OSCR

Spike Generation in Electroreceptor Afferents Introduces Additional Spectral Response Components by Weakly Nonlinear Interactions.

Code ↔ Paper

12 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 12 matches
  1. [1] § Materials and Methods › Stimulation ↔ profiles/spectral.py, lines 21–72 · score 0.85 · Fourier domain, cutoff frequency, white noise stimuli, band limited, standard deviation, inverse
  2. [2] § Materials and Methods › LIF models for P-units ↔ profiles/spectral.py, lines 21–72 · score 0.75 · Fourier domain, band limited white, standard deviation, noise stimulus, inverse
  3. [3] § Materials and Methods › Data analysis › Spectral analysis ↔ profiles/spectral.py, lines 160–283 · score 0.69 · power spectral density, Fourier transforms, FFT segments, spectrum, stimulus
  4. [4] § Materials and Methods › Data analysis › Spectral analysis ↔ profiles/spectral.py, lines 103–157 · score 0.66 · cross spectrum, transfer function, spike trains, power spectrum, Spectral, susceptibility
  5. [5] § Materials and Methods › Data analysis › White-noise analysis ↔ profiles/spectral.py, lines 75–100 · score 0.65 · Gaussian kernels, spike train, standard deviation, convolved, firing rate
  6. [6] § Materials and Methods › Identification of P-units and ampullary cells ↔ profiles/baseline.py, lines 18–56 · score 0.63 · interspike intervals, baseline activity, baseline firing rate, variation, coefficient, ISIs
  7. [7] § Materials and Methods › Data analysis › White-noise analysis ↔ profiles/baseline.py, lines 18–56 · score 0.62 · Gaussian kernels, standard deviation, baseline firing rate, variation, activity, spike
  8. [8] § Materials and Methods › LIF models for P-units ↔ profiles/punitprofiles.py, lines 198–249 · score 0.58 · serial correlation, vector strength, lag, ISIs, CV, EOD
  9. [9] § Materials and Methods › Identification of P-units and ampullary cells ↔ profiles/punitprofiles.py, lines 198–249 · score 0.57 · serial correlation, vector strengths, phase, ISIs, CV, EOD
  10. [10] § Materials and Methods › Data analysis › Spectral analysis ↔ profiles/spectral.py, lines 160–283 · score 0.56 · Fourier transform, binary, spectral, resolution, sum, FFTs
  11. [11] § Results › Ampullary afferents exhibit strong nonlinear interactions ↔ profiles/spectral.py, lines 1–18 · score 0.55 · band limited white, power spectrum, diagonals, firing rate, peaks, noise
  12. [12] § Results › Model-based estimation of the second-order susceptibility ↔ profiles/punitprofiles.py, lines 442–494 · score 0.52 · temporal resolution, fourier transform, spectra, model, stimulus

Paper

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

The paper is loaded when this pane is shown.

