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Perception and neural representation of intermittent odor stimuli in mice.

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 › Behavioral training ↔ Odor_pulses_data_guideline.ipynb, lines 32–108 · score 0.77 · Lick port location, Lick Training phase, water drops, Poisson distributions, day, Block
  2. [2] § Methods › Behavioral task ↔ Odor_pulses_data_guideline.ipynb, lines 32–108 · score 0.70 · Wait Period, lick port, water drop, Poisson distributed, Go, Odor Pulses
  3. [3] § Methods › Behavioral apparatus ↔ Behavioral_data_compiler_v2.ipynb, lines 373–425 · score 0.68 · methyl butyrate, clean air, Catch trials, psychometric, behavior, pulse
  4. [4] § Results › Odor pulse counting task ↔ Behavioral_data_compiler_v2.ipynb, lines 373–425 · score 0.63 · methyl butyrate, clean air, Catch trials, behavioral, pulses
  5. [5] § Results › Olfactory cortical responses largely reflect sensory information ↔ NComms_Submission/ephys_compiler.ipynb, lines 762–792 · score 0.53 · 9–12, firing rate, 5–8, 1–4, neurons, pulses

Paper

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

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The authors' code

Jupyter notebook · 287 lines · 11 KB · MIT · 2 matches

  1. # %%
  2. import pickle
  3. import pandas as pd
  4. import numpy as np
  5. import matplotlib as mpl
  6. from matplotlib import rcParams
  7. import matplotlib.pyplot as plt
  8. import h5py
  9. import os
  10. import tqdm
  11. import scipy
  12. from scipy import signal
  13. from scipy.signal import resample
  14. from tqdm import tnrange
  15. import seaborn as sns
  16. from scipy.stats import norm,entropy,linregress
  17. from scipy.optimize import minimize, curve_fit
  18. from scipy.io import savemat
  19. import multiprocess as mp
  20. from multiprocess import Pool
  21. import sys
  22. import warnings
  23. warnings.filterwarnings('ignore')
  24. sns.set_context('poster', font_scale=1.1)
  25. cmap = plt.rcParams['axes.prop_cycle'].by_key()['color']
  26. modulename = 'multiprocess'
  28. mpl.rcParams['svg.fonttype'] = 'none'
  29. sns.set_context('poster', font_scale=1.1)
  30. path = os.getcwd() + '/Session/'
  32. # %% [markdown]
  33. # Loading a session_animalname_date_sessionnum.pickle file gives a single-element list containing a dictionary
  34. # ============================================================================================================
  35. #
  36. # Information about trial outcomes
  37. # ---------------------------------------------------------------------------------------------------------------------------------------
  38. #
  39. # **num_trials**: number of trials recorded
  40. #
  41. # **high_trials**: boolean saying if the trial had a high number of pulses (above boundary) or not
  42. #
  43. # **low_trials**: the opposite of high_trials
  44. #
  45. # **high_choice**: boolean saying if animal chose the 'high side' or not
  46. #
  47. # **low_choice**: boolean saying if animal chose the 'low side' or not
  48. #
  49. # **correct_trials**: boolean saying if the animal got the trial correctly or not. (high_choice==True & high_trials==True and the opposite)
  50. #
  51. # **failure_trials**: Opposite of correct trials.
  52. #
  53. # **idle_trials**: if True, animal didn't make any binary choice in that trial
  54. #
  55. # **performance**: Correct trials/Total trials in the session
  56. #
  57. #
  58. # **Information about session parameters**
  59. # ---------------------------------------------------------------------------------------------------------------------------------------
  60. #
  61. # **type**: Whether high and low trials were delivered in blocks (usually true for training phase) or randomly (once animals learnt the task).
  62. #
  63. # **lick training**: When True, odors are not delivered. This is only used at the beggining of the training when animals are learning lick port location.
  64. #
  65. # **free_drop**: if True, water drop will delivered to the correct side independently of animal action. Only used at the beggining of training.
  66. #
  67. # **high_count**: lambda of the poisson distribution used for generating the trials with high number of pulses.
  68. #
  69. # **low_count**: lambda of the poisson distribution used for generating the trials with low number of pulses
  70. #
  71. # **pulse_time_ms**: duration of the individual odor pulses in ms
  72. #
  73. # **refract_time**: duration (in ms) of the waiting period before the sampling period of a trial
