OSCR

Hippocampal CA3 connectomics reveals a gradient of mossy fiber inputs and selective feedforward inhibition onto pyramidal cells.

Code ↔ Paper

16 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 16 matches
  1. [1] § Methods › Registration to the Allen CCF › Mapping Allen CCF to stereotactic coordinates ↔ mousebrains/core.py, lines 80–135 · score 0.95 · ccfv3 orig coords, ventral dorsal, anterior posterior, medial lateral, stereotactic coordinates, swap
  2. [2] § Methods › Identification of subcellular target locations of inhibitory axons ↔ notebooks/paper01_extfig7_get_skeletons_of_pyr_cells.ipynb, lines 66–80 · score 0.92 · interp_kind, resample_spacing, root_point, pcg_skel.pcg_skeleton, MeshParty, swc
  3. [3] § Methods › Registration to the Allen CCF › Mapping Allen CCF to stereotactic coordinates ↔ mousebrains/core.py, lines 80–135 · score 0.86 · anterior posterior, dorsal ventral, medial lateral, stereotactic coordinates, CCF, space
  4. [4] § Methods › Identification of subcellular target locations of inhibitory axons ↔ notebooks/CA3_Inhib_Pyr_Final.ipynb, lines 24–94 · score 0.85 · pcg_skel.pcg_skeleton, skeleton edges, inhibitory axon, root point, high resolution, connected
  5. [5] § Results › Dataset overview ↔ mousebrains/templates.py, lines 139–161 · score 0.81 · Allen Mouse Brain, template brain, C57BL, adult, tomography, CCF
  6. [6] § Methods › Analysis of MF–Pyr connectivity ↔ notebooks/paper01_fig4_MF_pyr_bipartite_motif_analysis.ipynb, lines 44–186 · score 0.79 · bipartite graph, radius models, configuration model, MF Pyr, shuffled, randomizes
  7. [7] § Methods › Analysis of MF–Pyr connectivity ↔ notebooks/paper01_fig4_MF_pyr_bipartite_motif_analysis.ipynb, lines 389–420 · score 0.77 · MF Pyr bipartite, bipartite motifs, projected graph, triangle, triplet, node
  8. [8] § Methods › Bouton extraction and vesicle segmentation ↔ notebooks/paper01_fig5_extfig6_MF_vesicle_counting.ipynb, lines 369–408 · score 0.67 · vesicle segment, OpenCV, cubic, cv2, resize, mask
  9. [9] § Methods › Bouton extraction and vesicle segmentation ↔ notebooks/paper01_fig5_extfig6_MF_bouton_extractions_based_on_vertex_density.ipynb, lines 307–413 · score 0.67 · voxel density, bouton extraction, away, score, vertex, radius
  10. [10] § Methods › Registration to the Allen CCF › Registration between CA3 EM and μCT volume ↔ cloudvolume/__init__.py, lines 1–68 · score 0.67 · neuroglancer compatible, image stack, cloud volume, precomputed, Python
  11. [11] § Methods › Proofreading › Proofreading rate ↔ notebooks/Axon_Proofreading_Final.ipynb, lines 721–849 · score 0.67 · axon proofreading, axon lengths, skeleton length, summed, timestamps, branches
  12. [12] § Methods › Analysis of MF–Pyr connectivity ↔ notebooks/paper01_fig4_MF_pyr_conn_analysis.ipynb, lines 766–876 · score 0.65 · radius models, MF Pyr, Pyr cells, shuffled, randomizes, edges
  13. [13] § Results › Increased sharing of MF inputs in distal pyramidal cells ↔ notebooks/paper01_fig4_MF_pyr_conn_analysis.ipynb, lines 649–750 · score 0.65 · randomized connectivity, radius model, common MF, MF Pyr, bouton, cell
  14. [14] § Methods › Laminar organization and spatial axes ↔ notebooks/paper01_extfig3_layer_boundaries_and_cell_locations_depths_curve_distance_synapse_locations.ipynb, lines 359–402 · score 0.64 · layer boundary, kernel, curved, oriens, radiatum, fitted
  15. [15] § Methods › Registration to the Allen CCF › Registration between CA3 μCT volume and the Allen CCF ↔ mousebrains/core.py, lines 221–258 · score 0.56 · CA3 EM, CCF, uCT, mousebrains, transform, Allen
  16. [16] § Methods › Laminar organization and spatial axes ↔ notebooks/paper01_extfig3_layer_boundaries_and_cell_locations_depths_curve_distance_synapse_locations.ipynb, lines 359–402 · score 0.54 · Curve distance, depth, fitted, boundary, lucidum, location