The authors' code

Python · 430 lines · 14 KB · GPL-3.0 · 7 matches

  1. """
  2. Spectral analysis of neuronal responses.
  4. ## Functions
  6. - `whitenoise()`: band-limited white noise.
  7. - `rate()`: firing rate computed by kernel convolution.
  8. - `spectra()`: stimulus- and response power spectra, and cross spectrum.
  9. - `susceptibilities()`: stimulus- and response spectra up to second order.
  10. - `diag_projection()`: projection of the chi2 matrix onto its diagonal.
  11. - `hor_projection()`: horizontal projection of the chi2 matrix.
  12. - `peak_size()`: normalized and relative size of a peak expected around a specific frequency.
  14. """
  16. import numpy as np
  17. from scipy.signal import welch, csd
  18. from scipy.stats import norm
  21. def whitenoise(cflow, cfup, dt, duration, rng=np.random.default_rng()):
  22. """Band-limited white noise.
  24. Generates white noise with a flat power spectrum between `cflow` and
  25. `cfup` Hertz, zero mean and unit standard deviation. Note, that in
  26. particular for short segments of the generated noise the mean and
  27. standard deviation of the returned noise can deviate from zero and
  28. one.
  30. Parameters
  31. ----------
  32. cflow: float
  33. Lower cutoff frequency in Hertz.
  34. cfup: float
  35. Upper cutoff frequency in Hertz.
  36. dt: float
  37. Time step of the resulting array in seconds.
  38. duration: float
  39. Total duration of the resulting array in seconds.
  41. Returns
  42. -------
  43. noise: 1-D array
  44. White noise.
  45. """
  46. # number of elements needed for the noise stimulus:
  47. n = int(np.ceil((duration+0.5*dt)/dt))
  48. # next power of two:
  49. nn = int(2**(np.ceil(np.log2(n))))
  50. # indices of frequencies with `cflow` and `cfup`:
  51. inx0 = int(np.round(dt*nn*cflow))
  52. inx1 = int(np.round(dt*nn*cfup))
  53. if inx0 < 0:
  54. inx0 = 0
  55. if inx1 >= nn/2:
  56. inx1 = nn/2
  57. # draw random numbers in Fourier domain:
  58. whitef = np.zeros((nn//2+1), dtype=complex)
  59. # zero and nyquist frequency must be real:
  60. if inx0 == 0:
  61. whitef[0] = 0
  62. inx0 = 1
  63. if inx1 >= nn//2:
  64. whitef[nn//2] = 1
  65. inx1 = nn//2-1
  66. phases = 2*np.pi*rng.random(size=inx1 - inx0 + 1)
  67. whitef[inx0:inx1+1] = np.cos(phases) + 1j*np.sin(phases)
  68. # inverse FFT:
  69. noise = np.real(np.fft.irfft(whitef))
  70. # scaling factor to ensure standard deviation of one:
  71. sigma = nn / np.sqrt(2*float(inx1 - inx0))
  72. return noise[:n]*sigma
  75. def rate(time, spikes, sigma):
  76. """ Firing rate computed by kernel convolution.
  78. Parameters
  79. ----------
  80. time: ndarray of float
  81. Times at which firing rate is evaluated.
  82. spikes: list of ndarray of float
  83. Spike times.
  84. sigma: float
  85. Width of the Gaussian kernel as a standard deviation.
  87. Returns
  88. -------
  89. rate: ndarray of float
  90. Firing rate of convolved spike trains averaged over trials.
  91. ratesd: ndarray of float
  92. Corresponding standard deviation.
  93. """
  94. kernel = norm.pdf(time[time < 8*sigma], loc=4*sigma, scale=sigma)
  95. rates = np.zeros((len(spikes), len(time)))
  96. xtime = np.append(time, time[-1] + time[1] - time[0])
  97. for i, spiket in enumerate(spikes):
  98. b, _ = np.histogram(spiket, xtime)
  99. rates[i] = np.convolve(b, kernel, 'same')
  100. return np.mean(rates, 0), np.std(rates, 0)
  103. def spectra(stimulus, spikes, dt, nfft):
  104. """Stimulus- and response power spectra, and cross spectrum.
  106. Compute the complex-valued transfer function (first-order
  107. susceptibility) and the stimulus-response coherence like this:
  109. ```
  110. freqs, pss, prr, prs = spectra(stimulus, spikes, dt, nfft)
  111. transfer = prs/pss
  112. coherence = np.abs(prs)**2/pss/prr
  113. ```
  115. The gain of the transfer function is the absolute value of the
  116. transfer function:
  118. ```
  119. gain = np.abs(prs)/pss
  120. ```
  122. Parameters
  123. ----------
  124. stimulus: ndarray of float
  125. Stimulus waveform with sampling interval 'dt'.
  126. spikes: list of ndarrays of float
  127. Spike times in response to the stimulus.
  128. dt: float
  129. Sampling interval of stimulus and resolution of the binary spike train.
  130. nfft: int
  131. Number of samples used for each Fourier transformation.
  133. Returns
  134. -------
  135. freqs: ndarray of float
  136. The frequencies corresponding to the spectra.
  137. pss: ndarray of float
  138. Power spectrum of the stimulus.
  139. prr: ndarray of float
  140. Power spectrum of the response averaged over trials.
  141. prs: ndarray of complex
  142. Cross spectrum between stimulus and response averaged over trials.
  144. """
  145. time = np.arange(len(stimulus))*dt
  146. freq, pss = welch(stimulus, fs=1/dt, nperseg=nfft, noverlap=nfft//2)
  147. prr = np.zeros((len(spikes), len(freq)))
  148. prs = np.zeros((len(spikes), len(freq)), dtype=complex)