  74. #
  75. # **delay_time**: duration of the sampling period (period in which odor pulses are delivered)
  76. #
  77. # **go_time**: duration of the period in which the animal has to lick (in ms)
  78. #
  79. #
  80. # **Information about odor and sniffing**
  81. # ---------------------------------------------------------------------------------------------------------------------------------------
  82. #
  83. # **trial_odor**: array with len equal to delay_time containing the time course of the commands to solenoid valves for odor delivery. 0 = no odor, 100 = odor on. When two or more consecutive pulses overlaps, that is displayed as 100 multiples
  84. #
  85. # **trial_pre_breath**: an array containing the sniffing during the refractory period.
  86. #
  87. # **trial_breath**: an array containing the sniffing during delay_time(sampling period)+go_time.
  88. #
  89. #
  90. # **Other accesory information**
  91. # ---------------------------------------------------------------------------------------------------------------------------------------
  92. #
  93. # **animal**: ID of the animal
  94. #
  95. # **year**: year of the session
  96. #
  97. # **month**: month of the session
  98. #
  99. # **day**: day date of the session
  100. #
  101. # **session_id**: number identifying the she specific session for when multiple sessions are conducted on a day
  103. # %%
  104. session_fname = path + 'session_' + 'Banner_20220305_3.pickle' #remember to change the path to where your file is
  105. if os.path.isfile(session_fname):
  106. with open(session_fname,'rb') as handle:
  107. session_list = pickle.load(handle)
  108. session = session_list[0]
  110. # %%
  111. #Plotting correct and error trials on a session.
  113. plt.plot(session_list[0]['correct_trials'].cumsum())
  114. plt.plot(session_list[0]['failure_trials'].cumsum())
  116. # %%
  117. # Plotting breathing an odor in a single trial.
  119. fig, ax1 = plt.subplots()
  121. # Plot the first variable on the left y-axis
  122. ax1.plot(session_list[0]['trial_breath'][2], '-k', linewidth=1)
  123. ax1.set_ylabel('Sniffing', color='k')
  125. # Create a second y-axis sharing the same x-axis
  126. ax2 = ax1.twinx()
  128. # Plot the second variable on the right y-axis
  129. ax2.plot(session_list[0]['trial_odor'][2], 'r-')
  130. ax2.set_ylabel('Odor commands', color='r')
  132. plt.show()
  134. # plt.plot(session_list[0]['trial_breath'][2])
  135. # plt.plot(session_list[0]['trial_odor'][2])
  136. # plt.show()
  138. # %% [markdown]
  139. # Loading a cell_animalname_date_sessionnum.pickle file gives a list, with the length=recorded units. For each element of the list there is a dictionary
  140. # ============================================================================================================
  141. #
  142. # Information about the specific neuron
  143. # ---------------------------------------------------------------------------------------------------------------------------------------
  144. #
  145. # **cell_id**: id of the neuron/unit
  146. #
  147. # **ks_id**: id of the neuron based on kilosort
  148. #
  149. # **sample_rate**: sampling rate used for the acquisition of neural recordings.
  150. #
  151. # **spike_times**: timing of the spikes during the recording window. Events can happen in a window which goes from 0 (500 ms after sampling window started) to 135000 (the end of the sampling window). Remember that sampling rate is 30000 Hz
  152. #
  153. # **firing_rate**: not sure how this firing rate calculation was done, so I don't use it.
  154. #
  155. #
  156. #
  157. # **Information about session parameters**
  158. # ---------------------------------------------------------------------------------------------------------------------------------------
  159. #
  160. # **num_trials**: number of trials recorded in the session.
  161. #
  162. # **delay_time**: duration of the sampling period (period in which odor pulses are delivered)
  163. #
  164. # **cut_delay_time**: duration of the part of delay time that was discarded of the analysis (the initial 0.5 s of the sampling periods; spikes were not taken into account due to artifacts)
  165. #
  166. # **full_delay_time**: duration of the part of sampling period for which spikes are actually considered (4.5 s)
  167. #
  168. #
  169. # **Other accesory information**
  170. # ---------------------------------------------------------------------------------------------------------------------------------------