Paper

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The authors' code

Python · 361 lines · 12 KB · GPL-3.0 · 3 matches

  1. # This script is part of mousebrains (http://www.github.com/schlegelp/navis-mousebrains).
  2. # Copyright (C) 2020 Philipp Schlegel
  4. # This program is free software: you can redistribute it and/or modify
  5. # it under the terms of the GNU General Public License as published by
  6. # the Free Software Foundation, either version 3 of the License, or
  7. # (at your option) any later version.
  8. #
  9. # This program is distributed in the hope that it will be useful,
  10. # but WITHOUT ANY WARRANTY; without even the implied warranty of
  11. # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
  12. # GNU General Public License for more details.
  14. import os
  15. import pathlib
  16. import warnings
  18. from navis import transforms
  20. import numpy as np
  21. import pandas as pd
  23. from .download import get_data_home
  25. __all__ = [
  26. "register_transforms",
  27. ]
  29. # Read in meta data
  30. fp = os.path.dirname(__file__)
  32. data_filepath = os.path.join(fp, "data")
  34. CCF_STEREO_TRANSFORM = None
  37. def inject_paths():
  38. """Register mousebrains paths with navis."""
  39. # Navis scans paths in order and if the same transform is found again
  40. # it will be ignored. Hence order matters in some circumstances!
  41. # First add the data home path
  42. transforms.registry.register_path(get_data_home(), trigger_scan=False)
  44. # Next add default path
  45. default_path = os.path.expanduser("~/mousebrains-data")
  46. if default_path not in transforms.registry.transpaths:
  47. transforms.registry.register_path(default_path, trigger_scan=False)
  49. transforms.registry.scan_paths()
  52. def ccf_coronal_sagital(points):
  53. """Transform CCF points from coronal to sagital orientation.
  55. Points are expected to be in um space.
  57. """
  58. # Swap x and z axes = rotate 90 degrees clock-wise
  59. points = points[:, [2, 1, 0]]
  60. # Flip along (new) x-axis to make it a counter-clockwise rotation
  61. # Note that 528 * 28 is the width of the orignal CCF in um
  62. points[:, 0] = points[:, 0] * -1 + (528 * 25)
  63. return points
  66. def ccf_sagital_coronal(points):
  67. """Transform CCF points from sagital to coronal orientation.
  69. Points are expected to be in um space.
  71. """
  72. # Flip along (new) x-axis to make it a counter-clockwise rotation
  73. # Note that 528 * 28 is the width of the orignal CCF in um
  74. points[:, 0] = (points[:, 0] - (528 * 25)) * -1
  75. # Swap x and z axes = rotate 90 degrees counter-clock-wise
  76. points = points[:, [2, 1, 0]]
  77. return points
  80. def ccf_to_stereotaxic(points):
  81. """Transform 3d points from CCFv3 space to stereotaxic coordinates.
  83. This function uses a coordinate transform generated by Perens et al., 2023.
  84. See below for the reference and the README.md in the data folder for
  85. further details.
  88. Parameters
  89. ----------
  90. points : array-like, shape (N, 3)
  91. Points in CCFv3 space to be transformed.
  92. Expected to be in microns.
  94. Returns
  95. -------
  96. points_xf : ndarray, shape (N, 3)
  97. Transformed points in stereotaxic coordinate space in millimeters.
  98. x = medial-lateral, y = anterior-posterior, z = dorsal-ventral
  100. References
  101. ----------
  102. Perens, J., Salinas, C. G., Roostalu, U., Skytte, J. L., Gundlach, C.,
  103. Hecksher-Sørensen, J., Dahl, A. B., & Dyrby, T. B. (2023).
  104. Multimodal 3D Mouse Brain Atlas Framework with the Skull-Derived Coordinate System.
  105. Neuroinformatics, 21(2), 269–286. https://doi.org/10.1007/s12021-023-09623-9
  107. """
  108. # Make sure these are numpy arrays
  109. points_xf = np.asarray(points)
  111. # Convert to 25um voxel space (that's what the transformation field is in)
  112. points_xf = points_xf / 25.0
  114. # Swap axes such that we have x = medial-lateral, y = anterior→posterior, z = ventral→dorsal