  149. for i, spiket in enumerate(spikes):
  150. b, _ = np.histogram(spiket, time)
  151. b = b / dt
  152. f, rr = welch(b - np.mean(b), fs=1/dt, nperseg=nfft, noverlap=nfft//2)
  153. f, rs = csd(b - np.mean(b), stimulus,
  154. fs=1/dt, nperseg=nfft, noverlap=nfft//2)
  155. prr[i] = rr
  156. prs[i] = rs
  157. return freq, pss, np.mean(prr, 0), np.mean(prs, 0)
  160. def susceptibilities(stimulus, spikes, dt=0.0005, nfft=2**9, nmax=0):
  161. """Stimulus- and response spectra up to second order.
  163. Compute the complex-valued transfer function (first-order
  164. susceptibility) and the stimulus-response coherence like this:
  166. ```
  167. freqs, pss, prr, prs, prss, n = susceptibilities(stimulus, spikes, dt, nfft)
  168. transfer = prs/pss
  169. coherence = np.abs(prs)**2/pss/prr
  170. ```
  172. The gain of the transfer function is the absolute value of the
  173. transfer function and has the unit Hz/[s]:
  175. ```
  176. gain = np.abs(prs)/pss
  177. ```
  179. The complex-valued second-order susceptibility has the unit
  180. Hz/[ss] and can be computed like this:
  182. ```
  183. chi2 = prss*0.5/(pss.reshape(1, -1)*pss.reshape(-1, 1))
  184. ```
  186. The variance of the stimulus is the integral over the stimulus
  187. power spectral density `pss` (unit [s]^2/Hz):
  188. ```
  189. deltaf = freqs[1] - freqs[0] # same as 1/(dt*nfft)
  190. vars = np.sum(pss)*deltaf
  191. ```
  192. Likewise for the response.
  194. The response spectral density `prr` (in Hz^2/Hz = Hz) approaches
  195. the firing rate for large frequencies.
  197. Parameters
  198. ----------
  199. stimulus: ndarray of float
  200. Stimulus waveform with sampling interval 'dt'.
  201. spikes: list of ndarrays of float
  202. Spike times in response to the stimulus.
  203. dt: float
  204. Sampling interval of stimulus and resolution of the binary spike train.
  205. nfft: int
  206. Number of samples used for each Fourier transformation.
  207. nmax: int
  208. Maximum number of FFT segments to be used. If 0, use all segments.
  210. Returns
  211. -------
  212. freqs: ndarray of float
  213. The frequencies corresponding to the spectra.
  214. pss: ndarray of float
  215. Power spectral density of the stimulus in unit [s]^2/Hz.
  216. prr: ndarray of float
  217. Power spectral density of the response averaged over segments
  218. in unit Hz^2/Hz = Hz.
  219. prs: ndarray of complex
  220. Cross spectrum between stimulus and response averaged over segments
  221. in unit Hz[s]/Hz = [s].
  222. prss: ndarray of complex
  223. Cross bispectrum between stimulus and response averaged
  224. over segments in unit [s]^2/Hz.
  225. n: int
  226. Number of FFT segments used.
  228. """
  229. freqs = np.fft.fftfreq(nfft, dt)
  230. freqs = np.fft.fftshift(freqs)
  231. f0 = np.argmin(np.abs(freqs)) # index of zero frequency
  232. fidx = np.arange(len(freqs))
  233. fsum_idx = fidx.reshape(-1, 1) + fidx.reshape(1, -1) - f0
  234. fsum_idx[fsum_idx < 0] = 0
  235. fsum_idx[fsum_idx >= len(fidx)] = len(fidx) - 1
  236. f0 = len(freqs)//4
  237. f1 = 3*len(freqs)//4
  238. segments = range(0, len(stimulus) - nfft, nfft)
  239. # stimulus:
  240. p_ss = np.zeros(len(freqs))
  241. fourier_s = np.zeros((len(segments), len(freqs)), complex)
  242. n = 0
  243. for j, k in enumerate(segments):
  244. fourier_s[j] = np.fft.fft(stimulus[k:k + nfft], n=nfft)
  245. fourier_s[j] = np.fft.fftshift(fourier_s[j])
  246. p_ss += np.abs(fourier_s[j]*np.conj(fourier_s[j]))
  247. n += 1
  248. if nmax > 0 and n >= nmax:
  249. break
  250. scale = dt/nfft/n
  251. p_ss *= scale
  252. # response spectra:
  253. time = np.arange(len(stimulus))*dt
  254. p_rr = np.zeros(len(freqs))
  255. p_rs = np.zeros(len(freqs), complex)
  256. p_rss = np.zeros((len(freqs), len(freqs)), complex)
  257. n = 0
  258. for i, spiket in enumerate(spikes):
  259. b, _ = np.histogram(spiket, time)
  260. b = b / dt
  261. for j, k in enumerate(segments):
  262. # stimulus:
  263. fourier_s1 = fourier_s[j].reshape(len(fourier_s[j]), 1)
  264. fourier_s2 = fourier_s[j].reshape(1, len(fourier_s[j]))
  265. fourier_s12 = np.conj(fourier_s1)*np.conj(fourier_s2)
  266. # response:
  267. fourier_r = np.fft.fft(b[k:k + nfft] - np.mean(b), n=nfft)
  268. fourier_r = np.fft.fftshift(fourier_r)
  269. p_rr += np.abs(fourier_r*np.conj(fourier_r))
  270. p_rs += np.conj(fourier_s[j])*fourier_r
  271. p_rss += fourier_s12*fourier_r[fsum_idx]
  272. n += 1
  273. if nmax > 0 and n >= nmax:
  274. break
  275. if nmax > 0 and n >= nmax:
  276. break
  277. scale = dt/nfft/n
  278. freqs = freqs[f0:f1]
  279. p_ss = p_ss[f0:f1]
  280. p_rr = p_rr[f0:f1]*scale
  281. p_rs = p_rs[f0:f1]*scale
  282. p_rss = p_rss[f0:f1, f0:f1]*dt*scale
  283. return freqs, p_ss, p_rr, p_rs, p_rss, n
  286. def diag_projection(freqs, chi2, fmax):
  287. """ Projection of the chi2 matrix onto its diagonal.
