OSCR

Modeling synaptic interactions between mammalian breathing and swallowing central pattern generators.

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] § Methods › Experimental data analysis ↔ code/rCPGswCPG/prc_extraction/prc_extraction_subroutines/prc_extraction_linear_fit.py, lines 16–31 · score 0.73 · linear fit, Hilbert transform, phase shift, minimize, PRC, stimulus
  2. [2] § Results › Phase space analysis of the Sw-CPG: transient dynamics of the swallowing half-center oscillator in response to a short SLN stimulation ↔ code/rCPGswCPG/plotting_figures/Fig6_Solitary_swallow_PIR/PIR_solitary_swallow_phase_space_analysis.py, lines 61–73 · score 0.66 · fast subsystem, equilibrium surface, phase space, swallowing
  3. [3] § Methods › Experimental data analysis ↔ code/rCPGswCPG/prc_extraction/run_prc_extraction.py, lines 19–73 · score 0.65 · Hilbert transform, phase shift, protophase, PRC, filtered, cycle
  4. [4] § Results › Phase space analysis of the Sw-CPG: transient dynamics of the swallowing half-center oscillator in response to a short SLN stimulation ↔ code/rCPGswCPG/plotting_figures/Fig6_Solitary_swallow_PIR/PIR_solitary_swallow_phase_space_analysis.py, lines 61–73 · score 0.61 · fast subsystem, equilibrium surface, phase space, Figure 6, swallowing
  5. [5] § Methods › Experimental data analysis ↔ code/rCPGswCPG/utils/sp_utils.py, lines 26–35 · score 0.58 · signal.filtfilt, bandpass filtered, butter

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 210 lines · 8.9 KB · MIT · 2 matches