  115. points_xf = points_xf[:, [2, 0, 1]]
  117. # Add a 70 voxel (1.75mm) padding in y
  118. points_xf[:, 1] += 70
  120. # Invert z axis such that we get ventral->dorsal instead of dorsal->ventral
  121. # Note the z-axis has shape 320 meaning index 0 needs to become 319.
  122. points_xf[:, 2] = 319 - points_xf[:, 2]
  124. # Load the transform field if not already done
  125. global CCF_STEREO_TRANSFORM
  126. if CCF_STEREO_TRANSFORM is None:
  127. import nrrd
  129. fp = os.path.join(data_filepath, "ccfv3_orig_coords_all.nrrd")
  130. disp_field, header = nrrd.read(fp)
  131. CCF_STEREO_TRANSFORM = transforms.DeformationFieldTransform(disp_field)
  133. # Lookup the stereotactic coordinates
  134. # The deformation field returns z/y/x which is why we're reversing the last axis
  135. return CCF_STEREO_TRANSFORM.xform(points_xf)[:, ::-1]
  138. def search_register_path(path, verbose=False):
  139. """Search a single path for transforms and register them."""
  140. path = pathlib.Path(path).expanduser()
  142. if verbose:
  143. print(f"Searching {path}")
  145. # Skip if this isn't an actual path
  146. if not path.is_dir():
  147. return
  149. # Find transform files/directories
  150. for ext, tr in zip(
  151. [".h5", ".list"], [transforms.h5reg.H5transform, transforms.cmtk.CMTKtransform]
  152. ):
  153. for hit in path.rglob(f"*{ext}"):
  154. if hit.is_dir() or hit.is_file():
  155. # These files are inside the CMTK folders and show as
  156. # symlinks in OSX/Linux but as files (?) in Windows
  157. # Hence we need to manually exclude them.
  158. if hit.name in ("orig.list", "original.list"):
  159. continue
  161. # Register this transform
  162. try:
  163. if "mirror" in hit.name or "imgflip" in hit.name:
  164. transform_type = "mirror"
  165. source = hit.name.split("_")[0]
  166. target = None
  167. else:
  168. transform_type = "bridging"
  169. source = hit.name.split("_")[0]
  170. target = hit.name.split("_")[1].split(".")[0]
  172. # "FAFB" refers to FAFB14 and requires microns
  173. # we will change its label to make this explicit
  174. # and later add a bridging transform
  175. if target == "FAFB":
  176. target = "FAFB14um"
  178. if source == "FAFB":
  179. source = "FAFB14um"
  181. # "JRCFIB2018F" likewise requires microns
  182. if target == "JRCFIB2018F":
  183. target = "JRCFIB2018Fum"
  185. if source == "JRCFIB2018F":
  186. source = "JRCFIB2018Fum"
  188. # "JRCFIB2022M" likewise requires microns
  189. if target == "JRCFIB2022M":
  190. target = "JRCFIB2022Mum"
  192. if source == "JRCFIB2022M":
  193. source = "JRCFIB2022Mum"
  195. # "MANC" likewise requires microns
  196. if target == "MANC":
  197. target = "MANCum"
  199. if source == "MANC":
  200. source = "MANCum"
  202. # Initialize the transform
  203. transform = tr(hit)
  205. if verbose:
  206. print(
  207. f"Registering {hit} ({tr.__name__}) "
  208. f'as "{source}" -> "{target}"'
  209. )
  211. transforms.registry.register_transform(
  212. transform=transform,
  213. source=source,
  214. target=target,
  215. transform_type=transform_type,
  216. )
  217. except BaseException as e:
  218. warnings.warn(f"Error registering {hit} as transform: {str(e)}")
  221. def register_zheng_transforms():
  222. """Register transforms for the Zheng CA3 dataset."""
  223. # Transform from raw to physical space
  224. tr = transforms.AffineTransform(np.diag([18, 18, 45, 1]))
  225. transforms.registry.register_transform(
  226. transform=tr,
  227. source="Zheng_CA3_EMraw", # voxels
  228. target="Zheng_CA3_EM", # nm
  229. transform_type="bridging",
  230. )
  232. # The transform to go from CA3 uCT to EM space
  233. fp = os.path.join(data_filepath, "zheng-ca3-em-to-uct.csv")
  234. lm = pd.read_csv(fp)
  235. tr = transforms.TPStransform(
  236. lm[["x_em_nm", "y_em_nm", "z_em_nm"]].values,
  237. lm[["x_uct_um", "y_uct_um", "z_uct_um"]].values,
  238. )
  239. transforms.registry.register_transform(
  240. transform=tr,