  289. Adapted from https://stackoverflow.com/questions/71362928/average-values-over-all-offset-diagonals
  291. Parameters
  292. ----------
  293. freqs: ndarray of float
  294. Frequencies of the chi2 matrix.
  295. chi2: 2-D ndarray of float
  296. Second-order susceptibility matrix.
  297. fmax: float
  298. Maximum frequency for the projection.
  300. Returns
  301. -------
  302. dfreqs: ndarray of float
  303. Frequencies of the projection.
  304. diagp: ndarray of float
  305. Projections of the chi2 matrix onto its diagonal.
  306. That is, averages over the anti-diagonals.
  307. """
  308. i0 = np.argmin(freqs < 0)
  309. i1 = np.argmax(freqs > fmax)
  310. if i1 == 0:
  311. i1 = len(freqs)
  312. chi2 = chi2[i0:i1, i0:i1]
  313. n = chi2.shape[0]
  314. diagp = np.zeros(n*2-1, dtype=float)
  315. for i in range(n):
  316. diagp[i:i + n] += chi2[i]
  317. diagp[0:n] /= np.arange(1, n+1, 1, dtype=float)
  318. diagp[n:] /= np.arange(n-1, 0, -1, dtype=float)
  319. dfreqs = np.arange(len(diagp))*(freqs[i0 + 1] - freqs[i0]) + freqs[i0]
  320. return dfreqs, diagp
  323. def hor_projection(freqs, chi2, fmax):
  324. """ Horizontal projection of the chi2 matrix.
  326. Parameters
  327. ----------
  328. freqs: ndarray of float
  329. Frequencies of the chi2 matrix.
  330. chi2: 2-D ndarray of float
  331. Second-order susceptibility matrix.
  332. fmax: float
  333. Maximum frequency for the projection.
  335. Returns
  336. -------
  337. hfreqs: ndarray of float
  338. Frequencies of the projection.
  339. horp: ndarray of float
  340. Projections of the chi2 matrix onto its x-axis.
  341. That is, averages over columns.
  342. """
  343. i0 = np.argmin(freqs < 0)
  344. i1 = np.argmax(freqs > fmax)
  345. if i1 == 0:
  346. i1 = len(freqs)
  347. hfreqs = freqs[i0:i1]
  348. chi2 = chi2[i0:i1, i0:i1]
  349. horp = np.mean(chi2, 1)
  350. return hfreqs, horp
  353. def peak_size(freqs, spectrum, ftarget, median=True,
  354. searchwin=50, distance=10, averagewin=10):
  355. """Normalized and relative size of a peak expected around a specific frequency.
  357. The peak is searched within `searchwin` Hz around the target
  358. frequency. As a baseline amplitude of the spectrum either the
  359. averaged values of the spectrum within `averagewin` Hz at a
  360. distance of `distance` Hz to the left and right of the found
  361. peak frequency (`median` is `False`, default), or the median of
  362. the whole spectrum is taken (`median` is `True`).
  364. Parameters
  365. ----------
  366. freqs: ndarray of float
  367. Frequencies of the spectrum.
  368. spectrum: ndarray of float
  369. Some spectrum. Or a projection of the chi2 matrix.
  370. ftarget: float
  371. The frequency where a peak is expected in the spectrum.
  372. median: bool
  373. If True, normalize the peak height by the median of the spectrum.
  374. Otherwise (default), normalize by averaged values of the spectrum
  375. close to the `peak frequency.
  376. searchwin: float
  377. Search for the largest peak in the spectrum at `ftarget` plus and
  378. minus `searchwin` Hertz.
  379. distance: float
  380. The windows for estimating the baseline around the peak start
  381. `distance` Hertz to the left and right of the found peak.
  382. averagewin: float
  383. For estimating the baseline around the peak, an average is taken in two
  384. `averagewin` Hertz wide windows that are located `distance`
  385. Hertz to the left and right of the detected peak.
  387. Returns
  388. -------
  389. peak_norm: float
  390. The height of the peak close to `ftarget` normalized to the
  391. baseline around the peak.
  392. peak_rel: float
  393. The height of the peak close to `ftarget` relative to the
  394. baseline around the peak.
  395. peak_freq: float
  396. The frequency of the detected peak.
  398. """
  399. mask = (freqs > ftarget - searchwin) & (freqs < ftarget + searchwin)
  400. snippet = spectrum[mask]
  401. if len(snippet) == 0:
  402. return np.nan, np.nan, np.nan
  403. peak = np.max(snippet)
  404. fpeak = freqs[np.argmax(snippet) + np.argmax(mask)]
  405. bleft = np.nan
  406. bright = np.nan
  407. baseline = np.nan
  408. if median:
  409. baseline = np.median(spectrum)
  410. else:
  411. mask = (freqs >= fpeak - distance - averagewin) & \
  412. (freqs <= fpeak - distance)
  413. bleft = np.mean(spectrum[mask]) if np.sum(mask) > 0 else np.nan
  414. mask = (freqs >= fpeak + distance) & \
  415. (freqs <= fpeak + distance + averagewin)
  416. bright = np.mean(spectrum[mask]) if np.sum(mask) > 0 else np.nan
  417. if np.isfinite(bleft) and np.isfinite(bright):
  418. baseline = 0.5*(bleft + bright)
  419. elif np.isfinite(bleft):
  420. baseline = bleft
  421. elif np.isfinite(bright):
  422. baseline = bright
  423. else:
  424. baseline = np.nan
  425. if np.isnan(baseline):
  426. return np.nan, np.nan, np.nan
  427. peak_norm = peak/baseline
  428. peak_rel = peak - baseline
  429. return peak_norm, peak_rel, fpeak

