OSCR

Spatially targeted inhibitory rhythms differentially affect neuronal integration.

Code ↔ Paper

29 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 29 matches
  1. [1] § Results › Frequency-specific effects of rhythmic inhibition on neuronal integration ↔ scripts/Fig7.ipynb, lines 1–36 · score 0.93 · fastest rhythm capable, beta band frequencies, minimal phase separation, events occurred near, dendritic spike offsets, preferred phase
  2. [2] § Results › Relationship between dendritic and somatic spikes ↔ scripts/Fig2_3.ipynb, lines 1–29 · score 0.93 · substantially longer apical, apical nexus compensate, Voltage gated Ca2, principal drivers, synaptic activity, tuft synapses
  3. [3] § Results › Frequency-specific effects of rhythmic inhibition on neuronal integration ↔ scripts/Fig7.ipynb, lines 1–36 · score 0.91 · membrane potential diverged, biased negative, biased positive, momentary changes, membrane voltage fluctuations, inhibition depolarized
  4. [4] § Results › Effect of beta and gamma rhythmic inhibition on neuronal integration ↔ scripts/Fig5.ipynb, lines 1–45 · score 0.86 · entire dendritic tree, spanning sites electrotonically, inhibition tonically, strongest impact, beta modulated, electronically close
  5. [5] § Results › Distinct excitation/inhibition balance effects of perisomatic and distal dendritic inhibition ↔ scripts/Fig4.ipynb, lines 1–33 · score 0.85 · interneurons form dense, lagged feedback inhibition, rapidly counterbalanced, local pyramidal neurons, excitatory activity, proportionate inhibition
  6. [6] § Results › Distinct excitation/inhibition balance effects of perisomatic and distal dendritic inhibition ↔ scripts/Fig4.ipynb, lines 1–33 · score 0.84 · dynamic variation, rescaled version, emulate situations, independently varied, inhibitory synaptic drive, excitation rate
  7. [7] § Results › Relationship between dendritic and somatic spikes ↔ scripts/Fig2_3.ipynb, lines 161–183 · score 0.83 · entire apical trunk, apical tuft normally, thresholded nonlinearity, spikes originate, driving Ca2, apical nexus
  8. [8] § Results › Modulation of dendritic spikes during oscillatory bursts ↔ scripts/Fig9.ipynb, lines 1–52 · score 0.83 · burst regime, evolving entrainment, rhythms mirrored, entrained somatic action, minimal modulation, Gamma bursts
  9. [9] § Results › Frequency-specific effects of rhythmic inhibition on neuronal integration ↔ scripts/Fig8.ipynb, lines 1–49 · score 0.83 · depolarizing membrane potential, membrane voltage fluctuations, inhibition depolarized, produced phase dependent, Lower frequency inhibition, membrane potential equivalent
  10. [10] § Results › Effect of beta and gamma rhythmic inhibition on neuronal integration ↔ scripts/Fig6.ipynb, lines 1–23 · score 0.81 · decreased excitation arising, voltage threshold shifted, voltage gated Na, gamma phase modulating, action potential initiation, trough phase
  11. [11] § Results › Effect of beta and gamma rhythmic inhibition on neuronal integration ↔ scripts/Fig5_Supp.ipynb, lines 1–41 · score 0.80 · gamma synapses altered, supply excitatory drive, delivering beta rhythmic, soma raised, somatic excitability, somatic membrane potential
  12. [12] § Results › Effect of beta and gamma rhythmic inhibition on neuronal integration ↔ scripts/Fig5_Supp.ipynb, lines 1–41 · score 0.78 · fast periodicity, rhythm modulated Ca2, dramatically reduced, gamma rhythms delivered, delivered perisomatically, phase modulation
  13. [13] § Methods › CC between presynaptic spikes and action potentials (Figure 10) ↔ scripts/Fig10.ipynb, lines 160–212 · score 0.70 · Presynaptic spike, positive peak, phase stratified, inhibitory modulation, root, zero
  14. [14] § Methods › Synapse distribution and extrinsic inputs › Rhythmic inhibition (Figures 5 and 6) ↔ scripts/Fig5.ipynb, lines 67–97 · score 0.70 · modulating perisomatic synapses, modulating distal inhibitory, modulation ensured, rhythms produced, inhibitory synapses, Beta rhythms
