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Association learning drives synaptic plasticity at feedforward synapses in somatosensory cortex.

Code ↔ Paper

2 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 2 matches
  1. [1] § Methods › Regression discontinuity tree model ↔ RDD_Test_V6.py, lines 92–204 · score 0.79 · regression discontinuity tree, Mann Whitney, cutoff point, qEPSC, variable, depth
  2. [2] § Methods › Quantification and statistical analysis ↔ RDD_Test_V6.py, lines 92–204 · score 0.77 · linear regressions, regression discontinuity tree, Mann Whitney, validated, SEM, bar

Paper

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The authors' code

Python · 220 lines · 9.3 KB · no license · 2 matches

  1. # -*- coding: utf-8 -*-
  2. """
  3. Created on Thu Oct 2 15:51:54 2025
  5. @author: JoeCh
  6. """
  8. import tkinter as tk
  9. from tkinter import filedialog, simpledialog
  10. import pandas as pd
  11. import matplotlib.pyplot as plt
  12. import numpy as np
  13. from sklearn.tree import DecisionTreeRegressor, plot_tree
  14. from sklearn.model_selection import GridSearchCV, cross_val_score
  15. from scipy.stats import mannwhitneyu, linregress, ttest_ind
  17. # Global variables
  18. df = None
  19. running_var = None
  20. outcome_var = None
  22. # Function to create a scrollable frame for radio buttons
  23. def create_scrollable_radiobuttons(columns, parent_frame):
  24. for widget in parent_frame.winfo_children():
  25. widget.destroy()
  26. canvas = tk.Canvas(parent_frame, height=200, width=250)
  27. scrollbar = tk.Scrollbar(parent_frame, orient="vertical", command=canvas.yview)
  28. scroll_frame = tk.Frame(canvas)
  29. scroll_frame.bind("<Configure>", lambda e: canvas.configure(scrollregion=canvas.bbox("all")))
  30. canvas.create_window((0, 0), window=scroll_frame, anchor="nw")
  31. canvas.configure(yscrollcommand=scrollbar.set)
  32. var = tk.StringVar()
  33. for col in columns:
  34. radio_button = tk.Radiobutton(scroll_frame, text=col, variable=var, value=col)
  35. radio_button.pack(anchor=tk.W)
  36. def on_mouse_wheel(event):
  37. canvas.yview_scroll(-1 * (event.delta // 120), "units")
  38. canvas.bind("<Enter>", lambda _: canvas.bind_all("<MouseWheel>", on_mouse_wheel))
  39. canvas.bind("<Leave>", lambda _: canvas.unbind_all("<MouseWheel>"))
  40. canvas.pack(side="left", fill="both", expand=True)
  41. scrollbar.pack(side="right", fill="y")
  42. return var
  44. # File chooser
  45. def choose_file():
  46. global df, running_var, outcome_var
  47. file_path = filedialog.askopenfilename(filetypes=[("Excel files", "*.xls;*.xlsx")])
  48. if file_path:
  49. df = pd.read_excel(file_path)
  50. columns = df.columns.tolist()
  51. running_var = create_scrollable_radiobuttons(columns, frame_running)
  52. outcome_var = create_scrollable_radiobuttons(columns, frame_outcome)
  54. # Bootstrap CI
  55. def bootstrap_ci(model, X, y, n_bootstrap=1000):
  56. estimates = []
  57. for _ in range(n_bootstrap):
  58. sample_idx = np.random.choice(len(y), len(y), replace=True)
  59. X_sample, y_sample = X[sample_idx], y[sample_idx]
  60. model.fit(X_sample, y_sample)
  61. estimates.append(model.predict(X).mean())
  62. lower_bound = np.percentile(estimates, 2.5)
  63. upper_bound = np.percentile(estimates, 97.5)
  64. return lower_bound, upper_bound
  66. # Permutation test (studentized t-statistic)
  67. def permutation_test_tstat(X, y, cutoff, n_permutations=1000):
  68. mask_above = X.flatten() > cutoff
  69. mask_below = ~mask_above
  70. y_above, y_below = y[mask_above], y[mask_below]
  71. if len(y_above) < 2 or len(y_below) < 2:
  72. return 1.0, 0.0, 0.0
  74. tau = y_above.mean() - y_below.mean()
  75. se = np.sqrt(y_above.var(ddof=1)/len(y_above) + y_below.var(ddof=1)/len(y_below))
  76. orig_t = tau / se if se > 0 else 0.0
  78. perm_tstats = []
  79. for _ in range(n_permutations):
  80. y_perm = np.random.permutation(y)
  81. ya, yb = y_perm[mask_above], y_perm[mask_below]
  82. if len(ya) < 2 or len(yb) < 2:
  83. perm_tstats.append(0.0)
  84. continue
  85. tau_perm = ya.mean() - yb.mean()
  86. se_perm = np.sqrt(ya.var(ddof=1)/len(ya) + yb.var(ddof=1)/len(yb))
  87. perm_tstats.append(tau_perm/se_perm if se_perm > 0 else 0.0)
  89. p_val = (np.sum(np.abs(perm_tstats) >= np.abs(orig_t)) + 1) / (n_permutations + 1)
  90. return p_val, orig_t, tau
  92. # Main analysis
  93. def run_rdt():
  94. global df, running_var, outcome_var
  95. if running_var.get() and outcome_var.get():
  96. running_variable = running_var.get()
  97. outcome_variable = outcome_var.get()
  99. color_map = {'SAT1': 'blue','SAT2': 'magenta','SAT5': 'lime','ACC': 'black',
  100. 'ACC6': 'gray','PSEcntl': 'lightcoral','PSE1': 'red','PSE2': 'maroon',
  101. 'SDT1': 'lightblue','SDT2': 'teal','Naïve': 'Purple','Naive': 'Purple','EE': 'gray'}
  103. X = df[running_variable].values.reshape(-1, 1)
  104. y = df[outcome_variable].values
  106. # Fit tree and find cutoff
  107. param_grid = {'max_depth': range(1, 11)}
  108. dt_regressor = DecisionTreeRegressor()