spectral.py at commit 08b9dbd, under GPL-3.0 · at the source

Overview

Authors: Alexandra Barayeu1, Maria Schlungbaum2,3, Benjamin Lindner2,3, Jan Grewe1, Jan Benda1,4
ORCID iDs: Alexandra Barayeu, Maria Schlungbaum, Benjamin Lindner, Jan Grewe, Jan Benda
  1. Institute for Neurobiology, Eberhard Karls Universität Tübingen, Tübingen 72076, Germany
  2. Bernstein Center for Computational Neuroscience Berlin, Berlin 10115, Germany
  3. Department of Physics, Humboldt University Berlin, Berlin 12489, Germany
  4. Bernstein Center for Computational Neuroscience Tübingen, Tübingen 72076, Germany
Institutions: University of Tübingen (Germany); Humboldt-Universität zu Berlin (Germany); Bernstein Center for Computational Neuroscience Berlin (Germany); Bernstein Center for Computational Neuroscience Tübingen (Germany)
Journal: eNeuro, volume 13, issue 5, pages ENEURO.0357-25.2026
Dates: received 24 September 2025; accepted 12 March 2026; published online 30 April 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/eneuro.0357-25.2026 · PMID 41922166 · PMCID PMC13150474 · OpenAlex W7147553139
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Single-unit activity, calcium imaging
Keywords: electric fish, nonlinear coding, second-order susceptibility, Volterra series
MeSH: Action Potentials*, Electric Organ*, Models, Neurological*, Neurons, Afferent*, Nonlinear Dynamics*, Sensory Receptor Cells*, Animals, Electric Fish, Female, Male (* major topic)
Journal subjects: Research Article: New Research, Neuronal Excitability
Topic: Fish biology, ecology, and behavior (Nature and Landscape Conservation, Environmental Science), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (430157666)
Citations: not cited yet (Europe PMC); 81 references in the paper