  171. #
  172. # **year**: year of the session
  173. #
  174. # **month**: month of the session
  175. #
  176. # **day**: day date of the session
  177. #
  178. # **session**: number identifying the she specific session for when multiple sessions are conducted on a day
  180. # %%
  181. new_path = os.getcwd() + '/HW_Data/pack_data_new/' #remember to change the path to where your file is
  182. cell_fname = new_path + 'cell_' + 'Bengal_20190430_0.pickle'
  183. if os.path.isfile(cell_fname):
  184. print('pepe')
  185. with open(cell_fname,'rb') as handle:
  186. cell_list = pickle.load(handle)
  187. cell = cell_list[0]
  189. # %%
  190. def convert_to_numpy_arrays(data):
  191. if isinstance(data, np.ndarray):
  192. return data
  193. elif isinstance(data, list):
  194. return np.array([convert_to_numpy_arrays(item) for item in data])
  195. else:
  196. return np.array([data])
  198. # %%
  199. # Generating raster plot of a single neuron across all trials on a session
  201. icell = 8
  202. cell = cell_list[icell]
  204. num_trials = cell['num_trials']
  205. #non_idle_trial_ids = np.argwhere(non_idle_trials).squeeze()
  206. sp_times = np.array(cell['spike_times'])[:num_trials]#[non_idle_trials]
  208. spike_times = convert_to_numpy_arrays(sp_times)
  209. max_counts = 0
  210. for i in range(len(spike_times)):
  211. max_counts = max(max_counts,len(spike_times[i]))
  212. #print(max_counts)
  214. spike_table =np.zeros((len(spike_times),max_counts))
  215. #rank_ind = np.argsort(trial_cum_odor)
  216. for i in range(len(spike_times)):
  217. spike_time = spike_times[i]
  218. spike_table[i,:len(spike_time)] = spike_time/30000 + 0.5
  221. sns.set_style('white')
  222. fig = plt.figure(figsize=(15,7.5))
  223. plt.eventplot(spike_table,color='black')
  224. plt.xlim(0.5,5)
  225. plt.ylim(0,242)
  226. plt.xlabel('Time during odor period(s)')
  227. plt.ylabel('Trial')
  228. plt.show()
  230. # %% [markdown]
  231. # Now, going to the codes and kernels data coming from the DUNL analysis of the ephys and behavior
  232. # ================================================================================================
  234. # %%
  235. codepath = os.getcwd() + '/Bahareh/'
  238. dat = np.load(codepath+'Bengal_20190508_0_data_all.npy', allow_pickle=True)
  239. data = dat.tolist() #Maybe this conversion to list is not necessary but I remember having some issues without it.
  240. print(data.keys())
  242. # %% [markdown]
  243. # Loading the data_all.npy file provides a single dictionary with a set of parameters that are repeated for each recorded neuron, indicated by the _0, 1, 2, etc., at the end of the variable.
  244. #
  245. # According to Bahareh, these variables represent:
  246. #
  247. # y_{#neuron}: spike counts from neuron #
  248. #
  249. # Hx_{#neuron}: you can skipa_{#neuron}: baseline
  250. #
  251. # rate_hat_{#neuron}: estimated rate for neuron
  252. #
  253. # x_est_{#neuron}: This is the code estimate from the trial. This is at the 50 ms binned resolution from each trial.
  254. #
  255. # kernel_est_{# neuron}: length of kernel which is 20 samples, with 50 ms res, this is 1 s
  256. #
  257. # x: this is the odor onset in the 50 ms bin resolution
  258. #
  259. # high_choice: a flag for whether the trial was high or low odor count.
  260. #
  261. # 'norm_phase_hist_{#neuron}': This a phase histogram weighted by the code amplitudes
  262. #
  263. # You can also get some of this information from the other files, the ones called _kernels.npy, codes_stacked_trial.npy, phase_weighted_by_codes.npy, but I found easier just to load the data_all, since it has it all.
  265. # %%
  268. # %%
  269. test = data['norm_phase_hist_0'] #This contains a vector of length 36, so the phase variations on code amplitude seem to be binned in 36 bins
  271. #This is the set of transformations that Hao used to do with the norm_phase_hist data coming from Bahareh's analysis.
  272. #Not completely sure if this is fully correct, but it's the way he was using
  274. print(test)
  275. X_upsampled = resample(test,360,axis=0)
  276. print(X_upsampled)
  277. X_rolled = np.roll(X_upsampled,50,axis=0)
  278. print(X_rolled)
  279. X_resampled = resample(X_rolled,14,axis=0)
  280. print(X_resampled)
  281. X_norm = X_resampled/X_resampled.max(axis=0).reshape(-1,1)
  282. print(X_norm)
  283. # X_norm_nan_removed = X_norm[~np.isnan(X_norm).any(axis=1)]
  284. # Xnorm.append(X_norm_nan_removed)
  286. # %%