  109. grid_search = GridSearchCV(dt_regressor, param_grid, cv=5, scoring='neg_mean_squared_error')
  110. grid_search.fit(X, y)
  111. best_tree = grid_search.best_estimator_
  112. optimal_cutoff = best_tree.tree_.threshold[0]
  114. slope, intercept, r_value, p_value, std_err = linregress(X.flatten(), y)
  116. # Plot scatter + regression line
  117. plt.figure(figsize=(10, 6))
  118. scatter_handles = []
  119. used_colors = set()
  120. for grp, color in color_map.items():
  121. df_grp = df[df['GrpTrain'] == grp]
  122. if not df_grp.empty:
  123. scatter_handle = plt.scatter(df_grp[running_variable], df_grp[outcome_variable], color=color, label=grp)
  124. scatter_handles.append(scatter_handle)
  125. used_colors.add(color)
  126. plt.axvline(x=optimal_cutoff, color='red', alpha=0.25, linestyle='--',
  127. label=f'Optimal Cutoff Point: {optimal_cutoff:.2f} µm')
  128. x_values = np.linspace(min(X), max(X), 100)
  129. y_values = intercept + slope * x_values
  130. plt.plot(x_values, y_values, color='black', linestyle='--', linewidth=2,
  131. label=f'Linear Regression (p-value: {p_value:.4f})')
  132. plt.legend()
  133. plt.xlabel("Depth (µm)", fontsize=26)
  134. plt.ylabel("Amplitude (pA)", fontsize=26)
  135. plt.title('Regression Discontinuity Tree with Linear Regression')
  136. plt.show()
  138. # Show decision tree
  139. plt.figure(figsize=(12, 8))
  140. plot_tree(best_tree, feature_names=[running_variable], filled=True, rounded=True)
  141. plt.show()
  143. # Cross-validation
  144. cross_val_scores = cross_val_score(best_tree, X, y, cv=5, scoring='neg_mean_squared_error')
  145. print(f'Cross-validated MSE: {cross_val_scores.mean()}')
  147. # Bootstrap CI
  148. lb, ub = bootstrap_ci(best_tree, X, y)
  149. print(f'Bootstrap CI: ({lb}, {ub})')
  151. # Choose cutoff
  152. cutoff_choice = simpledialog.askstring("Cutoff Selection", "Choose cutoff selection method: (RDT/Manual)")
  153. if cutoff_choice.lower() == 'rdt':
  154. cutoff = optimal_cutoff
  155. elif cutoff_choice.lower() == 'manual':
  156. cutoff = float(simpledialog.askstring("Manual Cutoff", "Enter manual cutoff value:"))
  157. else:
  158. raise ValueError("Invalid choice. Please choose either 'RDT' or 'Manual'.")
  160. # Groups
  161. group_below_cutoff = df[df[running_variable] <= cutoff][outcome_variable]
  162. group_above_cutoff = df[df[running_variable] > cutoff][outcome_variable]
  164. # Mann-Whitney
  165. mw_stat, mw_p = mannwhitneyu(group_below_cutoff, group_above_cutoff)
  167. # Discontinuity τ and t-test
  168. tau = group_above_cutoff.mean() - group_below_cutoff.mean()
  169. t_stat, _ = ttest_ind(group_above_cutoff, group_below_cutoff, equal_var=False)
  171. # Permutation test (t-stat based)
  172. perm_p, orig_t, orig_tau = permutation_test_tstat(X.flatten(), y, cutoff)
  174. print("Average qEPSC below cutoff:", group_below_cutoff.mean())
  175. print("Average qEPSC above cutoff:", group_above_cutoff.mean())
  176. print("Mann-Whitney test p-value:", mw_p)
  177. print(f"Discontinuity estimate (τ): {tau:.4f}")
  178. print(f"T-statistic for discontinuity: {t_stat:.4f}")
  179. print(f"Permutation test p-value: {perm_p}")
  181. # Bar plot
  182. training_group = simpledialog.askstring("Input", "Enter the training group:")
  183. average_below_cutoff = group_below_cutoff.mean()
  184. average_above_cutoff = group_above_cutoff.mean()
  185. std_err_below_cutoff = group_below_cutoff.sem()
  186. std_err_above_cutoff = group_above_cutoff.sem()
  187. bar_color = color_map.get(training_group, 'gray')
  189. plt.figure(figsize=(8, 6))
  190. bars = plt.bar([0, 1],
  191. [average_below_cutoff, average_above_cutoff],
  192. color=[bar_color, bar_color], alpha=0.25,
  193. yerr=[std_err_below_cutoff, std_err_above_cutoff], capsize=5)
  194. for i, group in enumerate([group_below_cutoff, group_above_cutoff]):
  195. x = np.ones(len(group)) * i
  196. jitter = np.linspace(-0.15, 0.15, len(group))
  197. plt.scatter(x + jitter, group, color=bar_color, alpha=0.6)
  198. plt.xticks([0, 1], ['Layer 2', 'Layer 3'], fontsize=26)
  199. plt.ylabel("Amplitude (pA)", fontsize=26)
  200. plt.title('Comparison of Amplitude Below and Above Cutoff', fontsize=18)
  201. plt.text(0.5, max(average_below_cutoff, average_above_cutoff) + 7,
  202. f'MW p={mw_p:.4f} | τ={tau:.2f} pA | T={t_stat:.2f} | Perm p={perm_p:.4f}',
  203. ha='center', fontsize=16)
  204. plt.show()
  206. root = tk.Tk()
  207. root.title("Excel Columns Radiobuttons")
  208. frame_running = tk.Frame(root)
  209. frame_running.pack(side=tk.LEFT, padx=10, pady=10)
  210. label_running = tk.Label(frame_running, text="X (Running)")
  211. label_running.pack()
  212. frame_outcome = tk.Frame(root)
  213. frame_outcome.pack(side=tk.LEFT, padx=10, pady=10)
  214. label_outcome = tk.Label(frame_outcome, text="Y (Outcome)")
  215. label_outcome.pack()
  216. button_choose = tk.Button(root, text="Choose Excel File", command=choose_file)
  217. button_choose.pack()
  218. button_plot = tk.Button(root, text="Run RDT Analysis", command=run_rdt)
  219. button_plot.pack()
  220. root.mainloop()

