OSCR

Distinct sources of decision-related signals in visual cortex are represented in different local field potential bands.

Code ↔ Paper

8 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 8 matches
  1. [1] § Results › High-gamma CP is consistent with a feedforward influence ↔ Code/figure_generation/plot_fig3_cp_timecourses.py, lines 37–74 · score 0.80 · 30–70 Hz, 5–30 Hz, 70–150 Hz, Alpha beta, Low gamma, high gamma
  2. [2] § Materials and methods › Analyses of neural and behavioral responses › LFP-based choice probabilities (CP). ↔ Code/figure_generation/plot_fig3_cp_timecourses.py, lines 37–74 · score 0.79 · 30–70 Hz, 5–30 Hz, 70–150 Hz, alpha beta, low gamma, high gamma
  3. [3] § Materials and methods › Analyses of neural and behavioral responses › LFP-based choice probabilities (CP). ↔ Code/analysis_pipeline/FigS4_simulation/plot_cp_bands_epochs.R, lines 1–38 · score 0.70 · 30–70 Hz, 70–150 Hz, low gamma, high gamma, frequency bands, delay
  4. [4] § Results › CP as a function of LFP frequency and behavioral epoch ↔ Code/analysis_pipeline/FigS4_simulation/plot_cp_bands_epochs.R, lines 1–38 · score 0.65 · 30–70 Hz, 70–150 Hz, low gamma, high gamma, frequency bands, delay
  5. [5] § Materials and methods › Analyses of neural and behavioral responses › LFP-based choice probabilities (CP). ↔ Code/analysis_pipeline/FigS4_simulation/sim_neurodsp.py, lines 27–28 · score 0.63 · sim_combined, aperiodic components, NeuroDSP, simulate, signals
  6. [6] § Materials and methods › Analyses of neural and behavioral responses › LFP preprocessing and re-referencing. ↔ Code/analysis_pipeline/FigS4_simulation/sim_neurodsp.py, lines 33–35 · score 0.57 · power spectral density, pre
  7. [7] § Results › Spectral weighting on decomposition of LFP population signals ↔ Code/figure_generation/plot_fig4_frequency_specific_decomposition.py, lines 1–35 · score 0.51 · alpha beta, low gamma, high gamma, energy, dimension, axis
  8. [8] § Results › Alpha-beta CP and reward history ↔ Code/figure_generation/plot_fig4_frequency_specific_decomposition.py, lines 1–35 · score 0.50 · reward history, alpha beta, activity, Figure 4, monkeys, CP

