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A biophysically grounded model of glutamatergic synaptic transmission integrating glutamate transport, receptor kinetics, and electrotonic effects.

Code ↔ Paper

3 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 3 matches
  1. [1] § Methods › Mathematical model › Glutamate transport ↔ Pars_and_Functions10_numba.py, lines 248–297 · score 0.55 · EAAT uptake, pulse, diffusion, duration, cleft, EAAT2
  2. [2] § Methods › Numerical implementation ↔ EAAT_main10.py, lines 816–860 · score 0.52 · Nelder Mead, error, minimizing, fitting
  3. [3] § Methods › Numerical implementation ↔ EAAT_main11.py, lines 832–876 · score 0.52 · Nelder Mead, error, minimizing, fitting

Paper

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The authors' code

Python · 434 lines · 16 KB · no license · 1 match

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  3. from numba import njit
  5. def LoadExpData():
  6. # control AMPA current
  7. I_Exp_AMPA_con = []
  8. t_Exp_AMPA_con = []
  9. with open('control_copy.atf', 'r') as aaa:
  10. n = 0
  11. while True:
  12. line = aaa.readline()
  13. if not line: # End of file
  14. break
  15. ts, I_contr = map(float, line.split()) # Assuming the file contains two floating-point numbers per line
  16. I_Exp_AMPA_con.append(I_contr * 1000)
  17. t_Exp_AMPA_con.append(ts * 1000 - 200) # ms
  18. n += 1
  20. # TBOA AMPA current
  21. I_Exp_AMPA_TBOA = []
  22. t_Exp_AMPA_TBOA = []
  23. with open('tboa_70_nM.atf', 'r') as aaa:
  24. n = 0
  25. while True:
  26. line = aaa.readline()
  27. if not line: # End of file
  28. break
  29. ts, I_contr = map(float, line.split())
  30. I_Exp_AMPA_TBOA.append(I_contr * 1000)
  31. t_Exp_AMPA_TBOA.append(ts * 1000 - 200) # ms
  32. n += 1
  34. # TBOA, NMDA current, +40 and -40
  35. t_Exp_NMDA_TBOA = []
  36. I_Exp_NMDA_TBOA_plus40 = []
  37. I_Exp_NMDA_TBOA_min40 = []
  38. with open('2019-12-18_001Group1Series19.atf', 'r') as aaa:
  39. for i in range(10): # skip 10 lines of a header
  40. aaa.readline()
  41. while True:
  42. line = aaa.readline()
  43. if not line: # End of file
  44. break
  45. ts, I_min40, dum1, dum2, I_plus40, dum3, dum4 = map(float, line.split())
  46. t_Exp_NMDA_TBOA.append(ts * 1000 - 1000) # ms
  47. I_Exp_NMDA_TBOA_plus40.append(I_plus40 * 1000)
  48. I_Exp_NMDA_TBOA_min40.append(I_min40 * 1000 + 270)
  50. # con, NMDA current, +40 and -40
  51. t_Exp_NMDA = []
  52. I_Exp_NMDA_plus40 = []
  53. I_Exp_NMDA_min40 = []
  54. with open('2019-12-02_001Group1Series04.atf', 'r') as aaa:
  55. for i in range(10): # skip 10 lines of a header
  56. aaa.readline()
  57. while True:
  58. line = aaa.readline()
  59. if not line: # End of file
  60. break
  61. ts, I_min40, dum1, dum2, I_plus40, dum3, dum4 = map(float, line.split())
  62. t_Exp_NMDA.append(ts * 1000 - 1000) # ms
  63. I_Exp_NMDA_plus40.append(I_plus40 * 1000 - 300)
  64. I_Exp_NMDA_min40.append(I_min40 * 1000 + 80)
  66. # control CC
  67. V_Exp_CC = []
  68. t_Exp_CC = []
  69. with open('2018-11-16_001Group1Series8_CC_av.atf', 'r') as aaa:
  70. for i in range(10): # skip 10 lines of a header
  71. aaa.readline()
  72. while True:
  73. line = aaa.readline()
  74. if not line: # End of file
  75. break
  76. ts, V_contr, dum1, dum2 = map(float, line.split())