  1. import pickle
  2. import os
  3. import numpy as np
  4. from scipy.optimize import fsolve
  5. from tqdm.auto import tqdm
  6. import numdifftools as nd
  7. import warnings
  8. from rCPGswCPG.Network import firing_rate
  9. from rCPGswCPG.Network import construct_model
  10. from rCPGswCPG.model_params.config_loader import load_model_cfg_file, model_params_from_cfg
  11. from rCPGswCPG.utils.gen_utils import get_project_root
  12. warnings.filterwarnings("ignore")
  13. from matplotlib import pyplot as plt
  14. from matplotlib.ticker import FuncFormatter
  15. import matplotlib as mpl
  16. mpl.use('MacOSX') # on macOS built-in backend
  17. import matplotlib.tri as mtri
  18. # mpl.use('QtAgg') # if you have PyQt5/PySide6 installed
  20. # find a solution of a system of nonlinear equations
  21. def find_solutions(dim, equations, args, bounds, num_iter):
  22. sols = []
  23. for i in range(num_iter):
  24. init_guess = np.array([bounds[j, 0] + (bounds[j, 1] - bounds[j, 0]) * np.random.rand() for j in range(dim)])
  25. roots = fsolve(equations, init_guess, args=args)
  26. if np.allclose(equations(roots, *args), np.zeros(dim)):
  27. roots = np.round(roots, 4)
  28. if not np.array_str(roots) in [np.array_str(sol) for sol in sols]:
  29. sols.append(roots)
  30. return sols
  32. def rhs(vs, ms, ws, drives, inps):
  33. # NOTE: this fixed-point computation still hardcodes the OLD parametrization
  34. # (scale=1/tau_v_old=200, alpha=0.01, beta=0.3). It is internally consistent
  35. # in the old v~O(40) coordinates, but mismatched with the rescaled model
  36. # trajectory below; reworking to the new coordinates is a separate task.
  37. scale = 200
  38. alpha = 0.01
  39. bias = -0.2
  40. fr = firing_rate(vs, 0.3)
  41. rhs_v = scale * (-alpha * vs - ms + (drives + bias) + ws @ fr + inps)
  42. return rhs_v
  44. def determine_stability(point, rhs_eq, ms, ws, drives, inps):
  45. # linearize equations first
  46. A = (nd.Jacobian(rhs_eq)(point, ms, ws, drives, inps))
  47. eigenvals = np.round(np.linalg.eig(A)[0], 4)
  48. label = 'unstable'
  49. if np.all(np.real(eigenvals) < 0):
  50. label = 'stable'
  51. return label, eigenvals
  53. def find_fixed_points(ms, drives, ws, inps, bounds):
  54. fixed_points = find_solutions(dim=2, equations=rhs, args=(ms, ws, drives, inps), bounds=bounds, num_iter=100)
  55. data = np.empty((0, 5), dtype=object)
  56. for fp in fixed_points:
  57. label, eigenvals = determine_stability(fp, rhs, ms, ws, drives, inps)
  58. data = np.append(data, np.array([[*ms, *fp, label]]), axis = 0)
  59. return data
  61. def calculate_equilibrium_surface(drives, ws, inps, bounds):
  62. m1s = np.linspace(0.0, 0.04, 40)
  63. m2s = np.linspace(0.17, 0.23, 40)
  65. # for every point find solutions of the fast subsystem:
  66. data_surf = np.empty((0, 5), dtype=object)
  67. for i in tqdm(range(len(m1s))):
  68. for j in range(len(m2s)):
  69. ms = np.array([m1s[i], m2s[j]])
  70. # calculate fixed points of the fast subsystem:
  71. data = find_fixed_points(ms, drives=drives, ws=ws, inps=inps, bounds=bounds)
  72. data_surf = np.append(data_surf, data, axis = 0)
  73. return data_surf
  75. def plot_sheet_trisurf(x, y, z, ax=None, color=None, alpha=0.12, len_pct=95, dz_pct=95, lw=0.2):
  76. if ax is None:
  77. fig, ax = plt.subplots(subplot_kw={'projection': '3d'}, figsize=(7, 5))
  78. tri = mtri.Triangulation(x, y)
  79. T = tri.triangles
  80. P3 = np.c_[x, y, z]
  81. e0 = np.linalg.norm(P3[T[:, 0]] - P3[T[:, 1]], axis=1)
  82. e1 = np.linalg.norm(P3[T[:, 1]] - P3[T[:, 2]], axis=1)
  83. e2 = np.linalg.norm(P3[T[:, 2]] - P3[T[:, 0]], axis=1)
  84. Lmax = np.maximum(e0, np.maximum(e1, e2))
  85. dz0 = np.abs(z[T[:, 0]] - z[T[:, 1]])
  86. dz1 = np.abs(z[T[:, 1]] - z[T[:, 2]])
  87. dz2 = np.abs(z[T[:, 2]] - z[T[:, 0]])
  88. DZmax = np.maximum(dz0, np.maximum(dz1, dz2))
  89. tri.set_mask((Lmax > np.percentile(Lmax, len_pct)) | (DZmax > np.percentile(DZmax, dz_pct)))
  90. ax.plot_trisurf(tri, z, linewidth=lw, antialiased=True, alpha=alpha, color=color)
  91. for a in (ax.xaxis, ax.yaxis, ax.zaxis):
  92. a.pane.set_facecolor((1, 1, 1, 0))
  93. a.pane.set_edgecolor((1, 1, 1, 0))
  94. ax.grid(False)
  95. ax.set(xlabel=r'$m_1$', ylabel=r'$m_2$', zlabel=r'$v_1$')
  96. return ax
  98. if __name__ == '__main__':
  99. #plotting PPA
  100. recalculate = False
  101. img_folder = os.path.join(get_project_root(), "img")
  102. data_folder = os.path.join(get_project_root(), "data")
  104. model_name = "swHCO"
  105. model_params = model_params_from_cfg(load_model_cfg_file(model_name.replace("model_", "")))
  106. hco = construct_model(model_params)
  107. external_inputs = np.zeros(model_params["N"])
  108. pnames = model_params["pnames"]