  15. [15] § Results › Effect of beta and gamma rhythms on responding to clustered synaptic drive ↔ scripts/Fig10.ipynb, lines 160–212 · score 0.68 · Separate cross correlograms, spurious periodicities, presynaptic spikes, trough phase, clustered, CC
  16. [16] § Methods › Synapse distribution and extrinsic inputs › Clustered synaptic input (Figure 10) ↔ scripts/Fig10.ipynb, lines 80–142 · score 0.68 · 40–50 um, directly connected, Poisson process, bifurcation, basal dendrites, jittering
  17. [17] § Results › Effect of beta and gamma rhythmic inhibition on neuronal integration ↔ scripts/Fig8.ipynb, lines 130–244 · score 0.67 · voltage threshold shifted, voltage gated Na, membrane voltage, phase modulating, trough phase, peak phase
  18. [18] § Results › Effect of beta and gamma rhythms on responding to clustered synaptic drive ↔ scripts/Fig10.ipynb, lines 1–62 · score 0.67 · bidirectionally modulated, location dependent manner, rhythms regulate, dendritic tree, pyramidal neurons, gamma rhythms
  19. [19] § Results › Modulation of dendritic spikes during oscillatory bursts ↔ scripts/Fig9.ipynb, lines 1–52 · score 0.66 · tonically delivered rhythms, oscillatory bursts, gamma rhythms occur, beta bursts, milliseconds, Figure 9
  20. [20] § Results › Distinct excitation/inhibition balance effects of perisomatic and distal dendritic inhibition ↔ scripts/Fig4.ipynb, lines 219–266 · score 0.66 · relatively unaffected, temporal relationship, apical branches, Basal branches, broadening, perisomatic inhibition
  21. [21] § Results › Relationship between dendritic and somatic spikes ↔ scripts/Fig2_3.ipynb, lines 1–29 · score 0.64 · compartments nearest, apical trunk, 2 ms, 2–3 ms, somatic spiking, somatic action potentials
  22. [22] § Results › Effect of beta and gamma rhythmic inhibition on neuronal integration ↔ scripts/Fig6_Supp.ipynb, lines 107–160 · score 0.59 · cumulative probability, somatic membrane voltage, Probability distribution, somatic membrane potentials, delivered perisomatically, minimal
  23. [23] § Results › Effect of beta and gamma rhythmic inhibition on neuronal integration ↔ scripts/Fig6_Supp.ipynb, lines 107–160 · score 0.56 · supply excitatory drive, somatic membrane potential, action potential threshold, beta rhythmic inhibition, Gamma rhythmic inhibition, peak phase
  24. [24] § Methods › Divergence, probability of transmission, and functional clustering ↔ scripts/Fig10.ipynb, lines 1–62 · score 0.55 · presynaptic cell assembly, functional presynaptic cell, assemblies activate, dendritic branch, 5–10 um, cluster
  25. [25] § Methods › Phase histogram of dendritic spikes (Figure 5) ↔ scripts/Fig5.ipynb, lines 100–123 · score 0.55 · bin widths, dendritic spike binary, phase bin, electronic distance, inhibitory rhythm, Entrainment
  26. [26] § Methods › STA of dendritic spikes (Figures 2 and 3) ↔ src/sta_ap_dendevt.py, lines 10–74 · score 0.53 · spike triggered, electrotonic distance, dendritic compartment, median, STAs, dendritic spike
  27. [27] § Results › Distinct excitation/inhibition balance effects of perisomatic and distal dendritic inhibition ↔ scripts/Fig4.ipynb, lines 392–415 · score 0.52 · lag produced maximal, perisomatic inhibition modulate, synaptic drive, dendritic spiking events, conversion, coupling
  28. [28] § Methods › STA of dendritic spikes (Figures 2 and 3) ↔ src/sta_files.py, lines 15–83 · score 0.52 · spike triggered, electrotonic distance, dendritic compartment, somatic action potentials, STAs, dendritic spike
  29. [29] § Results › Effect of beta and gamma rhythmic inhibition on neuronal integration ↔ scripts/Fig6_Supp.ipynb, lines 1–23 · score 0.51 · rhythms diminishes, rhythmic inhibition depends, somatic excitability, delivered perisomatically, gamma rhythms, impinges

