
  21. filtersize=7, strd=1, paddingsize=2, H1=300, H2=150, H3=50,
  22. out=2):
  23. super().__init__()
  24. self.conv_out1 = conv_out1
  25. self.conv_out2 = conv_out2
  26. self.conv_out3 = conv_out3
  27. self.conv_out4 = conv_out4
  28. self.filtersize = filtersize
  29. self.strd = strd
  30. self.H1 = H1
  31. self.H2 = H2
  32. self.H3 = H3
  33. self.out = out
  34. self.paddingsize = paddingsize
  36. # calculate feature map dimension after convolution-pooling layers
  37. cutting = self.filtersize - 1 - (2 * self.paddingsize)
  38. dimension = 40
  39. for conv in range(4):
  40. dimension = dimension - cutting
  41. if dimension % 2 == 1:
  42. dimension -= 1
  43. dimension = dimension / self.strd
  44. self.input1 = int(dimension / 2)
  46. # define convolutional layers
  47. self.conv1 = nn.Conv2d(1, self.conv_out1, self.filtersize, self.strd,
  48. padding=self.paddingsize)
  49. self.conv2 = nn.Conv2d(self.conv_out1, self.conv_out2, self.filtersize,
  50. self.strd, self.paddingsize)
  51. self.conv3 = nn.Conv2d(self.conv_out2, self.conv_out3, self.filtersize,
  52. self.strd, self.paddingsize)
  53. self.dropout_c34 = nn.Dropout(0.3)
  54. self.conv4 = nn.Conv2d(self.conv_out3, self.conv_out4, self.filtersize,
  55. self.strd, self.paddingsize)
  56. self.dropout_c4f1 = nn.Dropout(0.3)
  58. # define fully connected (fc) layers
  59. self.fc1 = nn.Linear(self.input1 * self.input1 * self.conv_out4,
  60. self.H1)
  61. self.dropout_f12 = nn.Dropout(0.75)
  62. self.fc2 = nn.Linear(self.H1, self.H2)
  63. self.dropout_f23 = nn.Dropout(0.65)
  64. self.fc3 = nn.Linear(self.H2, self.H3)
  65. self.dropout_f34 = nn.Dropout(0.55)
  66. self.fc4 = nn.Linear(self.H3, self.out)
  68. def forward(self, x):
  70. # apply the convolution operation and the activation function ReLU
  71. x = F.relu(self.conv1(x))
  72. x = F.relu(self.conv2(x))
  73. x = F.relu(self.conv3(x))
  74. x = self.dropout_c34(x)
  75. x = F.relu(self.conv4(x))
  77. # apply the max pooling operation
  78. x = F.max_pool2d(x, 2, 2)
  79. x = self.dropout_c4f1(x)
  81. # fatten x
  82. x = x.view(-1, self.input1 * self.input1 * self.conv_out4)
  84. # apply the ReLU activation function on fc layers
  85. x = F.relu(self.fc1(x))
  86. x = self.dropout_f12(x)
  87. x = F.relu(self.fc2(x))
  88. x = self.dropout_f23(x)
  89. x = F.relu(self.fc3(x))
  90. x = self.dropout_f34(x)
  91. x = self.fc4(x)
  92. return x
  94. # %%
  97. class MultiClassPost(nn.Module):
  99. def __init__(self, conv_out1=16, conv_out2=32, conv_out3=64, conv_out4=128,
  100. filtersize=7, strd=1, paddingsize=2, H1=300, H2=150, H3=50,
  101. out=2):
  102. super().__init__()
  103. self.conv_out1 = conv_out1
  104. self.conv_out2 = conv_out2
  105. self.conv_out3 = conv_out3
  106. self.conv_out4 = conv_out4
  107. self.filtersize = filtersize
  108. self.strd = strd
  109. self.H1 = H1
  110. self.H2 = H2
  111. self.H3 = H3
  112. self.out = out
  113. self.paddingsize = paddingsize
  115. # calculate feature map dimension after convolution-pooling layers
  116. cutting = self.filtersize - 1 - (2 * self.paddingsize)
  117. dimension = 80
  118. for conv in range(4):
  119. dimension = dimension - cutting
  120. if dimension % 2 == 1:
  121. dimension -= 1
  122. dimension = dimension / self.strd
  123. self.input1 = int(dimension / 2)
  125. # define convolutional layers
  126. self.conv1 = nn.Conv2d(1, self.conv_out1, self.filtersize, self.strd,
  127. padding=self.paddingsize)
  128. self.conv2 = nn.Conv2d(self.conv_out1, self.conv_out2, self.filtersize,
  129. self.strd, self.paddingsize)
  130. self.conv3 = nn.Conv2d(self.conv_out2, self.conv_out3, self.filtersize,
  131. self.strd, self.paddingsize)
  132. self.dropout_c34 = nn.Dropout(0.3)
  133. self.conv4 = nn.Conv2d(self.conv_out3, self.conv_out4, self.filtersize,
  134. self.strd, self.paddingsize)
  135. self.dropout_c4f1 = nn.Dropout(0.3)
  137. # define fully connected (fc) layers
  138. self.fc1 = nn.Linear(self.input1 * self.input1 * self.conv_out4,
  139. self.H1)
  140. self.dropout_f12 = nn.Dropout(0.75)
  141. self.fc2 = nn.Linear(self.H1, self.H2)
  142. self.dropout_f23 = nn.Dropout(0.65)
  143. self.fc3 = nn.Linear(self.H2, self.H3)
  144. self.dropout_f34 = nn.Dropout(0.55)
  145. self.fc4 = nn.Linear(self.H3, self.out)
  147. def forward(self, x):
  149. # apply the convolution operation and the activation function ReLU
  150. x = F.relu(self.conv1(x))
  151. x = F.relu(self.conv2(x))
  152. x = F.relu(self.conv3(x))
  153. x = self.dropout_c34(x)
  154. x = F.relu(self.conv4(x))
  156. # apply the max pooling operation
  157. x = F.max_pool2d(x, 2, 2)
  158. x = self.dropout_c4f1(x)
  160. # fatten x
  161. x = x.view(-1, self.input1 * self.input1 * self.conv_out4)
  163. # apply the ReLU activation function on fc layers
  164. x = F.relu(self.fc1(x))
  165. x = self.dropout_f12(x)
  166. x = F.relu(self.fc2(x))
  167. x = self.dropout_f23(x)
  168. x = F.relu(self.fc3(x))
  169. x = self.dropout_f34(x)
  170. x = self.fc4(x)
  171. return x
  173. # %%
  176. class GaussianNoiseAddition(object):
  177. def __init__(self, mean=0., stdev=1.):
  178. self.mean = mean
  179. self.stdev = stdev
  181. def __call__(self, tensor):
  182. return tensor + torch.randn(tensor.size()) * self.stdev + self.mean
  184. def __repr__(self):
  185. return self.__class__.name__ + '(mean={0}, std{1})'.format(self.mean,
  186. self.stdev)