  109. weights = np.array([model_params["W"][0, 1], model_params["W"][1, 0]])
  110. drives = np.array([model_params["drives_misc"][0, 0], model_params["drives_misc"][0, 1]])
  111. inputs = np.array([0.12, 0.07])
  112. # inputs = np.array([0.19, 0.07])
  113. dt = 0.01
  114. hco.dt = dt
  115. T_before_stim = 0.5
  116. T_stim = 0.15
  117. T_after_stim = 2
  119. hco.run(T_before_stim, np.zeros(2))
  120. hco.run(T_stim, inputs)
  121. hco.run(T_after_stim, np.zeros(2))
  122. recordings = hco.get_recordings()
  124. v1_traj = recordings["v_history"][:, 0]
  125. v2_traj = recordings["v_history"][:, 1]
  126. m1_traj = recordings["m_history"][:, 0]
  127. m2_traj = recordings["m_history"][:, 1]
  128. print(np.min(v1_traj), np.max(v1_traj))
  129. print(np.min(v2_traj), np.max(v2_traj))
  130. print(np.min(m1_traj), np.max(m1_traj))
  131. print(np.min(m2_traj), np.max(m2_traj))
  133. lim = np.array([-40, 20])
  134. inps = np.zeros(2)
  135. bounds = np.array([[-40, 20], [-40, 20]])
  136. tag = (np.abs(weights[0]), np.abs(weights[1]), drives[0], drives[1])
  137. file_name = os.path.join(data_folder, f"eq_surface_simplified_HCO_{tag}.pkl")
  138. if not os.path.exists(file_name) or recalculate:
  139. data_surf = calculate_equilibrium_surface(drives, hco.W, inps, bounds)
  140. pickle.dump(data_surf, open(file_name, 'wb+'))
  141. data_surf = pickle.load(open(file_name, 'rb+'))
  143. m1s = data_surf[:, 0].astype(float)
  144. m2s = data_surf[:, 1].astype(float)
  145. v1s = data_surf[:, 2].astype(float)
  146. v2s = data_surf[:, 3].astype(float)
  147. labels = data_surf[:, 4].astype(str)
  149. m1s_stable = m1s[labels == 'stable']
  150. m2s_stable = m2s[labels == 'stable']
  151. v1s_stable = v1s[labels == 'stable']
  152. v2s_stable = v2s[labels == 'stable']
  153. m1s_unstable = m1s[labels == 'unstable']
  154. m2s_unstable = m2s[labels == 'unstable']
  155. v1s_unstable = v1s[labels == 'unstable']
  156. v2s_unstable = v2s[labels == 'unstable']
  159. def split_upper_lower(z_stable, z_unstable):
  160. thr = float(np.mean(z_unstable))
  161. upper = z_stable >= thr
  162. lower = ~upper
  163. return upper, lower, thr
  165. upper, lower, thr = split_upper_lower(v1s_stable, v1s_unstable)
  166. m1_up, m2_up, v1_up = m1s_stable[upper], m2s_stable[upper], v1s_stable[upper]
  167. m1_lo, m2_lo, v1_lo = m1s_stable[lower], m2s_stable[lower], v1s_stable[lower]
  169. fig, ax = plt.subplots(subplot_kw={'projection': '3d'}, figsize=(7, 5))
  170. ax.scatter(m1s_stable, m2s_stable, v1s_stable, c='blue', marker='o', alpha = 0.1)
  171. ax.scatter(m1s_unstable, m2s_unstable, v1s_unstable, c='deepskyblue', marker='o', alpha = 0.1)
  172. # ax = plot_sheet_trisurf(m1_up, m2_up, v1_up, alpha=0.20, color='blue', ax=ax)
  173. # ax = plot_sheet_trisurf(m1s_unstable, m2s_unstable, v1s_unstable, alpha=0.30, color='deepskyblue', ax=ax)
  174. # ax = plot_sheet_trisurf(m1_lo, m2_lo, v1_lo, alpha=0.20, color='blue', ax=ax)
  176. start_stim = int(T_before_stim * 1000 / dt)
  177. stop_stim = int((T_stim) * 1000 / dt) + start_stim
  178. ax.scatter(m1_traj[start_stim+1], m2_traj[start_stim+1], v1_traj[start_stim+1], color='r', s=20)
  179. ax.scatter(m1_traj[stop_stim+1], m2_traj[stop_stim+1], v1_traj[stop_stim+1], color='g', s=20)
  180. ax.scatter(m1_traj[-1], m2_traj[-1], v1_traj[-1], color='b', s=20)
  182. ax.plot3D(m1_traj[start_stim:stop_stim], m2_traj[start_stim:stop_stim], v1_traj[start_stim:stop_stim], color='red', alpha=0.5)
  183. ax.plot3D(m1_traj[stop_stim:], m2_traj[stop_stim:], v1_traj[stop_stim:], color='k', alpha=0.5)
  185. ax.set_zlim(-40, 15)
  187. # three ticks per axis
  188. xt = np.linspace(*ax.get_xbound(), 3)
  189. yt = np.linspace(*ax.get_ybound(), 3)
  190. zt = np.linspace(*ax.get_zbound(), 3)
  192. ax.set_xticks(xt);
  193. ax.set_yticks(yt);
  194. ax.set_zticks(zt)
  196. # formatters: x/y with two decimals, z as integers
  197. ax.xaxis.set_major_formatter(FuncFormatter(lambda v, p: f"{v:.2f}"))
  198. ax.yaxis.set_major_formatter(FuncFormatter(lambda v, p: f"{v:.2f}"))
  199. ax.zaxis.set_major_formatter(FuncFormatter(lambda v, p: f"{int(round(v))}"))
  201. for a in (ax.xaxis, ax.yaxis, ax.zaxis):
  202. a.pane.set_facecolor((1, 1, 1, 0))
  203. a.pane.set_edgecolor((1, 1, 1, 0))
  204. ax.grid(False)
  205. ax.set(xlabel=r'$m_1$', ylabel=r'$m_2$', zlabel=r'$v_1$')
  206. ax.view_init(elev=20, azim=-127)
  207. tag = (np.abs(weights[0]), np.abs(weights[1]), drives[0], drives[1], inputs[0], inputs[1])
  208. plt.savefig(os.path.join(img_folder, f'3Dppa_{tag}.pdf'), bbox_inches='tight', transparent=True)
  209. plt.savefig(os.path.join(img_folder, f'3Dppa_{tag}.png'), bbox_inches='tight', transparent=True, dpi=500)
  210. plt.show()