Paper

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The authors' code

Jupyter notebook · 330 lines · 15 KB · MIT · 5 matches

  1. # %% [markdown]
  2. # # Figure 10: Beta and Gamma Rhythms Bidirectionally Modulate Clustered Synaptic Drive in a Location-Dependent Manner
  3. #
  4. # This notebook analyzes how beta and gamma rhythmic inhibition affect the responsiveness of pyramidal neurons to clustered excitatory synaptic inputs at different dendritic locations. Based on Headley et al. (2026), *Spatially targeted inhibitory rhythms differentially affect neuronal integration*, eLife.
  5. #
  6. # ## Biological Context:
  7. #
  8. # **Functional clustering:** Experimental evidence shows that synapses cluster on dendritic branches such that functional presynaptic cell assemblies activate the same branch within 5-10 µm. This clustering facilitates dendritic integration and can drive dendritic spikes.
  9. #
  10. # **Location matters:** The dendrites receive inputs from different cortical layers:
  11. # - **Distal apical dendrites (tuft):** Long-range inputs from higher cortical areas (feedback/top-down)
  12. # - **Proximal basal dendrites:** Local inputs from layer 5 pyramidal neurons (lateral connectivity)
  13. #
  14. #
  15. # ## Key Findings:
  16. #
  17. # 3. **Spatial specificity** (apical vs. basal location)
  18. #
  19. # **Beta rhythms (16 Hz, distal dendrites):**2. **Phase dependence** (peak vs. trough modulation)
  20. #
  21. # - **Bidirectionally modulate** transmission from distal (apical) clustered inputs1. **Strength of coupling** (area under cross-correlation peak)
  22. #
  23. # - Enhance during trough phase (low inhibition)Phase-stratified cross-correlations between clustered presynaptic spikes and somatic action potentials reveal:
  24. #
  25. # - Suppress during peak phase (high inhibition)
  26. #
  27. # - **Suppress** transmission from proximal (basal) clustered inputs## Analyses:
  28. #
  29. # - Effect relative to arhythmic (Poisson) inhibition baseline
  30. #
  31. # 4. **Coactivation:** 40 presynaptic spike trains with ±2 ms jitter from single Poisson process
  32. #
  33. # **Gamma rhythms (64 Hz, perisomatic):**3. **Synaptic density:** 1.3 synapses/µm over 140 µm (187 total synapses)
  34. #
  35. # - **Bidirectionally modulate** transmission from proximal (basal) clustered inputs2. **Clustered excitatory inputs** at distal apical dendrites (near nexus) or proximal basal dendrites
  36. #
  37. # - Enhance during trough phase1. **Rhythmic inhibition** (16 Hz distal or 64 Hz perisomatic) or Poisson control
  38. #
  39. # - Suppress during peak phaseThe simulations include:
  40. #
  41. # - **Suppress or barely affect** transmission from distal (apical) clustered inputs
  42. #
  43. # ## Simulation Details:
  44. #
  45. # **Counterposed modulation:** Beta and gamma rhythms regulate neuronal sensitivity to afferents throughout the dendritic tree in a **location-dependent manner** that is opposite/complementary:
  46. #
  47. # - Beta gates distal inputs (feedback/top-down signals)- Gamma gates proximal inputs (local/feedforward signals)
  49. # %%
  50. import sys
  51. import os
  52. sys.path.append('..') # have to do this for relative imports in jupyter
  53. import numpy as np
  54. import pandas as pd
  55. import scipy as sp
  56. import matplotlib.pyplot as plt
  57. import matplotlib.colors as colors
  58. from sklearn.linear_model import LogisticRegression
  59. from src.cc_presyn_phdep_files import cc_presyn_phdep_files
  60. from src.load_spike_h5 import load_spike_h5
  61. from src.load_spike_aux_h5 import load_spike_aux_h5
  62. from src.cc_ptpt import cc_ptpt
  64. # %% [markdown]
  65. # ## 1.0 Analysis Parameters
  66. #
  67. # **Spurious periodicities:** Since both presynaptic spikes and action potentials have inherent periodicities from the rhythmic inhibition, these must be corrected by converting to frequency domain, setting coefficients at the inhibitory modulation frequency to 0, and converting back (see Methods).
  68. #