Paper

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The authors' code

Python · 343 lines · 12 KB · MIT · 2 matches

  1. #!/usr/bin/env python3
  2. """
  3. Figure 3: frequency- and epoch-specific LFP choice probability.
  5. This script regenerates Fig. 3 from two canonical source-data files in
  6. Source_Data/:
  8. Fig3_main_timecourse_values.csv
  9. Fig3_main_epoch_summary_values.csv
  11. The timecourse file contains the MT and V4 traces used for the left and middle
  12. columns. The epoch-summary file contains the epoch-level aggregation values used
  13. for the right column and the manuscript-reported epoch summaries.
  14. """
  16. from __future__ import annotations
  18. from pathlib import Path
  19. from typing import Tuple
  21. import numpy as np
  22. import pandas as pd
  23. import matplotlib.pyplot as plt
  25. # ---------------------------------------------------------------------
  26. # Paths
  27. # ---------------------------------------------------------------------
  28. SCRIPT_DIR = Path(__file__).resolve().parent
  29. REPO_ROOT = SCRIPT_DIR.parents[1]
  30. SOURCE_DIR = REPO_ROOT / "Source_Data"
  31. OUT_DIR = REPO_ROOT / "Output" / "generated_figures"
  32. OUT_DIR.mkdir(parents=True, exist_ok=True)
  34. TIMECOURSE_FILE = SOURCE_DIR / "Fig3_main_timecourse_values.csv"
  35. EPOCH_SUMMARY_FILE = SOURCE_DIR / "Fig3_main_epoch_summary_values.csv"
  37. # ---------------------------------------------------------------------
  38. # Figure settings
  39. # ---------------------------------------------------------------------
  40. PRE_COLOR = "black"
  41. POST_COLOR = "#0072B2"
  42. STIM_SHADE = "#F7C6D0"
  43. BASELINE_LINE = "#808080"
  45. BANDS = [
  46. {
  47. "band_key": "high_gamma",
  48. "panel": "A",
  49. "label": "High gamma\n(70–150 Hz)",
  50. "sig": "***",
  51. "area_ylim": {"MT": (0.48, 0.52), "V4": (0.47, 0.57)},
  52. "agg_ylim": (0.488, 0.527),
  53. },
  54. {
  55. "band_key": "low_gamma",
  56. "panel": "B",
  57. "label": "Low gamma\n(30–70 Hz)",
  58. "sig": "n.s.",
  59. "area_ylim": {"MT": (0.47, 0.545), "V4": (0.44, 0.57)},
  60. "agg_ylim": (0.487, 0.535),
  61. },
  62. {
  63. "band_key": "alpha_beta",
  64. "panel": "C",
  65. "label": "Alpha–beta\n(5–30 Hz)",
  66. "sig": "n.s.",
  67. "area_ylim": {"MT": (0.47, 0.54), "V4": (0.43, 0.59)},
  68. "agg_ylim": (0.472, 0.526),
  69. },
  70. ]
  72. AREA_TITLES = {"MT": "Area MT", "V4": "Area V4"}
  73. EPOCH_ORDER = ["baseline", "stimulus", "delay"]
  74. EPOCH_LABELS = ["baseline", "stimulus\nresponse", "delay"]
  76. # ---------------------------------------------------------------------
  77. # Helpers
  78. # ---------------------------------------------------------------------
  79. def require_file(path: Path) -> Path:
  80. if not path.exists():
  81. required = [
  82. "Fig3_main_timecourse_values.csv",
  83. "Fig3_main_epoch_summary_values.csv",
  84. ]
  85. msg = [
  86. f"Required source-data file not found: {path}",
  87. "",
  88. "This Figure 3 script uses the canonical manuscript-style source-data files.",
  89. f"Please place the following files in: {SOURCE_DIR}",
  90. ]
  91. msg += [f" - {name}" for name in required]