RDD_Test_V6.py at commit 36cdd36, no license · at the source

Overview

Authors: Joseph A Christian1, Eunsol Park1,2, Alison L Barth1
ORCID iDs: Joseph A Christian, Eunsol Park, Alison L Barth
  1. Department of Biological Sciences and Center for Neural Basis of Cognition, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, United States
  2. Unit on Functional Neural Circuits, Systems Neurodevelopment Laboratory, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, United States
Institutions: Center for the Neural Basis of Cognition (United States); Carnegie Mellon University (United States); National Institutes of Health (United States); National Institute of Mental Health (United States)
Journal: Cerebral cortex (New York, N.Y. : 1991), volume 36, issue 6, article bhag047
Dates: received 7 January 2026; accepted 23 March 2026; published online 5 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/cercor/bhag047 · PMID 42248694 · PMCID PMC13240849 · OpenAlex W7163667937
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Evoked potentials, Connectivity
Keywords: electrophysiology, excitatory synapses, high-throughput training, quantal analysis, superficial layers
MeSH: Association Learning*, Neuronal Plasticity*, Somatosensory Cortex*, Synapses*, Animals, Excitatory Postsynaptic Potentials, Female, Male, Mice, Mice, Inbred C57BL, Mice, Transgenic, Optogenetics, Pyramidal Cells, Reward, Vibrissae (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIH HHS (R01 NS123711, R21 NS127354)
Citations: not cited yet (Europe PMC); 97 references in the paper
Research resources: C57BL/6J mice RRID:IMSR_JAX:000664, Scnn1a-tg3-Cre mice RRID:IMSR_JAX:009613, Ai32 mice RRID:IMSR_JAX:024109, Nelf-Cre mice RRID:MMRRC_037424-UCD