  69. # ### 1.1 Simulation Constants and Cross-Correlation Windows
  70. #
  71. # Cross-correlation analysis measures the temporal coupling between clustered presynaptic spike times and somatic action potentials. The analysis window captures the synaptic-to-spike delay and reveals how rhythmic inhibition modulates this coupling in a phase-dependent manner.
  73. # %%
  74. samps_per_ms = 10 # samples per millisecond
  75. fs = 10000 # sampling frequency
  76. sim_win = [0, 9000000] # beginning and start points of simulation in samples
  77. cc_bin = samps_per_ms * 1 # bin size for cross-correlation in samples
  78. cc_win = [-499, 500] # number of lags for cross-correlation
  80. # %% [markdown]
  81. # ### 1.2 Simulation Files
  82. #
  83. # - Jitter: ±2 ms from single Poisson process (simulates coordinated assembly activity)
  84. #
  85. # These files contain simulations with functional clustering of excitatory synapses:- Coactivation: 40 presynaptic neurons, each driving ~4.6 synapses
  86. #
  87. # - Density: 1.3 synapses/µm (similar to in vivo functional clusters)
  88. #
  89. # **Clustered input locations:****Synaptic clustering parameters:**
  90. #
  91. # - **conc_apical:** 140 µm at apical nexus (bifurcation toward tuft)
  92. #
  93. # - **conc_basal:** 140 µm across 3 basal dendrites directly connected to soma (40-50 µm each)- **poiss:** Arhythmic Poisson inhibition (baseline)
  94. #
  95. # - **diff:** Diffuse (control) - inputs distributed throughout dendrites- **64 Hz:** Gamma rhythm at perisomatic compartments (<100 µm from soma)
  96. #
  97. # - **16 Hz:** Beta rhythm at distal dendrites (>100 µm from soma)
  98. # **Rhythmic inhibition conditions:**
  100. # %%
  101. # locate simulation files
  102. dir_list = [{'RootDir': 'D:\\DendCompOscPublic\\output_clust_16Hz_conc_apical\\3groups',
  103. 'InhibType': '16',
  104. 'ExcType': 'conc',
  105. 'ExcLoc': 'apical'},
  106. {'RootDir': 'D:\\DendCompOscPublic\\output_clust_16Hz_conc_basal\\3groups',
  107. 'InhibType': '16',
  108. 'ExcType': 'conc',
  109. 'ExcLoc': 'basal'},
  110. {'RootDir': 'D:\\DendCompOscPublic\\output_clust_16Hz_diff\\long',
  111. 'InhibType': '16',
  112. 'ExcType': 'diff',
  113. 'ExcLoc': 'apical'},
  115. {'RootDir': 'D:\\DendCompOscPublic\\output_clust_64Hz_conc_apical\\3groups',
  116. 'InhibType': '64',
  117. 'ExcType': 'conc',
  118. 'ExcLoc': 'apical'},
  119. {'RootDir': 'D:\\DendCompOscPublic\\output_clust_64Hz_conc_basal\\3groups',
  120. 'InhibType': '64',
  121. 'ExcType': 'conc',
  122. 'ExcLoc': 'basal'},
  123. {'RootDir': 'D:\\DendCompOscPublic\\output_clust_64Hz_diff\\long',
  124. 'InhibType': '64',
  125. 'ExcType': 'diff',
  126. 'ExcLoc': 'apical'},
  128. {'RootDir': 'D:\\DendCompOscPublic\\output_clust_poiss_conc_apical\\3groups',
  129. 'InhibType': 'poiss',
  130. 'ExcType': 'conc',
  131. 'ExcLoc': 'apical'},
  132. {'RootDir': 'D:\\DendCompOscPublic\\output_clust_poiss_conc_basal\\3groups',
  133. 'InhibType': 'poiss',
  134. 'ExcType': 'conc',
  135. 'ExcLoc': 'basal'},
  136. {'RootDir': 'D:\\DendCompOscPublic\\output_clust_poiss_diff\\long',
  137. 'InhibType': 'poiss',
  138. 'ExcType': 'diff',
  139. 'ExcLoc': 'apical'}]
  142. sim_df = pd.DataFrame(dir_list)
  144. # %% [markdown]
  145. # ### 1.3 Generate Inhibitory Rhythm Phase Time Series
  146. #
  147. # Sinusoidal modulation of inhibitory drive creates phase labels for stratifying cross-correlation analysis. Events occurring during positive phase (above mean) are labeled as 'peak' phase, while those during negative phase are labeled as 'trough' phase. This allows separate measurement of synaptic transmission strength during high vs. low inhibition periods.
  149. # %%
  150. # generate phase time series for inhibitory afferents
  151. t_ser = np.arange(sim_win[0], sim_win[1], 1) / fs # seconds
  152. sin_16_ser = np.sin(t_ser*16*2*np.pi)
  153. sin_64_ser = np.sin(t_ser*64*2*np.pi)
  155. fig, ax = plt.subplots()
  156. ax.plot(t_ser[0:1000],sin_16_ser[0:1000])
  157. ax.plot(t_ser[0:1000],sin_64_ser[0:1000])
  158. plt.show()