  92. raise FileNotFoundError("\n".join(msg))
  93. return path
  96. def load_timecourse_source() -> pd.DataFrame:
  97. path = require_file(TIMECOURSE_FILE)
  98. df = pd.read_csv(path)
  99. required_cols = {
  100. "frequency_band", "area", "time_ms", "condition", "mean_cp", "sem_cp"
  101. }
  102. missing = required_cols.difference(df.columns)
  103. if missing:
  104. raise ValueError(f"{path.name} is missing columns: {sorted(missing)}")
  105. return df.copy()
  108. def load_epoch_summary_source() -> pd.DataFrame:
  109. path = require_file(EPOCH_SUMMARY_FILE)
  110. df = pd.read_csv(path)
  111. required_cols = {
  112. "frequency_band", "aggregation_level", "epoch", "condition", "mean_cp", "sem_cp"
  113. }
  114. missing = required_cols.difference(df.columns)
  115. if missing:
  116. raise ValueError(f"{path.name} is missing columns: {sorted(missing)}")
  117. return df.copy()
  120. def load_area(trace_df: pd.DataFrame, band_key: str, area: str) -> pd.DataFrame:
  121. sub = trace_df[
  122. (trace_df["frequency_band"] == band_key) &
  123. (trace_df["area"] == area)
  124. ].copy()
  125. if sub.empty:
  126. raise ValueError(f"No timecourse rows found for frequency_band={band_key}, area={area}")
  128. wide = sub.pivot_table(
  129. index="time_ms",
  130. columns="condition",
  131. values=["mean_cp", "sem_cp"],
  132. aggfunc="first",
  133. )
  134. required_pairs = [("mean_cp", "pre"), ("sem_cp", "pre"), ("mean_cp", "post"), ("sem_cp", "post")]
  135. for pair in required_pairs:
  136. if pair not in wide.columns:
  137. raise ValueError(f"Missing condition column {pair} for frequency_band={band_key}, area={area}")
  139. out = pd.DataFrame({
  140. "time_ms": wide.index.astype(float),
  141. "CP_pre": wide[("mean_cp", "pre")].to_numpy(dtype=float),
  142. "CP_pre_sem": wide[("sem_cp", "pre")].to_numpy(dtype=float),
  143. "CP_post": wide[("mean_cp", "post")].to_numpy(dtype=float),
  144. "CP_post_sem": wide[("sem_cp", "post")].to_numpy(dtype=float),
  145. }).sort_values("time_ms").reset_index(drop=True)
  147. expected_n = 31
  148. if len(out) != expected_n:
  149. raise ValueError(
  150. f"Expected {expected_n} time points for frequency_band={band_key}, area={area}; found {len(out)}"
  151. )
  152. return out
  155. def load_aggregation(summary_df: pd.DataFrame, band_key: str) -> pd.DataFrame:
  156. sub = summary_df[
  157. (summary_df["frequency_band"] == band_key) &
  158. (summary_df["aggregation_level"] == "All")
  159. ].copy()
  160. if sub.empty:
  161. raise ValueError(f"No aggregation rows found for frequency_band={band_key}")
  163. wide = sub.pivot_table(
  164. index="epoch",
  165. columns="condition",
  166. values=["mean_cp", "sem_cp"],
  167. aggfunc="first",
  168. )
  169. rows = []
  170. for epoch in EPOCH_ORDER:
  171. if epoch not in wide.index:
  172. raise ValueError(f"Missing epoch={epoch} for frequency_band={band_key}")
  173. required_pairs = [("mean_cp", "pre"), ("sem_cp", "pre"), ("mean_cp", "post"), ("sem_cp", "post")]
  174. for pair in required_pairs:
  175. if pair not in wide.columns:
  176. raise ValueError(f"Missing condition column {pair} for frequency_band={band_key}, epoch={epoch}")
  177. rows.append({
  178. "epoch": epoch,