  77. t0 = 0.152 # s start of response
  78. if ts >= t0:
  79. V_Exp_CC.append(V_contr - 6.3)
  80. t_Exp_CC.append((ts - t0) * 1000) # ms
  81. t_Exp_CC = np.array(t_Exp_CC)
  82. V_Exp_CC = np.array(V_Exp_CC)
  84. # control VC
  85. I_Exp_VC = []
  86. t_Exp_VC = []
  87. with open('2018-11-16_001Group1Series9_VC_av.atf', 'r') as aaa:
  88. for i in range(10): # skip 10 lines of a header
  89. aaa.readline()
  90. while True:
  91. line = aaa.readline()
  92. if not line: # End of file
  93. break
  94. ts, I_contr, dum1, dum2 = map(float, line.split())
  95. t0 = 0.152 # s start of response
  96. if ts >= t0:
  97. I_Exp_VC.append(I_contr * 1000 + 160)
  98. t_Exp_VC.append((ts - t0) * 1000) # ms
  99. t_Exp_VC = np.array(t_Exp_VC)
  100. I_Exp_VC = np.array(I_Exp_VC)
  101. I_Exp_VC = Filter_Array(I_Exp_VC, gam=0.2)
  103. return t_Exp_AMPA_con, I_Exp_AMPA_con, \
  104. t_Exp_AMPA_TBOA, I_Exp_AMPA_TBOA, \
  105. t_Exp_CC, V_Exp_CC, t_Exp_VC, I_Exp_VC, \
  106. t_Exp_NMDA_TBOA, I_Exp_NMDA_TBOA_plus40, I_Exp_NMDA_TBOA_min40, \
  107. t_Exp_NMDA, I_Exp_NMDA_plus40, I_Exp_NMDA_min40
  110. # ---------------------------------------------------------------------------
  111. # Stimulus functions
  112. # NOTE: These cannot be @njit because they are called from outside numba context
  113. # too, but Stim_5_njit below is the version used inside System_of_ODEs_njit.
  114. # ---------------------------------------------------------------------------
  116. def Stim(t, t0, duration):
  117. y = 0
  118. if (t >= t0) and (t <= t0 + duration):
  119. y = 1
  120. return y
  122. def Stim_5(t, t0, duration, ISI):
  123. y = 0
  124. iStim_ = int((t - t0) / ISI)
  125. if (iStim_ <= 4) and (t - t0 >= iStim_ * ISI) and (t - t0 <= iStim_ * ISI + duration):
  126. y = 1
  127. return y
  129. def Stim_5_100Hz(t, t0, duration):
  130. ISI_ = 10 # ms
  131. y = 0
  132. iStim_ = int((t - t0) / ISI_)
  133. if (iStim_ <= 4) and (t - t0 >= iStim_ * ISI_) and (t - t0 <= iStim_ * ISI_ + duration):
  134. y = 1
  135. return y
  138. # ---------------------------------------------------------------------------
  139. # numba-compatible version of the stimulus (no external calls, pure arithmetic)
  140. # ---------------------------------------------------------------------------
  141. @njit(cache=True)
  142. def Stim_5_njit(t, t0, duration, ISI):
  143. """Numba-compiled 5-pulse stimulus. Identical logic to Stim_5."""
  144. if ISI <= 0.0:
  145. return 0.0
  146. dt = t - t0
  147. if dt < 0.0:
  148. return 0.0
  149. iStim_ = int(dt / ISI)
  150. if iStim_ > 4:
  151. return 0.0
  152. t_in_pulse = dt - iStim_ * ISI
  153. if 0.0 <= t_in_pulse <= duration:
  154. return 1.0
  155. return 0.0
  158. # ---------------------------------------------------------------------------
  159. # Activation / transport functions — scalar versions for use inside @njit
  160. # ---------------------------------------------------------------------------
  162. @njit(cache=True)
  163. def S_AMPA_scalar(x):
  164. """Hill function for AMPA receptor sensitivity (eq. 11).
  165. EC50 = 1000 µM, Hill coefficient n = 1.6."""
  166. if x <= 0.0:
  167. return 0.0
  168. EC50 = 1000.0 # µM
  169. n = 1.6
  170. return 1.0 / (1.0 + (EC50 / x) ** n)
  173. @njit(cache=True)
  174. def S_NMDA_scalar(x):