  160. # %% [markdown]
  161. # ## 2.0 Phase-Stratified Cross-Correlation Analysis
  162. #
  163. # This creates a **frequency-specific routing mechanism** where different rhythms gate different information streams based on their cortical origin and dendritic termination zone.
  164. #
  165. # This analysis quantifies how rhythmic inhibition modulates the **conversion of clustered presynaptic activity into somatic action potentials**.
  166. #
  167. # - Gamma (perisomatic) → modulates feedforward/local signals arriving at basal dendrites
  168. #
  169. # **Mechanisms revealed:**- Beta (distal dendrites) → modulates feedback/top-down signals arriving at apical tuft
  170. #
  171. # 1. **Bidirectional modulation:** Enhanced transmission during trough, suppressed during peak**Interpretation:** The spatial specificity of these effects suggests that beta and gamma rhythms regulate different input pathways:
  172. #
  173. # 2. **Suppression only:** Reduced transmission during both phases relative to Poisson baseline
  174. # 3. **No effect:** Transmission strength unchanged by rhythm
  176. # %% [markdown]
  177. # ### 2.1 Calculate Phase-Stratified Cross-Correlations
  178. #
  179. # The positive peak nearest zero lag represents the synaptic-to-spike coupling. Its **area under the curve** (integrated over zero-crossings) quantifies the strength of this coupling. Changes in peak area reveal how rhythmic inhibition modulates responsiveness to the clustered input.
  180. #
  181. # The cross-correlation function measures the temporal relationship between clustered presynaptic spikes and somatic action potentials, separated by rhythm phase:**Peak identification:**
  182. #
  183. #
  184. #
  185. # **For rhythmic conditions (16 Hz, 64 Hz):**- Serves as baseline for comparison
  186. #
  187. # - Presynaptic spikes grouped by peak vs. trough phase of inhibitory rhythm- Single cross-correlogram (no phase separation)
  188. #
  189. # - Separate cross-correlograms computed for each phase**For Poisson control:**
  190. #
  191. # - Notch filter removes spurious periodicities at inhibition frequency
  192. # - Smoothing with 1 ms window reduces noise
  194. # %%
  195. def cc_func(sim_dir, inhib_type):
  196. # create full file path for presynaptic spike times and AP times, make OS independent
  197. presyn_file = os.path.join(sim_dir, 'exc_stim_aux_spikes2.h5')
  198. ap_file = os.path.join(sim_dir, 'spikes.h5')
  200. # calculate cross-correlation
  201. if inhib_type == '16':
  202. cc = cc_presyn_phdep_files(presyn_file, ap_file, sin_16_ser, cc_bin, cc_win, sm_win=1, notch_freq=16)
  203. elif inhib_type == '64':
  204. cc = cc_presyn_phdep_files(presyn_file, ap_file, sin_64_ser, cc_bin, cc_win, sm_win=1, notch_freq=64)
  205. elif inhib_type == 'poiss':
  206. spk_pts = load_spike_h5(ap_file)
  207. pre_pts = load_spike_aux_h5(presyn_file)
  208. cc = {'poiss': cc_ptpt(spk_pts, pre_pts, cc_bin, cc_win, 1)}
  209. return cc
  211. # calculate cross-correlation for each simulation
  212. sim_cc_df = sim_df.assign(AllCC = sim_df.apply(lambda x: cc_func(x['RootDir'], x['InhibType']), axis=1))
  214. # %%
  215. # breakout CC dictionaries
  216. sim_cc_df = sim_cc_df.join(pd.DataFrame(sim_cc_df['AllCC'].tolist()))
  217. sim_cc_df = pd.melt(sim_cc_df, id_vars=['RootDir', 'InhibType', 'ExcType', 'ExcLoc'],
  218. value_vars=['t', 'p', 'poiss'], var_name='InhibPhase', value_name='CC')
  219. sim_cc_df = sim_cc_df.dropna()
  220. sim_cc_df = sim_cc_df.reset_index(drop=True)
  221. sim_cc_df = sim_cc_df.join(pd.DataFrame(sim_cc_df['CC'].tolist()))
  222. sim_cc_df.drop('CC', axis=1)
  224. # smooth cross-correlations
  225. def sm_func(x):
  226. sm_kern = np.hamming(3)
  227. x_sm = np.convolve(x, sm_kern/np.sum(sm_kern), mode='same')
  228. x_sm -= np.mean(x_sm)
  229. return x_sm
  231. # smooth values_corr in sim_cc_df
  232. sim_cc_df['values_corr_sm'] = sim_cc_df['values_corr'].apply(sm_func)
  234. # %%
  235. # group by excitatory input type
  236. exc_df = sim_cc_df.groupby(['ExcType', 'ExcLoc'])
  238. # %%