  179. "CP_pre": float(wide.loc[epoch, ("mean_cp", "pre")]),
  180. "CP_pre_sem": float(wide.loc[epoch, ("sem_cp", "pre")]),
  181. "CP_post": float(wide.loc[epoch, ("mean_cp", "post")]),
  182. "CP_post_sem": float(wide.loc[epoch, ("sem_cp", "post")]),
  183. })
  184. return pd.DataFrame(rows)
  187. def add_timecourse_panel(ax, df: pd.DataFrame, ylim: Tuple[float, float], show_xlabel: bool) -> None:
  188. t = df["time_ms"].to_numpy(dtype=float)
  189. pre = df["CP_pre"].to_numpy(dtype=float)
  190. pre_sem = df["CP_pre_sem"].to_numpy(dtype=float)
  191. post = df["CP_post"].to_numpy(dtype=float)
  192. post_sem = df["CP_post_sem"].to_numpy(dtype=float)
  194. ax.axvspan(50, 250, color=STIM_SHADE, alpha=0.25, zorder=0)
  195. ax.axhline(0.5, color=BASELINE_LINE, linestyle="--", linewidth=1.0, zorder=1)
  196. ax.axvline(0, color=BASELINE_LINE, linestyle="--", linewidth=1.0, zorder=1)
  198. ax.fill_between(t, pre - pre_sem, pre + pre_sem, color=PRE_COLOR, alpha=0.18, linewidth=0, zorder=2)
  199. ax.plot(t, pre, color=PRE_COLOR, linewidth=2.2, zorder=3)
  200. ax.fill_between(t, post - post_sem, post + post_sem, color=POST_COLOR, alpha=0.18, linewidth=0, zorder=2)
  201. ax.plot(t, post, color=POST_COLOR, linewidth=2.2, zorder=3)
  203. ax.set_xlim(-200, 400)
  204. ax.set_ylim(*ylim)
  205. ax.set_xticks([-200, -100, 0, 100, 200, 300, 400])
  206. if show_xlabel:
  207. ax.set_xlabel("Time from stimulus onset (ms)", fontsize=9)
  208. else:
  209. ax.set_xticklabels([])
  211. ax.tick_params(axis="both", labelsize=8, width=1, length=3)
  212. ax.spines["top"].set_visible(False)
  213. ax.spines["right"].set_visible(False)
  214. for side in ["left", "bottom"]:
  215. ax.spines[side].set_linewidth(1.0)
  218. def add_aggregation_panel(ax, df: pd.DataFrame, ylim: Tuple[float, float], sig_label: str) -> None:
  219. x = np.arange(len(EPOCH_ORDER), dtype=float)
  220. pre = df["CP_pre"].to_numpy(dtype=float)
  221. pre_sem = df["CP_pre_sem"].to_numpy(dtype=float)
  222. post = df["CP_post"].to_numpy(dtype=float)
  223. post_sem = df["CP_post_sem"].to_numpy(dtype=float)
  225. ax.axhline(0.5, color=BASELINE_LINE, linestyle="--", linewidth=1.0, zorder=1)
  226. ax.errorbar(x, pre, yerr=pre_sem, color=PRE_COLOR, marker="o", markersize=3.5,
  227. linewidth=2.0, capsize=2.5, zorder=3)
  228. ax.errorbar(x, post, yerr=post_sem, color=POST_COLOR, marker="o", markersize=3.5,
  229. linewidth=2.0, capsize=2.5, zorder=3)
  231. ax.set_xlim(-0.15, 2.15)
  232. ax.set_ylim(*ylim)
  233. ax.set_xticks(x)
  234. ax.set_xticklabels(EPOCH_LABELS, rotation=20, ha="right", fontsize=8)
  235. ax.tick_params(axis="y", labelsize=8, width=1, length=3)
  236. ax.tick_params(axis="x", width=1, length=3)
  237. ax.spines["top"].set_visible(False)
  238. ax.spines["right"].set_visible(False)
  239. for side in ["left", "bottom"]:
  240. ax.spines[side].set_linewidth(1.0)
  242. # Significance marker over stimulus-response epoch.
  243. y_top = ylim[1] - 0.08 * (ylim[1] - ylim[0])
  244. y_bar = ylim[1] - 0.13 * (ylim[1] - ylim[0])
  245. ax.plot([0.82, 1.18], [y_bar, y_bar], color="black", linewidth=1.0, clip_on=False)
  246. ax.text(1.0, y_top, sig_label, ha="center", va="bottom", fontsize=10, fontweight="bold")