  241. source="Zheng_CA3_EM", # nm
  242. target="Zheng_CA3_uCT", # microns
  243. transform_type="bridging",
  244. )
  246. # Transform to go from CA3 uCT to CCF space (best guess alignment)
  247. fp = os.path.join(data_filepath, "zheng-ca3-uct-to-ccf.csv")
  248. lm = pd.read_csv(fp)
  249. tr = transforms.TPStransform(
  250. lm[["x_ccf", "y_ccf", "z_ccf"]].values,
  251. lm[["x_uct", "y_uct", "z_uct"]].values,
  252. )
  253. transforms.registry.register_transform(
  254. transform=tr,
  255. source="AllenCCF", # microns
  256. target="Zheng_CA3_uCT", # microns
  257. transform_type="bridging",
  258. )
  261. def register_transforms():
  262. """Register transforms with navis."""
  263. # These are the paths we need to scan
  264. data_home = pathlib.Path(get_data_home()).expanduser()
  265. default_path = pathlib.Path("~/mousebrains-data").expanduser()
  267. # Combine while retaining order
  268. search_paths = [data_home]
  269. if default_path not in search_paths:
  270. search_paths.append(default_path)
  272. # Go over all paths and add transforms
  273. for path in search_paths:
  274. # Do not (re-)move this line! Otherwise is_dir() might fail
  275. path = pathlib.Path(path).expanduser()
  277. # Skip if path does not exist
  278. if not path.is_dir():
  279. continue
  281. search_register_path(path)
  283. # Add transforms to go between CCF voxel and micron space
  284. transforms.registry.register_transform(
  285. transform=transforms.AffineTransform(np.diag([10, 10, 10, 1])),
  286. source="AllenCCF10",
  287. target="AllenCCF",
  288. transform_type="bridging",
  289. weight=0.1,
  290. )
  291. transforms.registry.register_transform(
  292. transform=transforms.AffineTransform(np.diag([25, 25, 25, 1])),
  293. source="AllenCCF25",
  294. target="AllenCCF",
  295. transform_type="bridging",
  296. weight=0.1,
  297. )
  298. transforms.registry.register_transform(
  299. transform=transforms.AffineTransform(np.diag([50, 50, 50, 1])),
  300. source="AllenCCF50",
  301. target="AllenCCF",
  302. transform_type="bridging",
  303. weight=0.1,
  304. )
  305. transforms.registry.register_transform(
  306. transform=transforms.AffineTransform(np.diag([100, 100, 100, 1])),
  307. source="AllenCCF100",
  308. target="AllenCCF",
  309. transform_type="bridging",
  310. weight=0.1,
  311. )
  313. # Add some aliases for AllenCCF
  314. for alias in ("AllenCCFum", "AllenCCFv3", "CCFv3"):
  315. tr = transforms.AffineTransform(np.diag([1, 1, 1, 1]))
  316. transforms.registry.register_transform(
  317. transform=tr,
  318. source=alias,
  319. target="AllenCCF",
  320. transform_type="bridging",
  321. weight=0,
  322. )
  324. # Transform to go from coronal to sagital CCF orientation
  325. transforms.registry.register_transform(
  326. transform=transforms.FunctionTransform(ccf_coronal_sagital),
  327. source="AllenCCF_coronal",
  328. target="AllenCCF",
  329. transform_type="bridging",
  330. )
  331. transforms.registry.register_transform(
  332. transform=transforms.FunctionTransform(ccf_sagital_coronal),
  333. source="AllenCCF",
  334. target="AllenCCF_coronal",
  335. transform_type="bridging",
  336. )
  338. # Add the transform to go from AllenCCF (coronal) to the test uCT space
  339. fp = os.path.join(data_filepath, "uct-to-ccf_landmarks.csv")
  340. lm = pd.read_csv(fp)
  341. tr = transforms.TPStransform(
  342. lm[["x_ccf", "y_ccf", "z_ccf"]].values, lm[["x_uct", "y_uct", "z_uct"]].values
  343. )
  344. transforms.registry.register_transform(
  345. transform=tr,
  346. source="AllenCCF_coronal",
  347. target="uCT_test",
  348. transform_type="bridging",
  349. )
  351. # Transform to go from CCFv3 to stereotaxic coordinates
  352. # (Perens et al., 2023)
  353. transforms.registry.register_transform(
  354. transform=transforms.FunctionTransform(ccf_to_stereotaxic),
  355. source="AllenCCF",
  356. target="Stereotaxic",
  357. transform_type="bridging",
  358. )
  360. # Register the Zheng CA3 transforms
  361. register_zheng_transforms()