  175. """Hill function for NMDA receptor sensitivity (eq. 11).
  176. EC50 = 4.7 µM, Hill coefficient n = 1."""
  177. if x <= 0.0:
  178. return 0.0
  179. EC50 = 4.7 # µM
  180. # n = 1 → 1/(1 + EC50/x)
  181. return 1.0 / (1.0 + EC50 / x)
  184. @njit(cache=True)
  185. def fEAAT2_scalar(glU, Na_e=130.0, Na_a=15.0):
  186. """EAAT2 transport function (eq. 14).
  187. k_half^Glu = 28 µM (same constant used in dF/dt and dD/dt)."""
  188. dNa = Na_e - Na_a
  189. k_ = dNa ** 1.5 / (50.0 ** 1.5 + dNa ** 1.5)
  190. return k_ * glU / (28.0 + glU)
  193. # ---------------------------------------------------------------------------
  194. # Array versions kept for post-processing / plotting (unchanged behaviour)
  195. # ---------------------------------------------------------------------------
  197. def S_AMPA(x):
  198. EC502 = 1000 # µM
  199. n22 = 1.6
  200. result = np.zeros_like(x)
  201. mask = x > 0
  202. result[mask] = 1.0 / (1.0 + (EC502 / x[mask]) ** n22)
  203. return result
  206. def S_NMDA(x):
  207. EC501 = 4.7 # µM
  208. n11 = 1
  209. result = np.zeros_like(x)
  210. mask = x > 0
  211. result[mask] = 1.0 / (1.0 + (EC501 / x[mask]) ** n11)
  212. return result
  215. def fEAAT2(glU, Na_e=130, Na_a=15):
  216. k_ = (Na_e - Na_a) ** 1.5 / (50 ** 1.5 + (Na_e - Na_a) ** 1.5)
  217. return k_ * glU / (28 + glU)
  220. def fNMDA(U, Mg=1):
  221. return 1.0 / (1.0 + (Mg / 3.57) * np.exp(-0.062 * U))
  224. # ---------------------------------------------------------------------------
  225. # Core ODE right-hand side — numba JIT compiled
  226. #
  227. # Paper equations implemented here:
  228. # eq.12 dC_glu/dt = (C_glu_periph - C_glu)/tau_glU + F*D*nu
  229. # eq.13 dC_glu_periph/dt = (C_glu - C_glu_periph)/(beta*tau_glU)
  230. # - C_glu_periph/tau_diff
  231. # - alpha_periph * f_EAAT2(C_glu_periph)
  232. # eq.15 dF/dt = (F0 - F)/tau_F + H_F*(1-F)*C_glu_periph/k_half
  233. # eq.16 dD/dt = (1-D)/tau_D - H_D*D*C_glu_periph/k_half
  234. # NOTE: sign of H_D term is negative (depression), matching eq.16
  235. # of the paper. The original code had the same sign convention
  236. # via H_D being defined as a positive number subtracted implicitly
  237. # through the "-" in the formula; here it is made explicit.
  238. # eq.9 d(mAMPA)/dt = (-mAMPA + S_AMPA(C_glu)) / tau_AMPA
  239. #
  240. # Consistency fix applied vs. original code:
  241. # * eq.13 second (sink) term: paper uses C_glu_periph/tau_diff,
  242. # original code used (glU0 - glU_perif)/tau_diff which adds a nonzero
  243. # baseline glU0. The numba version retains glU0 as a parameter to
  244. # preserve the original behaviour; a comment marks the discrepancy.
  245. # * k_half = 28 µM is hard-coded in the original; made explicit here.
  246. # ---------------------------------------------------------------------------
  248. @njit(cache=True)
  249. def System_of_ODEs_njit(t, y,
  250. t0, duration, amplitude, ISI,
  251. beta, tau_glU, tau_diff, glU0,
  252. alpha_periph,
  253. tau_F, H_F, F0,
  254. tau_AMPA,
  255. H_D, tau_D):
  256. """
  257. Numba-JIT right-hand side for the 5-ODE glutamate/plasticity system.
  259. State vector y = [C_glu, C_glu_periph, F, D, mAMPA]
  261. Parameters (all floats):
  262. t0, duration, amplitude, ISI – stimulus parameters
  263. beta – volume ratio (periph/cleft), eq.13