  249. # ---------------------------------------------------------------------
  250. # Main
  251. # ---------------------------------------------------------------------
  252. def main() -> None:
  253. plt.rcParams.update({
  254. "font.family": "Arial",
  255. "pdf.fonttype": 42,
  256. "ps.fonttype": 42,
  257. "axes.linewidth": 1.0,
  258. })
  260. trace_df = load_timecourse_source()
  261. summary_df = load_epoch_summary_source()
  263. fig = plt.figure(figsize=(8.3, 7.2))
  264. gs = fig.add_gridspec(
  265. nrows=3,
  266. ncols=3,
  267. left=0.14,
  268. right=0.98,
  269. top=0.94,
  270. bottom=0.12,
  271. wspace=0.34,
  272. hspace=0.34,
  273. width_ratios=[1.0, 1.0, 0.72],
  274. )
  276. axes = [[fig.add_subplot(gs[r, c]) for c in range(3)] for r in range(3)]
  278. for r, band in enumerate(BANDS):
  279. band_key = band["band_key"]
  280. show_xlabel = r == 2
  282. # Left and middle columns: MT and V4 time courses.
  283. for c, area in enumerate(["MT", "V4"]):
  284. ax = axes[r][c]
  285. df_area = load_area(trace_df, band_key, area)
  286. add_timecourse_panel(ax, df_area, band["area_ylim"][area], show_xlabel)
  287. if r == 0:
  288. ax.set_title(AREA_TITLES[area], fontsize=11, fontweight="bold", pad=5)
  289. if c == 0:
  290. ax.set_ylabel("Choice probability", fontsize=9)
  291. else:
  292. ax.set_ylabel("")
  293. # Stimulus-window significance marker in area panels.
  294. y = band["area_ylim"][area][1] - 0.08 * (band["area_ylim"][area][1] - band["area_ylim"][area][0])
  295. ax.text(150, y, band["sig"], ha="center", va="bottom", fontsize=10, fontweight="bold")
  297. # Right column: epoch aggregation.
  298. ax_agg = axes[r][2]
  299. df_agg = load_aggregation(summary_df, band_key)
  300. add_aggregation_panel(ax_agg, df_agg, band["agg_ylim"], band["sig"])
  301. if r == 0:
  302. ax_agg.set_title("Aggregation", fontsize=11, fontweight="bold", pad=5)
  303. ax_agg.set_ylabel("Choice probability", fontsize=9)
  305. # Row labels and panel letters.
  306. row_ax = axes[r][0]
  307. row_ax.text(
  308. -0.47, 0.50, band["label"], transform=row_ax.transAxes,
  309. ha="right", va="center", fontsize=11, fontweight="bold", linespacing=0.9,
  310. )
  311. row_ax.text(
  312. -0.62, 1.08, band["panel"], transform=row_ax.transAxes,
  313. ha="left", va="top", fontsize=13, fontweight="bold",
  314. )
  316. # Legend at bottom center.
  317. from matplotlib.lines import Line2D
  318. legend_handles = [
  319. Line2D([0], [0], color=PRE_COLOR, linewidth=4, label="Pre-inactivation"),
  320. Line2D([0], [0], color=POST_COLOR, linewidth=4, label="Post-inactivation"),
  321. ]
  322. fig.legend(
  323. handles=legend_handles,
  324. loc="lower center",
  325. bbox_to_anchor=(0.54, 0.035),
  326. ncol=2,
  327. frameon=False,
  328. fontsize=10,
  329. handlelength=2.3,
  330. columnspacing=3.0,
  331. )
  333. out_pdf = OUT_DIR / "Fig3_CP_timecourses.pdf"
  334. out_png = OUT_DIR / "Fig3_CP_timecourses.png"
  335. fig.savefig(out_pdf, bbox_inches="tight")
  336. fig.savefig(out_png, dpi=300, bbox_inches="tight")
  337. plt.close(fig)
  338. print(f"Saved: {out_pdf}")
  339. print(f"Saved: {out_png}")
  342. if __name__ == "__main__":
  343. main()