  239. # plot corrected cross-correlations by excitatory input type
  240. def plot_cc(sim_row, ax, **kwargs):
  241. ax.stairs(sim_row['values_corr_sm'].iloc[0][0:-1], sim_row['lags'].iloc[0], **kwargs)
  243. def plot_exp(sim_set, exp_name, exp_freq, ax):
  244. plot_cc(sim_set[sim_set['InhibPhase']=='poiss'],ax, color=[0.8]*3, fill=True)
  245. plot_cc(sim_set[sim_set['InhibPhase']=='poiss'],ax, color='k')
  246. plot_cc(sim_set.loc[(sim_set['InhibPhase']=='t') & (sim_set['InhibType']==exp_freq)],ax, color='b')
  247. plot_cc(sim_set.loc[(sim_set['InhibPhase']=='p') & (sim_set['InhibType']==exp_freq)],ax, color='r')
  249. ax.set_title((exp_name, exp_freq))
  250. #ax.set_ylim(-0.01,0.015)
  251. ax.set_xlim(-10,20)
  252. # create grid only on origin
  253. ax.vlines(0, -0.01, 0.02, color='k', linewidth=0.5, alpha=0.5)
  254. ax.hlines(0, -10, 20, color='k', linewidth=0.5, alpha=0.5)
  257. fig,ax = plt.subplots(2,2)
  258. plot_exp(exc_df.get_group(('conc', 'basal')), ('conc', 'basal'), '16',ax[0,0])
  259. ax[0,0].set_ylim(-0.01,0.02)
  260. plot_exp(exc_df.get_group(('conc', 'basal')), ('conc', 'basal'), '64',ax[0,1])
  261. ax[0,1].set_ylim(-0.01,0.02)
  262. plot_exp(exc_df.get_group(('conc', 'apical')), ('conc', 'apical'), '16',ax[1,0])
  263. ax[1,0].set_ylim(-0.005,0.01)
  264. plot_exp(exc_df.get_group(('conc', 'apical')), ('conc', 'apical'), '64',ax[1,1])
  265. ax[1,1].set_ylim(-0.005,0.01)
  266. fig.set_size_inches(8, 8)
  267. fig.tight_layout()
  269. # %%
  270. # define a function to calculate the area under a curve bordered by zero-crossings
  271. # that encompasses a user specified position
  272. def area_under_curve(x, ind):
  273. # find the zero-crossing before the index
  274. ind0 = np.where(np.diff(np.sign(x[0:ind])))[0]
  275. if len(ind0) == 0:
  276. ind0 = 0
  277. else:
  278. ind0 = ind0[-1]
  280. # find the zero-crossing after the index
  281. ind1 = np.where(np.diff(np.sign(x[ind:-1])))[0]
  283. if len(ind1) == 0:
  284. ind1 = len(x)
  285. else:
  286. ind1 = ind1[0] + ind
  288. # calculate the area under the curve
  289. area = np.trapz(x[ind0:ind1])
  291. return area
  293. # identify the index overlapping with the cross-correlation peak
  294. ind_peak = (-cc_win[0])+5
  296. # calculate the area under the curve for each simulation
  297. sim_cc_df['resp_area'] = sim_cc_df['values_corr_sm'].apply(lambda x: area_under_curve(x, ind_peak))
  299. # %%
  300. exc_df = sim_cc_df.groupby(['ExcType', 'ExcLoc'])[['resp_area', 'InhibType', 'InhibPhase']]
  301. exc_df.get_group(('conc', 'basal'))
  303. # %%
  304. # group by excitatory input type
  306. # plot cross-correlation strengths by excitatory input type and phase
  307. def plot_exp_resp(sim_set, exp_freq, offset, ax):
  308. offset = offset*4
  309. poiss_resp = sim_set.loc[(sim_set['InhibPhase']=='poiss')]['resp_area'].iloc[0]
  310. t_resp = sim_set.loc[(sim_set['InhibPhase']=='t') & (sim_set['InhibType']==exp_freq)]['resp_area'].iloc[0]
  311. p_resp = sim_set.loc[(sim_set['InhibPhase']=='p') & (sim_set['InhibType']==exp_freq)]['resp_area'].iloc[0]
  313. ax.plot(offset+np.array([0.5,2.5]), [poiss_resp, poiss_resp], color='k')
  314. #ax.scatter(offset, poiss_resp, color='k')
  315. ax.plot(offset+np.array([1,2]), [t_resp, p_resp], color='k', linestyle='--')
  316. ax.scatter(offset+1, t_resp, color='b')
  317. ax.scatter(offset+2, p_resp, color='r')
  320. fig,ax = plt.subplots()
  321. plot_exp_resp(exc_df.get_group(('conc', 'basal')), '16', 0, ax)
  322. plot_exp_resp(exc_df.get_group(('conc', 'apical')), '16', 1, ax)
  323. plot_exp_resp(exc_df.get_group(('conc', 'basal')), '64', 2, ax)
  324. plot_exp_resp(exc_df.get_group(('conc', 'apical')), '64', 3, ax)
  325. ax.set_ylim([0,0.08])
  326. ax.set_xticks([1,5,9,13])
  327. ax.set_xticklabels(['basal 16', 'apical 16', 'basal 64', 'apical 64'])
  328. ax.set_ylabel('Area under cross-correlation peak')
  329. ax.set_title('Modulation of NMDA/AP response by phase of inhibitory \n input and location of excitatory input')
  330. fig.tight_layout()