PIR_solitary_swallow_phase_space_analysis.py at commit a6c7742, under MIT · at the source

Overview

Authors: Pavel Tolmachev1,2, Rishi R Dhingra3, Jonathan H Manton1, Mathias Dutschmann3
  1. Department of Electrical and Electronic Engineering, University of Melbourne, Grattan street 100, Melbourne, VIC 3010 Australia
  2. Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540 USA
  3. Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106 USA
Institutions: The University of Melbourne (Australia); Princeton University (United States); Case Western Reserve University (United States)
Journal: Biological cybernetics, volume 120, issue 5-6, article 36
Dates: received 20 February 2026; accepted 24 August 2026; published online 18 September 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s00422-026-01061-5 · PMID 42758335 · PMCID PMC13588932 · OpenAlex W7131071476
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), none (in silico) (organism)
Methods: Spectral & time-frequency, Connectivity, Preprocessing, Statistics, Single-unit activity, calcium imaging, Machine learning
Keywords: Postinspiration, Dorsal swallowing group, Pontine respiratory group, Pre-Bötzinger complex, Digestion, Brainstem, Computational model
MeSH: Brain Stem*, Central Pattern Generators*, Deglutition*, Models, Neurological*, Respiration*, Synapses*, Animals, Computer Simulation, Laryngeal Nerves (* major topic)
Topic: Neuroscience of respiration and sleep (Endocrine and Autonomic Systems, Neuroscience), according to OpenAlex
Funding: National Health and Medical Research Council of Australia (APP1165529)
Citations: not cited yet (Europe PMC); 122 references in the paper