core.py at commit 0da6eae, under GPL-3.0 · at the source

Overview

Authors: Zhihao Zheng1, Changjoo Park1, Eric W. Hammerschmith1, Ran Lu1, Szi-Chieh Yu1, Marissa Sorek1, Ben Silverman1, Chris S. Jordan1, Amy R. Sterling1, William M. Silversmith1, Philipp Schlegel2,3, Gregory S. X. E. Jefferis2,3, Forrest Collman4, H. Sebastian Seung1,5, David W. Tank1,6,7
ORCID iDs: Zhihao Zheng, Changjoo Park, Ran Lu, Szi-Chieh Yu, Marissa Sorek, Philipp Schlegel, Gregory S. X. E. Jefferis, Forrest Collman, H. Sebastian Seung, David W. Tank
  1. Princeton Neuroscience Institute, Princeton University,Princeton, NJ USA
  2. Neurobiology Division, MRC Laboratory of Molecular Biology,Cambridge, UK
  3. Drosophila Connectomics Group, Department of Zoology, University of Cambridge,Cambridge, UK
  4. Allen Institute for Brain Science,Seattle, WA USA
  5. Computer Science Department, Princeton University,Princeton, NJ USA
  6. Bezos Center for Neural Circuit Dynamics, Princeton University,Princeton, NJ USA
  7. Department of Molecular Biology, Princeton University,Princeton, NJ USA
Institutions: Princeton University (United States); MRC Laboratory of Molecular Biology (United Kingdom); University of Cambridge (United Kingdom); Allen Institute for Brain Science (United States)
Journal: Nature neuroscience, volume 29, issue 9, pages 2311-2323
Dates: received 9 July 2025; accepted 25 June 2026; published online 5 August 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41593-026-02388-9 · PMID 42557444 · PMCID PMC13533834 · OpenAlex W7172545582
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), mouse (organism), systems (subfield)
Methods: Connectivity, Statistics, Machine learning, Graphs, fMRI & imaging
Keywords: 3-D reconstruction, Neural circuits, Brain, Transmission electron microscopy
MeSH: CA3 Region, Hippocampal*, Connectome*, Mossy Fibers, Hippocampal*, Neural Inhibition*, Pyramidal Cells*, Animals, Interneurons, Male, Mice, Mice, Inbred C57BL (* major topic)
Journal subjects: Resource
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIH (UM1 NS132250, K99 NS135650, U19 NS132720, UM1 NS132253, RF1 MH123400, S10OD023602); Simons Foundation
Citations: cited by 1 paper (Europe PMC); 93 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 16 matches between paragraphs and lines of code.

seung-lab/seuron

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c083449b47ce02d72e4d7427cade471df897a5a5, 18 September 2026
Languages: Python (75), Shell (5), Jupyter (1)
Size: 120 files, 81 scripts
Software Heritage: archived
Found in: “Code availability”
Holds: README, license file, environment (requirements.txt, docker/Dockerfile, docker/base/Dockerfile.base-alpine, docker/base/Dockerfile.base-slim, docker/base/Dockerfile.base.test), tests, continuous integration, 1 notebook
Not found: CITATION.cff, documentation
Tools: Matplotlib (1 file), NumPy (1 file), PyTorch (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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83 files
At the source: github.com/seung-lab/seuron

seung-lab/cloud-volume

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 23724c93e6a7fe81c7b2785fb4a324cf1fea2d6f, 19 September 2026
Languages: Python (95)
Size: 5,655 files, 95 scripts
Software Heritage: archived
Found in: “Code availability”
Holds: README, license file, environment (requirements.txt, setup.py), tests, continuous integration
Not found: CITATION.cff, documentation
Tools: NumPy (56 files), Matplotlib (2 files), Pillow (2 files), SciPy (2 files), pandas (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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97 files
At the source: github.com/seung-lab/cloud-volume

navis-org/navis-mousebrains

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0da6eae6e17c10b64f468707fdad7291ce186a64, 4 January 2026
Languages: Python (6)
Size: 21 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (requirements.txt, setup.py), continuous integration
Not found: CITATION.cff, tests, documentation
Tools: NumPy (1 file), pandas (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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8 files
At the source: github.com/navis-org/navis-mousebrains

seung-lab/ca3_paper

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f4d7df639d9253955cb7d76572e1a5baf9414c9c, 21 July 2025
Languages: Jupyter (23)
Size: 57 files, 23 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (requirements.txt), 23 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: Matplotlib (23 files), pandas (23 files), NumPy (22 files), SciPy (20 files), NetworkX (13 files), Plotly (13 files), scikit-learn (10 files), seaborn (7 files), igraph (1 file), imageio (1 file), OpenCV (1 file), Pillow (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
24 files
At the source: github.com/seung-lab/ca3_paper