  264. tau_glU – cleft-to-periph diffusion time [ms], eqs.12,13
  265. tau_diff – periph-to-bath diffusion time [ms], eq.13
  266. glU0 – residual periph glutamate baseline [µM]
  267. (NOTE: paper eq.13 uses C_glu_periph/tau_diff,
  268. code uses (glU0 - C_glu_periph)/tau_diff;
  269. glU0=0 recovers the paper formula exactly)
  270. alpha_periph – EAAT uptake rate [µM/ms], eq.13
  271. tau_F, H_F, F0 – facilitation parameters, eq.15
  272. tau_AMPA – AMPA closing time [ms], eq.9
  273. H_D, tau_D – depression parameters, eq.16
  274. """
  275. C_glu = y[0]
  276. C_glu_periph = y[1]
  277. F = y[2]
  278. D = y[3]
  279. mAMPA = y[4]
  280. k_half = 28.0 # µM (half-max concentration for EAAT2 and plasticity scaling)
  281. # Stimulus pulse (eq.12, release term)
  282. nu_ = amplitude * Stim_5_njit(t, t0, duration, ISI)
  284. # eq.12: dC_glu/dt
  285. dglU_dt = (C_glu_periph - C_glu) / tau_glU + F * nu_ #* D # 12.03.2026
  286. # eq.13: dC_glu_periph/dt
  287. dglU_periph_dt = ((C_glu - C_glu_periph) / (tau_glU * beta)
  288. + (glU0 - C_glu_periph) / tau_diff
  289. - alpha_periph * fEAAT2_scalar(C_glu_periph))
  290. # eq.15: dF/dt
  291. dF_dt = -(F - F0) / tau_F + H_F * (1.0 - F) * C_glu_periph / k_half
  292. # eq.16: dD/dt
  293. dD_dt = -(D - 1.0) / tau_D - H_D * D * S_AMPA_scalar(C_glu) #* C_glu_periph / k_half # 12.03.2026
  294. # eq.9: d(mAMPA)/dt
  295. dmAMPA_dt = (-mAMPA + S_AMPA_scalar(C_glu) * D) / tau_AMPA # 12.03.2026
  297. return (dglU_dt, dglU_periph_dt, dF_dt, dD_dt, dmAMPA_dt)
  299. # #*******************************************************************************************************************
  300. # dglU_dt = (glU_perif - glU) / tau_glU + F_*nu_#*D #- EAAT *fEAAT2(glU, Na_e,Na_a)
  301. # dglU_perif_dt = (glU - glU_perif) / (tau_glU * beta) + (glU0 - glU_perif) / tau_diff - EAAT_perif*fEAAT2(glU_perif)
  302. # dF_dt = -(F-F0)/tau_F + H_F * (1-F) * glU_perif/28#muM
  303. # dD_dt = -(D- 1)/tau_D - H_D * D * S_AMPA(glU)
  304. # dmAMPA_dt = (- mAMPA + S_AMPA(glU) * D )/tau_AMPA
  305. # #*******************************************************************************************************************
  308. # ---------------------------------------------------------------------------
  309. # scipy-compatible wrapper (scipy passes (t, y) for solve_ivp, (y, t) for odeint)
  310. # Provide both flavours so the main script can choose freely.
  311. # ---------------------------------------------------------------------------
  313. def System_of_ODEs_ivp(t, y, t0, duration, amplitude, ISI, params):
  314. """Wrapper for scipy.integrate.solve_ivp (t, y) calling convention.
  315. Returns a list so it is drop-in compatible with the original function."""
  316. beta, tau_glU, tau_diff, glU0, alpha_periph, tau_F, H_F, F0, tau_AMPA, H_D, tau_D = params
  317. res = System_of_ODEs_njit(t, np.asarray(y, dtype=np.float64),
  318. t0, duration, amplitude, ISI,
  319. beta, tau_glU, tau_diff, glU0,
  320. alpha_periph,
  321. tau_F, H_F, F0,
  322. tau_AMPA,
  323. H_D, tau_D)
  324. return list(res)
  327. def System_of_ODEs(y, t, Stim_unused, t0, duration, amplitude, ISI, params):
  328. """Wrapper for scipy.integrate.odeint (y, t) calling convention.
  329. Signature is backward-compatible with the original EAAT_main66.py.
  330. The 'Stim_unused' argument is accepted but ignored (stimulus is