Abstract

Breathing and swallowing are tightly coupled motor behaviors whose execution depends on the coordinated activity of two brainstem central pattern generators (R-CPG, Sw-CPG). We present a mechanistic neural network model that captures this coordination. The model was built by incrementally expanding the synaptic connectivity of R-CPG and Sw-CPG modules, each composed of neuronal population nodes whose dynamics incorporate firing rate adaptation. The rhythmogenic cores of both CPGs are implemented as half-center oscillators operating independently. In agreement with accompanying experimental recordings from an in situ perfused brainstem preparation, a simulated 10 s sensory drive to the superior laryngeal nerve (SLN) reliably elicits fictive sequential swallowing bursts accompanied by glottal closure and suppression of inspiratory activity. Short SLN stimuli (100 ms) produce isolated single swallows that reset the phase of the respiratory oscillator. Phase space analysis of the model dynamics offers additional insight into this SLN-evoked respiratory phase resetting. Consistent with prior experimental observations, simulated pontine inhibition reproduces apneusis and abolish swallowing-related glottal closure during sequential swallowing. Systematic perturbation of synaptic weights across specific network pathways identifies vulnerable connections whose weakening recapitulates clinically relevant breathing–swallowing disorders, including aspiration. The model thereby generates mechanistic predictions that may inform the design of therapeutic interventions for conditions characterized by impaired breathing–swallowing coordination.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

ptolmachev/SimplifiedModel_rCPG_swCPG_interaction

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: a6c77427e6f0466ce91650a5e99c8c287e6d9674, 23 June 2026
Languages: Python (43)
Size: 78 files, 43 scripts
Software Heritage: not archived
Found in: “Code Availability”
Holds: README, license file, environment (code/setup.py), documentation
Not found: CITATION.cff, tests, continuous integration
Tools: NumPy (32 files), Matplotlib (23 files), SciPy (11 files), SymPy (3 files), scikit-learn (2 files), pandas (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
45 files
At the source: github.com/ptolmachev/SimplifiedModel_rCPG_swCPG_interaction

Code Availability

The code for modeling, PRC extraction, and experimental data processing is available on GitHub at https://github.com/ptolmachev/SimplifiedModel_rCPG_swCPG_interaction.

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 43 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

The experimental recordings of phrenic and vagal nerve activity during short and long SLN stimulation are publicly available on Zenodo at: https://doi.org/10.5281/zenodo.18750367

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 3, 28 September 2026

  • Publisher: n/a → Springer Science+Business Media

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 7 keywords, 9 MeSH terms, 1 funder, 117 references.

Cite

This paper

Tolmachev, P., Dhingra, R. R., Manton, J. H., & Dutschmann, M. (2026). Modeling synaptic interactions between mammalian breathing and swallowing central pattern generators. Biological cybernetics, 120(5-6), 36. https://doi.org/10.1007/s00422-026-01061-5

BibTeX

@article{tolmachev2026modeling,
author = {Tolmachev, Pavel and Dhingra, Rishi R and Manton, Jonathan H and Dutschmann, Mathias},
title = {{Modeling synaptic interactions between mammalian breathing and swallowing central pattern generators}},
journal = {Biological cybernetics},
year = {2026},
month = sep,
volume = {120},
number = {5-6},
pages = {36},
publisher = {Springer Science+Business Media},
issn = {0340-1200},
doi = {10.1007/s00422-026-01061-5},
url = {https://doi.org/10.1007/s00422-026-01061-5},
pmid = {42758335},
pmcid = {PMC13588932}
}

RIS

TY - JOUR
AU - Tolmachev, Pavel
AU - Dhingra, Rishi R
AU - Manton, Jonathan H
AU - Dutschmann, Mathias
TI - Modeling synaptic interactions between mammalian breathing and swallowing central pattern generators
T2 - Biological cybernetics
J2 - Biol Cybern
PY - 2026
DA - 2026/09/18
VL - 120
IS - 5-6
SP - 36
SN - 0340-1200
PB - Springer Science+Business Media
DO - 10.1007/s00422-026-01061-5
UR - https://doi.org/10.1007/s00422-026-01061-5
LA - en
ER -

CSL-JSON

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"id": "10.1007/s00422-026-01061-5",
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"author": [
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"family": "Tolmachev",
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"container-title-short": "Biol Cybern",
"volume": "120",
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"page": "36",
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"PMID": "42758335",
"PMCID": "PMC13588932",
"ISSN": "0340-1200",
"publisher": "Springer Science+Business Media",
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"date-parts": [
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2026,
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