plot_fig3_cp_timecourses.py at commit 7198f0c, under MIT · at the source

Overview

Authors: Yueyue Sapphire Hou1, Pooya Laamerad1,2, Liu D Liu1, Christopher C Pack1
ORCID iDs: Yueyue Sapphire Hou
  1. Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
  2. Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
Institutions: Montreal Neurological Institute and Hospital (Canada); McGill University (Canada); University of Pennsylvania (United States)
Journal: PLoS biology, volume 24, issue 6, article e3003873
Dates: received 23 November 2025; accepted 9 June 2026; published online 22 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003873 · PMID 42330005 · PMCID PMC13298987 · OpenAlex W7165543166
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality), non-human primate (organism)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Single-unit activity, calcium imaging, Physiology & signal measures
MeSH: Decision Making*, Visual Cortex*, Action Potentials, Animals, Local Field Potential Measurement, Macaca mulatta, Male, Neurons, Photic Stimulation, Visual Perception (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: China Scholarship Council (202308880013); Canadian Institutes of Health Research (PJT178071)
Citations: not cited yet (Europe PMC); 75 references in the paper

Abstract

Fluctuations in single-neuron activity in the sensory cortex often correlate with perceptual decisions. This kind of correlation is often hypothesized to reflect a causal influence of sensory signals on decisions, but it can be attributed to various noncausal factors as well. To disentangle these different possibilities, we have examined local field potentials (LFPs) recorded from the middle temporal (MT) area and area V4 of nonhuman primates (Macaca mulatta) while they performed two different perceptual decision-making tasks. Compared to single-neuron spiking, LFPs have the advantage of being decomposable into frequency bands that are associated with different anatomical sources of input. More importantly, they persist when spiking activity is inactivated, which precludes a causal influence of the corresponding neural activity on behavior. We found that high-gamma frequency (70–150 Hz) LFP power was correlated with perceptual decisions and that this correlation disappeared when spikes were inactivated, consistent with a causal role for this frequency band in decision-making. These signals overlapped in time with decision signals in the lower gamma band (30–70 Hz), which persisted after spiking inactivation, suggesting a noncausal input. Interestingly, lower-frequency LFP signals (5–30 Hz) reflected both impending perceptual decisions and the outcome of preceding trials, suggesting a modulatory influence of recent experience on neural dynamics. Our results, therefore, reveal that neural activity multiplexes different sources of information about perceptual decisions and that these types of information can be estimated reliably from different LFP frequencies.

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 8 matches between paragraphs and lines of code.

Zenodo 20583873

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (7 files), NumPy (7 files), pandas (4 files), Chronux (2 files), ggplot2 (2 files), tidyverse (2 files), NeuroDSP (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
19 files
At the source: doi.org/10.5281/zenodo.20583873

yueyuesapphirehou/LFP_choice_probability

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 7198f0cd64a2507e6bb41cc7278f271ab714dd78, 7 June 2026
Languages: Python (10), MATLAB (5), R (2)
Size: 51 files, 17 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: Matplotlib (7 files), NumPy (7 files), pandas (4 files), Chronux (2 files), ggplot2 (2 files), tidyverse (2 files), NeuroDSP (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
19 files
At the source: github.com/yueyuesapphirehou/LFP_choice_probability

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

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.

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 34 scripts, each with its path and the digest of its content;
  • 8 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

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

Data Availability

The minimal dataset underlying the findings and the accompanying custom analysis code are available without restriction at https://doi.org/10.5281/zenodo.20583873. This repository is also available on GitHub at https://github.com/yueyuesapphirehou/LFP_choice_probability.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 10 MeSH terms, 2 funders, 74 references.

Cite

This paper

Hou, Y. S., Laamerad, P., Liu, L. D., & Pack, C. C. (2026). Distinct sources of decision-related signals in visual cortex are represented in different local field potential bands. PLoS biology, 24(6), e3003873. https://doi.org/10.1371/journal.pbio.3003873

BibTeX

@article{hou2026distinct,
author = {Hou, Yueyue Sapphire and Laamerad, Pooya and Liu, Liu D and Pack, Christopher C},
title = {{Distinct sources of decision-related signals in visual cortex are represented in different local field potential bands}},
journal = {PLoS biology},
year = {2026},
month = jun,
volume = {24},
number = {6},
pages = {e3003873},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003873},
url = {https://doi.org/10.1371/journal.pbio.3003873},
pmid = {42330005},
pmcid = {PMC13298987}
}

RIS

TY - JOUR
AU - Hou, Yueyue Sapphire
AU - Laamerad, Pooya
AU - Liu, Liu D
AU - Pack, Christopher C
TI - Distinct sources of decision-related signals in visual cortex are represented in different local field potential bands
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/06/22
VL - 24
IS - 6
SP - e3003873
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003873
UR - https://doi.org/10.1371/journal.pbio.3003873
LA - en
ER -

CSL-JSON

{
"id": "10.1371/journal.pbio.3003873",
"type": "article-journal",
"title": "Distinct sources of decision-related signals in visual cortex are represented in different local field potential bands",
"container-title": "PLoS biology",
"author": [
{
"family": "Hou",
"given": "Yueyue Sapphire"
},
{
"family": "Laamerad",
"given": "Pooya"
},
{
"family": "Liu",
"given": "Liu D"
},
{
"family": "Pack",
"given": "Christopher C"
}
],
"container-title-short": "PLoS Biol",
"volume": "24",
"issue": "6",
"page": "e3003873",
"DOI": "10.1371/journal.pbio.3003873",
"PMID": "42330005",
"PMCID": "PMC13298987",
"ISSN": "1544-9173",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pbio.3003873",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
22
]
]
}
}

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