Odor_pulses_data_guideline.ipynb at commit 5653ebc, under MIT · at the source

Overview

Authors: Luis E Boero1,2, Hao Wu1,2,3, Joseph D Zak4, Paul Masset5,6, Farhad Pashakhanloo1,2, Siddharth Jayakumar1,2, Bahareh Tolooshams7,8, Demba Ba2,9,10, Venkatesh N Murthy1,2,10
ORCID iDs: Luis E Boero, Hao Wu, Joseph D Zak, Paul Masset, Siddharth Jayakumar, Demba Ba, Venkatesh N Murthy
  1. Department of Molecular & Cellular Biology, Harvard University, Cambridge, MA USA
  2. Center for Brain Science, Harvard University, Cambridge, MA USA
  3. Department of Chemistry & Chemical Biology, Harvard University, Cambridge, MA USA
  4. Department of Biological Sciences, University of Illinois, Chicago, IL USA
  5. Department of Psychology, McGill University, Montreal, QC Canada
  6. Mila - Quebec Artificial Intelligence Institute, Montreal, Canada
  7. Alberta Machine Intelligence Institute (Amii), Edmonton, AB Canada
  8. Neuroscience and Mental Health Institute (NMHI), University of Alberta, Edmonton, AB Canada
  9. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA USA
  10. Kempner Institute for the Study of Natural & Artificial Intelligence, Harvard University, Cambridge, MA USA
Institutions: Harvard University (United States); University of Illinois Chicago (United States); Mila - Quebec Artificial Intelligence Institute (Canada); McGill University (Canada); University of Alberta (Canada); Alberta Machine Intelligence Institute (Canada); Neuroscience and Mental Health Institute (Canada)
Journal: Nature communications, volume 17, issue 1, article 5575
Dates: received 17 March 2025; accepted 15 April 2026; published online 29 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-72445-1 · PMID 42056108 · PMCID PMC13304163 · OpenAlex W4407448503
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.02.12.637969
Status: code verified
Categories: mouse (organism)
Methods: Machine learning, Statistics, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Neuroscience, Olfactory system
MeSH: Odorants*, Olfactory Perception*, Olfactory Receptor Neurons*, Piriform Cortex*, Smell*, Animals, Behavior, Animal, Cues, Male, Mice, Mice, Inbred C57BL, Neurons, Respiration (* major topic)
Topic: Olfactory and Sensory Function Studies (Sensory Systems, Neuroscience), according to OpenAlex
Funding: Harvard University; Ministry of Defence; Pew Charitable Trusts; U. S. Department of Defense; NIDCD NIH HHS (R00 DC017754); RCUK | Engineering and Physical Sciences Research Council (EPSRC) (W911NF-16-1-0368)
Citations: cited by 3 papers (Europe PMC); 88 references in the paper