Fig10.ipynb at commit 0476c95, under MIT · at the source

Overview

Authors: Drew B Headley1, Benjamin Latimer2, Adin Aberbach2, Satish S Nair2
ORCID iDs: Drew B Headley, Satish S Nair
  1. Center for Molecular and Behavioral Neuroscience, Rutgers University – Newark Newark United States
  2. Electrical Engineering and Computer Science, University of Missouri Columbia United States
Institutions: Rutgers, The State University of New Jersey (United States); University of Missouri (United States)
Journal: eLife, volume 13, article RP95562
Dates: published online 13 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.95562 · PMID 41823989 · PMCID PMC12987649 · OpenAlex W4396722341
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: rat (organism)
Methods: Spectral & time-frequency, Single-unit activity, calcium imaging
Keywords: inhibitory rhythmic modulation, dendritic integration, interneuron subtype specificity, Rat
MeSH: Interneurons*, Models, Neurological*, Neural Inhibition*, Pyramidal Cells*, Action Potentials, Animals, Beta Rhythm, Dendrites, Gamma Rhythm, Nerve Net, Parvalbumins, Somatostatin (* major topic)
Journal subjects: Neuroscience
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institutes of Health (NINDS 5R01NS123396, MH122023); National Science Foundation (OAC-1730655)
Citations: cited by 1 paper (Europe PMC); 114 references in the paper