Abstract

Spiking thresholds in neurons or rectification at synapses are essential for neuronal computations rendering neuronal processing inherently nonlinear. Nevertheless, linear response theory has been instrumental for understanding, for example, the impact of noise or neuronal synchrony on signal transmission, or the emergence of oscillatory activity, but is valid only at low stimulus amplitudes or large levels of intrinsic noise. At higher signal-to-noise ratios, however, nonlinear response components become relevant. Theoretical results for leaky integrate-and-fire neurons in the weakly nonlinear regime suggest strong responses at the sum of two input frequencies if one of these frequencies or their sum matches the neuron’s baseline firing rate. We here analyze nonlinear responses in two types of primary electroreceptor afferents, the P-units of the active and the ampullary cells of the passive electrosensory system of the wave-type electric fish Apteronotus leptorhynchus of either sex. In our combined experimental and modeling approach, we identify these predicted nonlinear responses in those 31 out of 172 P-units that are characterized by low intrinsic noise. In contrast, the majority (22 out of 30) ampullary cells show nonlinear responses. Our results provide experimental evidence for nonlinear responses of spike generators in the weakly nonlinear regime. We conclude that such nonlinear responses occur in any sensory neuron that operates in similar regimes, particularly at near-threshold stimulus conditions.

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 12 matches between paragraphs and lines of code.

bendalab/punitmodel

License: GPL-3.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 08b9dbdc12f0cac6bb6cd1b6af84d51c7e333667, 24 September 2025
Languages: Python (9)
Size: 245 files, 9 scripts
Software Heritage: not archived
Found in: the text, “LIF models for P-units”
Holds: README, license file, documentation
Not found: CITATION.cff, environment file, tests, continuous integration
Tools: NumPy (9 files), Matplotlib (4 files), SciPy (4 files), pandas (2 files), Numba (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
11 files
At the source: github.com/bendalab/punitmodel/tree/v1

doi:10.12751/g-node.4w8ebq

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Code and data accessibility”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.12751/g-node.4w8ebq

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;
  • 9 scripts, each with its path and the digest of its content;
  • 12 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.

Code and data accessibility

The P-unit model parameters and spectral analysis algorithms are available at https://github.com/bendalab/punitmodel/tree/v1. The electrophysiological data of all the P-units and Ampullary cells analyzed here, the analysis scripts, and the scripts simulating the P-unit model responses are accessible at https://doi.org/10.12751/g-node.4w8ebq.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 4 keywords, 10 MeSH terms, 1 funder, 73 references.

Cite

This paper

Barayeu, A., Schlungbaum, M., Lindner, B., Grewe, J., & Benda, J. (2026). Spike Generation in Electroreceptor Afferents Introduces Additional Spectral Response Components by Weakly Nonlinear Interactions. eNeuro, 13(5), ENEURO.0357-25.2026. https://doi.org/10.1523/eneuro.0357-25.2026

BibTeX

@article{barayeu2026spike,
author = {Barayeu, Alexandra and Schlungbaum, Maria and Lindner, Benjamin and Grewe, Jan and Benda, Jan},
title = {{Spike Generation in Electroreceptor Afferents Introduces Additional Spectral Response Components by Weakly Nonlinear Interactions}},
journal = {eNeuro},
year = {2026},
month = may,
volume = {13},
number = {5},
pages = {ENEURO.0357--25.2026},
publisher = {Society for Neuroscience},
issn = {2373-2822},
doi = {10.1523/eneuro.0357-25.2026},
url = {https://doi.org/10.1523/eneuro.0357-25.2026},
pmid = {41922166},
pmcid = {PMC13150474}
}