CNNs_GaussianNoiseAdder.py at commit 016847b, no license · at the source

Overview

Authors: Chandima Bulumulla1, Deng Zhang1, Deepika Walpita1, Nirmala Iyer1, Mark Eddison1, David Ackerman1, Hideo Otsuna1, Xianling Zhao1, Shuqin Zhang1, Shihong M. Gao1, Nan Wang1, Abraham G. Beyene1
ORCID iDs: Chandima Bulumulla, Deng Zhang, Nirmala Iyer, Mark Eddison, David Ackerman, Hideo Otsuna, Xianling Zhao, Shihong M. Gao, Nan Wang, Abraham G. Beyene
  1. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
Institutions: Howard Hughes Medical Institute (United States); Janelia Research Campus (United States)
Journal: Science advances, volume 12, issue 40, article eaei0203
Dates: received 13 April 2026; accepted 21 August 2026; published online 2 October 2026; in print October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aei0203 · PMID 42826180 · PMCID PMC13632744 · OpenAlex W7216097993
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Machine learning, fMRI & imaging, Single-unit activity, calcium imaging
MeSH: Dopamine*, Neurotransmitter Agents*, Signal Transduction*, Synapses*, Animals, Mice, Receptors, Dopamine D1, Receptors, Dopamine D2, Synaptic Transmission (* major topic)
Journal subjects: Neuroscience, Cellular Neuroscience
Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Howard Hughes Medical Institute (HHMI)
Citations: not cited yet (Europe PMC); 57 references in the paper

Abstract

Neuromodulators are generally understood to act through expansive projections that broadcast signals relatively indiscriminately, in stark contrast to the precise, synapse-bound organization of fast neurotransmitters like glutamate and GABA. This dichotomy has left the local architecture of neuromodulatory release and receptor engagement largely obscure. Here, using dopamine as a model system, we reveal that neuromodulatory signaling can be guided by precise molecular specificity. We show that dopamine axons exhibit a tropism for neurons expressing D1 or D2 dopamine receptors. Single-bouton–resolved optical imaging of release shows that dopamine is not broadcast stochastically but is highly localized to varicosities that form direct, synapse-like appositions with receptor-expressing somata and dendrites. To map endogenous receptor organization relative to release sites, we generated ALFA-tagged D1 and D2 receptor knock-in mice (ALFADoR mice). In cultures and intact tissue, D1 and D2 receptors formed discrete puncta rather than exhibiting diffuse membrane distributions. Across striatum, amygdala, and prefrontal cortex, receptor puncta were preferentially positioned near dopamine varicosities, and apposed receptor puncta were larger than nonapposed puncta. These findings reveal a synapse-like architecture for dopamine signaling in which release sites and receptor clusters are spatially coupled to support a precise and efficient neuromodulation.