Abstract

Odor cues in nature are sparse and fluctuating due to turbulent transport. To investigate how animals perceive these intermittent cues, we developed a behavioral task in which mice made binary decisions based on the total number of discrete odor pulses presented stochastically over several seconds. Mice quickly learned this task, placing higher perceptual weight to stimuli arriving during inhalation than exhalation, a phase dependency that strongly correlated with the magnitude of responses in olfactory sensory neurons. Neurons in the anterior piriform cortex responded to odor pulses with varying degrees of dependence on respiration phase. Single cortical neurons responded stochastically and transiently to odor pulses, leading to a representation that carries signatures of sensory evidence, but not its accumulation. Our study reveals that mice can integrate intermittent odor signals across dozens of breaths and that respiratory modulation imposes limits on sensory information acquisition that cortical circuits cannot overcome to improve behavior.

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.

leboero/odor_pulses_task_analysis

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 5653ebc491961e6ca196b39c18b086ffedf932ac, 17 March 2026
Languages: Jupyter (32), Python (6)
Size: 53 files, 38 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 32 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (17 files), NumPy (17 files), SciPy (17 files), seaborn (17 files), h5py (15 files), pandas (14 files), scikit-learn (7 files), Pingouin (5 files), statsmodels (4 files), PyTorch (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
18 files
At the source: github.com/leboero/Odor_Pulses_Task_Analysis/tree/main/NComms_Submission

Code availability

Code for the analysis of the data is provided at: https://github.com/leboero/Odor_Pulses_Task_Analysis/tree/main/NComms_Submission.

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

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  • 17 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 Statement

The data generated in this study have been deposited in Figshare under accession code 10.6084/m9.figshare.31792705. Additionally, source data are provided with this paper in the Source Data File. Source data are provided with this paper.

Code for the analysis of the data is provided at: https://github.com/leboero/Odor_Pulses_Task_Analysis/tree/main/NComms_Submission.

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 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 2 keywords, 13 MeSH terms, 6 funders, 80 references.

Cite

This paper

Boero, L. E., Wu, H., Zak, J. D., Masset, P., Pashakhanloo, F., Jayakumar, S., Tolooshams, B., Ba, D., & Murthy, V. N. (2026). Perception and neural representation of intermittent odor stimuli in mice. Nature communications, 17(1), 5575. https://doi.org/10.1038/s41467-026-72445-1

BibTeX

@article{boero2026perception,
author = {Boero, Luis E and Wu, Hao and Zak, Joseph D and Masset, Paul and Pashakhanloo, Farhad and Jayakumar, Siddharth and Tolooshams, Bahareh and Ba, Demba and Murthy, Venkatesh N},
title = {{Perception and neural representation of intermittent odor stimuli in mice}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5575},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72445-1},
url = {https://doi.org/10.1038/s41467-026-72445-1},
pmid = {42056108},
pmcid = {PMC13304163}
}

RIS

TY - JOUR
AU - Boero, Luis E
AU - Wu, Hao
AU - Zak, Joseph D
AU - Masset, Paul
AU - Pashakhanloo, Farhad
AU - Jayakumar, Siddharth
AU - Tolooshams, Bahareh
AU - Ba, Demba
AU - Murthy, Venkatesh N
TI - Perception and neural representation of intermittent odor stimuli in mice
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/29
VL - 17
IS - 1
SP - 5575
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72445-1
UR - https://doi.org/10.1038/s41467-026-72445-1
LA - en
ER -

CSL-JSON

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