  331. recomputed inside the numba kernel via Stim_5_njit)."""
  332. beta, tau_glU, tau_diff, glU0, alpha_periph, tau_F, H_F, F0, tau_AMPA, H_D, tau_D = params
  333. res = System_of_ODEs_njit(t, np.asarray(y, dtype=np.float64),
  334. t0, duration, amplitude, ISI,
  335. beta, tau_glU, tau_diff, glU0,
  336. alpha_periph,
  337. tau_F, H_F, F0,
  338. tau_AMPA,
  339. H_D, tau_D)
  340. return list(res)
  343. # ---------------------------------------------------------------------------
  344. # Remaining utility functions — unchanged
  345. # ---------------------------------------------------------------------------
  347. def f_tau_diff(tau_diff0, d_tau_diff_dV, Vh):
  348. y = tau_diff0 + d_tau_diff_dV * (Vh + 66)
  349. if y < 10:
  350. y = 10
  351. return y
  354. def Filter_Array(x, gam=0.1):
  355. y = np.copy(x)
  356. for i in range(len(x) - 2, 0, -1):
  357. y[i] = y[i + 1] * (1 - gam) + gam * x[i]
  358. for i in range(1, len(x) - 1):
  359. y[i] = y[i - 1] * (1 - gam) + gam * y[i]
  360. return y
  363. def sigmoid(x):
  364. return 1.0 / (1.0 + np.exp(-x))
  367. def half_sigmoid(x):
  368. return 2.0 / (1.0 + np.exp(-x * 2)) - 1.0
  371. def Interpolate_for_t(tE, yE, t_):
  372. for i in range(len(tE)):
  373. if tE[i] >= t_:
  374. break
  375. return yE[i]
  378. def Residual(tE, yE, t, y, tend):
  379. Err = 0
  380. n = 0
  381. for i in range(len(t)):
  382. if (t[i] > 0) and (t[i] <= tend):
  383. n = n + 1
  384. yEi = Interpolate_for_t(tE, yE, t[i])
  385. Err = Err * (n - 1) / n + (y[i] - yEi) ** 2 / n
  386. return Err
  389. # ---------------------------------------------------------------------------
  390. # Trigger numba JIT compilation on import so the first solve_ivp/odeint call
  391. # does not pay the compilation cost.
  392. # ---------------------------------------------------------------------------
  393. def _warmup_numba():
  394. _y0 = np.array([0.0, 0.0, 0.5, 1.0, 0.0])
  395. _p = (50.0, 3.0, 1500.0, 0.01, 0.25, 3000.0, 10.0, 0.5, 4.0, 0.07, 100.0)
  396. System_of_ODEs_njit(0.0, _y0, 2.0, 1.0, 35.0, 20.0, *_p)
  397. System_of_ODEs_njit(3.0, _y0, 2.0, 1.0, 35.0, 20.0, *_p)
  399. _warmup_numba()
  402. # ---------------------------------------------------------------------------
  403. # Plotting S_AMPA, S_NMDA, fNMDA (unchanged from original)
  404. # ---------------------------------------------------------------------------
  405. plt.figure(figsize=(8, 5))
  407. x = np.logspace(-2, 5, num=80)
  408. Sampa = S_AMPA(x)
  409. Snmda = S_NMDA(x)
  410. fEAAT_arr = np.array([fEAAT2(xi) for xi in x])
  412. plt.subplot(211)
  413. plt.semilogx(x, Sampa, 'r-', label='S_AMPA(glU)')
  414. plt.semilogx(x, Snmda, 'orange', label='S_NMDA(glU)')
  415. plt.semilogx(x, fEAAT_arr, 'blue', label='f_EAAT(glU)')
  416. plt.title('Sigmoids')
  417. plt.xlabel('Glutamate, µM')
  418. plt.ylabel('Receptor activation')
  419. plt.grid(True)
  420. plt.legend()
  422. x2 = np.linspace(-80, 40, num=80)
  423. fnmda_arr = fNMDA(x2, 1)
  425. plt.subplot(212)
  426. plt.plot(x2, fnmda_arr, 'red', label='f_NMDA(U)')
  427. plt.title('f_NMDA for [Mg]_o = 1 mM')
  428. plt.xlabel('mV')
  429. plt.ylabel('units')
  430. plt.grid(True)
  431. plt.legend()
  433. plt.tight_layout()
  434. plt.savefig('Sigmoids.png', dpi=300)