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 5 matches between paragraphs and lines of code.

janeliascicomp/nnmf

License: BSD-3-Clause
State: the link answers, verified on 5 October 2026
Evidence: files inventoried
Commit: 8d5d5ecd61b48771cbf31c4419c7aa672a5fcd47, 20 December 2024
Languages: MATLAB (67)
Size: 73 files, 67 scripts
Software Heritage: not archived
Found in: the text, “Data acquisition and analysis for neuronal activ”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (9 files), Image Processing Toolbox (5 files), Statistics and Machine Learning Toolbox (5 files)
Availability: 1 check, the latest on 5 October 2026: the link answers
  • 5 October 2026: the link answers
69 files
At the source: github.com/JaneliaSciComp/nnmf/commits/main/

Imbrosci/synaptic-vesicles-detection

License: none: the authors keep all their rights
State: the link answers, verified on 5 October 2026
Evidence: files inventoried
Commit: 016847bedf90b6952f4e9e1f80309d40534ffbca, 26 January 2024
Languages: Python (5)
Size: 9 files, 5 scripts
Software Heritage: not archived
Found in: the text, “TEM sample preparation and imaging”
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: PyTorch (4 files), NumPy (3 files), Matplotlib (1 file), OpenCV (1 file), pandas (1 file), Pillow (1 file), scikit-learn (1 file), SciPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 5 October 2026: the link answers
  • 5 October 2026: the link answers
6 files
At the source: github.com/Imbrosci/synaptic-vesicles-detection

JaneliaSciComp/beyene-lab

License: BSD-3-Clause
State: the link answers, verified on 5 October 2026
Evidence: files inventoried
Commit: f6a01dd2c09fdc1d3b83346f6da8557cb6906bef, 8 September 2024
Languages: MATLAB (19)
Size: 23 files, 19 scripts
Software Heritage: not archived
Found in: “Data, code, and materials availability:”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (5 files), Statistics and Machine Learning Toolbox (5 files)
Availability: 1 check, the latest on 5 October 2026: the link answers
  • 5 October 2026: the link answers
21 files
At the source: github.com/JaneliaSciComp/beyene-lab

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

Tracing map

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Data

Datasets cited

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. Access to AlfaDoR transgenic mouse lines produced as part of the study can be made available pending materials transfer agreement between HHMI and the requesting institution. All such requests should be directed to the corresponding author A.G.B at . Raw imaging data are accessible under DOI: 10.5281/zenodo.22030234 (http://dx.doi.org/10.5281/zenodo.22030234). Analysis codes can be accessed at https://github.com/JaneliaSciComp/beyene-lab and https://github.com/JaneliaSciComp/nnmf/commits/main/.

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

Versions

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Version 1, 6 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 9 MeSH terms, 1 funder, 57 references.

Cite

This paper

Bulumulla, C., Zhang, D., Walpita, D., Iyer, N., Eddison, M., Ackerman, D., Otsuna, H., Zhao, X., Zhang, S., Gao, S. M., Wang, N., & Beyene, A. G. (2026). Synapse-like specializations at dopamine release sites orchestrate efficient and precise neuromodulatory signaling. Science advances, 12(40), eaei0203. https://doi.org/10.1126/sciadv.aei0203

BibTeX

@article{bulumulla2026synapse,
author = {Bulumulla, Chandima and Zhang, Deng and Walpita, Deepika and Iyer, Nirmala and Eddison, Mark and Ackerman, David and Otsuna, Hideo and Zhao, Xianling and Zhang, Shuqin and Gao, Shihong M. and Wang, Nan and Beyene, Abraham G.},
title = {{Synapse-like specializations at dopamine release sites orchestrate efficient and precise neuromodulatory signaling}},
journal = {Science advances},
year = {2026},
month = oct,
volume = {12},
number = {40},
pages = {eaei0203},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aei0203},
url = {https://doi.org/10.1126/sciadv.aei0203},
pmid = {42826180},
pmcid = {PMC13632744}
}

RIS

TY - JOUR
AU - Bulumulla, Chandima
AU - Zhang, Deng
AU - Walpita, Deepika
AU - Iyer, Nirmala
AU - Eddison, Mark
AU - Ackerman, David
AU - Otsuna, Hideo
AU - Zhao, Xianling
AU - Zhang, Shuqin
AU - Gao, Shihong M.
AU - Wang, Nan
AU - Beyene, Abraham G.
TI - Synapse-like specializations at dopamine release sites orchestrate efficient and precise neuromodulatory signaling
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/10/02
VL - 12
IS - 40
SP - eaei0203
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aei0203
UR - https://doi.org/10.1126/sciadv.aei0203
LA - en
ER -

CSL-JSON

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"PMCID": "PMC13632744",
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