CAVEconnectome

License: none: the authors keep all their rights
State: the link answers, verified on 27 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 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: github.com/CAVEconnectome

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41593-026-02388-9.

Tracing map

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Data

Datasets cited

Data availability statement

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  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41593-026-02388-9.

Versions

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Version 3, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 4 keywords, 10 MeSH terms, 2 funders, 88 references.

Cite

This paper

Zheng, Z., Park, C., Hammerschmith, E. W., Lu, R., Yu, S.-C., Sorek, M., Silverman, B., Jordan, C. S., Sterling, A. R., Silversmith, W. M., Schlegel, P., Jefferis, G. S. X. E., Collman, F., Seung, H. S., & Tank, D. W. (2026). Hippocampal CA3 connectomics reveals a gradient of mossy fiber inputs and selective feedforward inhibition onto pyramidal cells. Nature neuroscience, 29(9), 2311-2323. https://doi.org/10.1038/s41593-026-02388-9

BibTeX

@article{zheng2026hippocampal,
author = {Zheng, Zhihao and Park, Changjoo and Hammerschmith, Eric W. and Lu, Ran and Yu, Szi-Chieh and Sorek, Marissa and Silverman, Ben and Jordan, Chris S. and Sterling, Amy R. and Silversmith, William M. and Schlegel, Philipp and Jefferis, Gregory S. X. E. and Collman, Forrest and Seung, H. Sebastian and Tank, David W.},
title = {{Hippocampal CA3 connectomics reveals a gradient of mossy fiber inputs and selective feedforward inhibition onto pyramidal cells}},
journal = {Nature neuroscience},
year = {2026},
month = aug,
volume = {29},
number = {9},
pages = {2311--2323},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02388-9},
url = {https://doi.org/10.1038/s41593-026-02388-9},
pmid = {42557444},
pmcid = {PMC13533834}
}

RIS

TY - JOUR
AU - Zheng, Zhihao
AU - Park, Changjoo
AU - Hammerschmith, Eric W.
AU - Lu, Ran
AU - Yu, Szi-Chieh
AU - Sorek, Marissa
AU - Silverman, Ben
AU - Jordan, Chris S.
AU - Sterling, Amy R.
AU - Silversmith, William M.
AU - Schlegel, Philipp
AU - Jefferis, Gregory S. X. E.
AU - Collman, Forrest
AU - Seung, H. Sebastian
AU - Tank, David W.
TI - Hippocampal CA3 connectomics reveals a gradient of mossy fiber inputs and selective feedforward inhibition onto pyramidal cells
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/08/05
VL - 29
IS - 9
SP - 2311
EP - 2323
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02388-9
UR - https://doi.org/10.1038/s41593-026-02388-9
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41593-026-02388-9",
"type": "article-journal",
"title": "Hippocampal CA3 connectomics reveals a gradient of mossy fiber inputs and selective feedforward inhibition onto pyramidal cells",
"container-title": "Nature neuroscience",
"author": [
{
"family": "Zheng",
"given": "Zhihao"
},
{
"family": "Park",
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{
"family": "Hammerschmith",
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},
{
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},
{
"family": "Yu",
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},
{
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},
{
"family": "Silverman",
"given": "Ben"
},
{
"family": "Jordan",
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},
{
"family": "Sterling",
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},
{
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},
{
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},
{
"family": "Jefferis",
"given": "Gregory S. X. E."
},
{
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"given": "Forrest"
},
{
"family": "Seung",
"given": "H. Sebastian"
},
{
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}
],
"container-title-short": "Nat Neurosci",
"volume": "29",
"issue": "9",
"page": "2311-2323",
"DOI": "10.1038/s41593-026-02388-9",
"PMID": "42557444",
"PMCID": "PMC13533834",
"ISSN": "1097-6256",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41593-026-02388-9",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
5
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]
}
}

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