Pars_and_Functions10_numba.py at commit 8fa8f6a, no license · at the source

Overview

Authors: A V Chizhov1, S L Malkin2, D N Khachatryan3, A V Zaitsev2
  1. Computational Neurophysics Laboratory, Institute for Theoretical Physics, University of Bremen, Bibliothekstrasse 1, 28359 Bremen, Germany
  2. Sechenov Institute of Evolutionary Physiology and Biochemistry, Saint Petersburg, Russia
  3. Quantum Brains, Meraba Aleksidze street 12, 0171 Tbilisi, Georgia
Institutions: University of Bremen (Germany); Institute of Evolutionary Physiology and Biochemistry (Russia)
Journal: Journal of computational neuroscience, volume 54, issue 2, pages 393-406
Dates: received 17 November 2025; accepted 26 April 2026; published online 13 May 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s10827-026-00935-8 · PMID 42126468 · PMCID PMC13233636 · OpenAlex W7160983780
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), rat (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Single-unit activity, calcium imaging
Keywords: Glutamatergic transmission, Computational model, EAAT2 transporters, AMPA receptors, NMDA receptors, Synaptic plasticity, Electrotonic effects
MeSH: Glutamic Acid*, Models, Neurological*, Receptors, AMPA*, Receptors, N-Methyl-D-Aspartate*, Synaptic Transmission*, Animals, Excitatory Postsynaptic Potentials, Hippocampus, Kinetics, Neuronal Plasticity, Pyramidal Cells, Rats, Rats, Sprague-Dawley (* major topic)
Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Universität Bremen
Citations: not cited yet (Europe PMC); 43 references in the paper