Abstract

Learning broadly alters neocortical synapses, although the input and target specificity for this plasticity has not been well-defined. Feedforward synapses into sensory cortex have early critical periods for plasticity after which they are resistant to experience-dependent changes. Whether these synapses are altered during learning has not been investigated, particularly in a setting where animals must identify causal relationships between sensory stimuli and rewards. Here, we examined whether these feedforward synapses can be altered by training mice in a freely-moving and whisker-dependent association task. Pathway-specific optogenetic stimulation and analysis of quantal excitatory postsynaptic currents in layer 2/3 (L2/3) pyramidal neurons from barrel cortex revealed a rapid and transient potentiation of layer 4 (L4) inputs at the onset of training, without any change in thalamocortical inputs onto L4 neurons. In contrast, pseudotraining—where stimuli and rewards were decoupled—drove depression of L4–L2/3 quantal excitatory postsynaptic currents. Because environmental enrichment did not influence quantal excitatory postsynaptic current amplitude, these data suggest that reward-prediction accuracy is a key driver of feedforward plasticity in primary sensory cortex.

Significance statement: Although it is well accepted that sensory learning can alter cortical synapses, the pathways that are modified and the specific cues that drive this synaptic change have not been systematically investigated. By manipulating stimulus–reward probabilities, we identified discrete and opposite changes in the strength of L4–L2/3 synapses depending on the predictive accuracy of the stimulus. These data suggest that feedforward sensory circuits are exquisitely sensitive to the predictive value of sensory input in a goal-directed task.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

barthlab/Sensory-association-training-behavior

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0321e55f8bb3a86c0fa4c20fd8622ea038c5a554, 12 October 2020
Languages: MATLAB (1)
Size: 14 files, 1 script
Software Heritage: not archived
Found in: the resources table
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/barthlab/Sensory-association-training-behavior

barthlab/RegressionDiscontinuityTree

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 36cdd36346d24f65dc334a4e39cb2a9079a154f9, 8 December 2025
Languages: Python (1)
Size: 2 files, 1 script
Software Heritage: not archived
Found in: the resources table
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (1 file), NumPy (1 file), pandas (1 file), scikit-learn (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/barthlab/RegressionDiscontinuityTree

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 15 MeSH terms, 1 funder, 96 references, 4 RRIDs.

Cite

This paper

Christian, J. A., Park, E., & Barth, A. L. (2026). Association learning drives synaptic plasticity at feedforward synapses in somatosensory cortex. Cerebral cortex (New York, N.Y. : 1991), 36(6), bhag047. https://doi.org/10.1093/cercor/bhag047

BibTeX

@article{christian2026association,
author = {Christian, Joseph A and Park, Eunsol and Barth, Alison L},
title = {{Association learning drives synaptic plasticity at feedforward synapses in somatosensory cortex}},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
year = {2026},
month = jun,
volume = {36},
number = {6},
pages = {bhag047},
publisher = {Oxford University Press},
issn = {1047-3211},
doi = {10.1093/cercor/bhag047},
url = {https://doi.org/10.1093/cercor/bhag047},
pmid = {42248694},
pmcid = {PMC13240849}
}

RIS

TY - JOUR
AU - Christian, Joseph A
AU - Park, Eunsol
AU - Barth, Alison L
TI - Association learning drives synaptic plasticity at feedforward synapses in somatosensory cortex
T2 - Cerebral cortex (New York, N.Y. : 1991)
J2 - Cereb Cortex
PY - 2026
DA - 2026/06/01
VL - 36
IS - 6
SP - bhag047
SN - 1047-3211
PB - Oxford University Press
DO - 10.1093/cercor/bhag047
UR - https://doi.org/10.1093/cercor/bhag047
LA - en
ER -

CSL-JSON

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"id": "10.1093/cercor/bhag047",
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"title": "Association learning drives synaptic plasticity at feedforward synapses in somatosensory cortex",
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"author": [
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"family": "Christian",
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"given": "Alison L"
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"container-title-short": "Cereb Cortex",
"volume": "36",
"issue": "6",
"page": "bhag047",
"DOI": "10.1093/cercor/bhag047",
"PMID": "42248694",
"PMCID": "PMC13240849",
"ISSN": "1047-3211",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/cercor/bhag047",
"language": "en",
"issued": {
"date-parts": [
[
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1
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]
}
}

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