Abstract

Pyramidal neurons form dense recurrently connected networks with multiple types of inhibitory interneurons. A major differentiator between interneuron subtypes is whether they synapse onto perisomatic or dendritic regions. They can also engender local inhibitory rhythms, beta (12–35 Hz) and gamma (40–80 Hz). The interaction between the rhythmicity of inhibition and its spatial targeting on the neuron may determine how it regulates neuronal integration. Thus, we sought to understand how rhythmic perisomatic and distal dendritic inhibition impacted integration in a layer 5 pyramidal neuron model with realistic dendrites supporting Na+, NMDA, and Ca2+ spikes. We found that inhibition regulated the coupling between dendritic spikes and action potentials in a location and rhythm-dependent manner. Perisomatic inhibition principally regulated action potential generation, while distal dendritic inhibition regulated the incidence of dendritic spikes and their temporal coupling with action potentials. Perisomatic inhibition was most effective when provided at gamma frequencies, while distal dendritic inhibition functioned best at beta. Moreover, beta modulated responsiveness to distal inputs in a phase-dependent manner, while gamma did so for proximal inputs. These results may provide a functional interpretation for the reported association of soma-targeting parvalbumin-positive interneurons with gamma and dendrite-targeting somatostatin interneurons with beta.

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 29 matches between paragraphs and lines of code.

modeldb:2019883

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source: modeldb.science/2019883

dbheadley/InhibOnDendComp

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 0476c95784cf8a5dd6e7f6565e7de2dd3fe75a49, 12 March 2026
Languages: Python (39), Jupyter (36)
Size: 214 files, 75 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, environment (environment.yml), tests, 36 notebooks
Not found: CITATION.cff, continuous integration, documentation
Tools: NumPy (69 files), pandas (45 files), Matplotlib (35 files), SciPy (16 files), h5py (15 files), scikit-learn (3 files), statsmodels (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
77 files
At the source: github.com/dbheadley/InhibOnDendComp

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.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 75 scripts, each with its path and the digest of its content;
  • 29 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

Simulation code is deposited at ModelDB at https://modeldb.science/2019883. The raw simulation data are available from Dryad at https://doi.org/10.5061/dryad.v6wwpzhb8. Analysis code is posted as a GitHub repo at https://github.com/dbheadley/InhibOnDendComp (copy archived at Headley, 2025).

The following dataset was generated:

DrewH BenL SatishN 2026Data from: Spatially targeted inhibitory rhythms differentially affect neuronal integrationDryad Digital Repository10.5061/dryad.v6wwpzhb8PMC1298764941823989

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 4 keywords, 12 MeSH terms, 2 funders, 113 references.

Cite

This paper

Headley, D. B., Latimer, B., Aberbach, A., & Nair, S. S. (2026). Spatially targeted inhibitory rhythms differentially affect neuronal integration. eLife, 13, RP95562. https://doi.org/10.7554/elife.95562

BibTeX

@article{headley2026spatially,
author = {Headley, Drew B and Latimer, Benjamin and Aberbach, Adin and Nair, Satish S},
title = {{Spatially targeted inhibitory rhythms differentially affect neuronal integration}},
journal = {eLife},
year = {2026},
month = mar,
volume = {13},
pages = {RP95562},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.95562},
url = {https://doi.org/10.7554/elife.95562},
pmid = {41823989},
pmcid = {PMC12987649}
}

RIS

TY - JOUR
AU - Headley, Drew B
AU - Latimer, Benjamin
AU - Aberbach, Adin
AU - Nair, Satish S
TI - Spatially targeted inhibitory rhythms differentially affect neuronal integration
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/03/13
VL - 13
SP - RP95562
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.95562
UR - https://doi.org/10.7554/elife.95562
LA - en
ER -

CSL-JSON

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