RIS

TY - JOUR
AU - Barayeu, Alexandra
AU - Schlungbaum, Maria
AU - Lindner, Benjamin
AU - Grewe, Jan
AU - Benda, Jan
TI - Spike Generation in Electroreceptor Afferents Introduces Additional Spectral Response Components by Weakly Nonlinear Interactions
T2 - eNeuro
J2 - eNeuro
PY - 2026
DA - 2026/05/04
VL - 13
IS - 5
SP - ENEURO.0357
EP - 25.2026
SN - 2373-2822
PB - Society for Neuroscience
DO - 10.1523/eneuro.0357-25.2026
UR - https://doi.org/10.1523/eneuro.0357-25.2026
LA - en
ER -

CSL-JSON

{
"id": "10.1523/eneuro.0357-25.2026",
"type": "article-journal",
"title": "Spike Generation in Electroreceptor Afferents Introduces Additional Spectral Response Components by Weakly Nonlinear Interactions",
"container-title": "eNeuro",
"author": [
{
"family": "Barayeu",
"given": "Alexandra"
},
{
"family": "Schlungbaum",
"given": "Maria"
},
{
"family": "Lindner",
"given": "Benjamin"
},
{
"family": "Grewe",
"given": "Jan"
},
{
"family": "Benda",
"given": "Jan"
}
],
"container-title-short": "eNeuro",
"volume": "13",
"issue": "5",
"page": "ENEURO.0357-25.2026",
"DOI": "10.1523/eneuro.0357-25.2026",
"PMID": "41922166",
"PMCID": "PMC13150474",
"ISSN": "2373-2822",
"publisher": "Society for Neuroscience",
"URL": "https://doi.org/10.1523/eneuro.0357-25.2026",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
4
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1007/s00422-026-01063-3 [code]
Increased firing rates monotonically expand neuronal coding bandwidth.
Journal: Biological cybernetics
In common: Numba, pandas, SciPy, 2 other tools, 15 references, 3 authors
[2] doi:10.1371/journal.pone.0348018
Spatial coding of conspecifics in the electrosensory system: Early segregation of information streams.
Journal: PloS one
In common: 6 references
[3] doi:10.1371/journal.pbio.3003856 [code]
Aging and metabolism contribute separately to brain-body health.
Journal: PLoS biology
In common: pandas, SciPy, Matplotlib, 1 other tool, 3 references
[4] doi:10.1007/s00422-026-01043-7
Fluctuation-response relations for a two-stage population of spiking neurons stimulated by common noise.
Journal: Biological cybernetics
In common: 4 references
[5] doi:10.1126/sciadv.aed4172 [code]
Locomotion optimizes sensory representations through a computational principle shared by rodents and primates.
Journal: Science advances
In common: Numba, pandas, SciPy, 2 other tools, cellular / molecular, 1 reference
[6] doi:10.1371/journal.pcbi.1014157 [code]
Modeling spatial contrast sensitivity in responses of primate retinal ganglion cells to natural movies.
Journal: PLoS computational biology
In common: Numba, pandas, SciPy, 1 other tool, 2 references
[7] doi:10.1523/eneuro.0060-26.2026 [code]
Spatial Adaptation of Primate Retinal Ganglion Cells Between Artificial and Natural Stimuli.
Journal: eNeuro
In common: pandas, SciPy, Matplotlib, 1 other tool, 2 references
[8] doi:10.1126/sciadv.aed6417 [code]
Intrinsic timing, not temporal prediction, underlies ramping dynamics in visual and parietal cortex during passive behavior.
Journal: Science advances
In common: pandas, SciPy, Matplotlib, 1 other tool, 2 references
[9] doi:10.1371/journal.pcbi.1014532 [code]
High reelin expression may explain why a subgroup of entorhinal cortex neurons functions as an initial nucleation site of Alzheimer's disease.
Journal: PLoS computational biology
In common: Numba, pandas, SciPy, 2 other tools, 1 reference
[10] doi:10.1371/journal.pcbi.1014571 [code]
SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification.
Journal: PLoS computational biology
In common: Numba, pandas, SciPy, 2 other tools, 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.