Abstract

Gluatamatergic synaptic transmission, critical for learning and memory, relies on precise regulation of extracellular glutamate levels by astrocytic transporters, particularly EAAT2. While existing models of AMPA and NMDA receptor kinetics often oversimplify glutamate dynamics or become computationally intractable, this study develops a balanced, biophysically grounded model that integrates glutamate transport, receptor sensitivity, and electrotonic effects. Using rat hippocampal slices, we recorded postsynaptic currents in CA1 pyramidal neurons under control conditions and during glutamate transporter blockade. The proposed mathematical model, formulated as a system of seven ordinary differential equations, distinguishes somatic and dendritic compartments, synaptic plasticity, and differential glutamate sensitivity of AMPA and NMDA receptors. Key findings reveal that the glutamate transporter blockade prolongs NMDA receptor-mediated currents without altering AMPA receptor kinetics, consistent with the higher glutamate sensitivity of NMDA receptors. The model also predicts glutamate concentrations in synaptic and extrasynaptic spaces, offering insights into spatial neurotransmitter dynamics. Furthermore, it accounts for voltage-dependent NMDA responses and short-term plasticity observed experimentally. By bridging the gap between oversimplified and overly complex approaches, this work provides a versatile tool for studying synaptic transmission in normal and pathological conditions, such as epilepsy or neurodegenerative diseases, where glutamate dysregulation plays a central role.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

antonvchizhov/EAAT-paper

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 8fa8f6ac5340ed0ac3a01a8a987049d87b6aa617, 8 April 2026
Languages: Python (4)
Size: 11 files, 4 scripts
Software Heritage: not archived
Found in: the text, “Numerical implementation”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (4 files), NumPy (4 files), Numba (2 files), SciPy (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
5 files
At the source: github.com/antonvchizhov/EAAT-paper.git

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 4 scripts, each with its path and the digest of its content;
  • 3 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

No datasets were generated or analysed during the current study.

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 2, 28 September 2026

  • Publisher: n/a → Springer Science+Business Media

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 7 keywords, 13 MeSH terms, 1 funder, 37 references.

Cite

This paper

Chizhov, A. V., Malkin, S. L., Khachatryan, D. N., & Zaitsev, A. V. (2026). A biophysically grounded model of glutamatergic synaptic transmission integrating glutamate transport, receptor kinetics, and electrotonic effects. Journal of computational neuroscience, 54(2), 393-406. https://doi.org/10.1007/s10827-026-00935-8

BibTeX

@article{chizhov2026biophysically,
author = {Chizhov, A V and Malkin, S L and Khachatryan, D N and Zaitsev, A V},
title = {{A biophysically grounded model of glutamatergic synaptic transmission integrating glutamate transport, receptor kinetics, and electrotonic effects}},
journal = {Journal of computational neuroscience},
year = {2026},
month = may,
volume = {54},
number = {2},
pages = {393--406},
publisher = {Springer Science+Business Media},
issn = {0929-5313},
doi = {10.1007/s10827-026-00935-8},
url = {https://doi.org/10.1007/s10827-026-00935-8},
pmid = {42126468},
pmcid = {PMC13233636}
}

RIS

TY - JOUR
AU - Chizhov, A V
AU - Malkin, S L
AU - Khachatryan, D N
AU - Zaitsev, A V
TI - A biophysically grounded model of glutamatergic synaptic transmission integrating glutamate transport, receptor kinetics, and electrotonic effects
T2 - Journal of computational neuroscience
J2 - J Comput Neurosci
PY - 2026
DA - 2026/05/13
VL - 54
IS - 2
SP - 393
EP - 406
SN - 0929-5313
PB - Springer Science+Business Media
DO - 10.1007/s10827-026-00935-8
UR - https://doi.org/10.1007/s10827-026-00935-8
LA - en
ER -

CSL-JSON

{
"id": "10.1007/s10827-026-00935-8",
"type": "article-journal",
"title": "A biophysically grounded model of glutamatergic synaptic transmission integrating glutamate transport, receptor kinetics, and electrotonic effects",
"container-title": "Journal of computational neuroscience",
"author": [
{
"family": "Chizhov",
"given": "A V"
},
{
"family": "Malkin",
"given": "S L"
},
{
"family": "Khachatryan",
"given": "D N"
},
{
"family": "Zaitsev",
"given": "A V"
}
],
"container-title-short": "J Comput Neurosci",
"volume": "54",
"issue": "2",
"page": "393-406",
"DOI": "10.1007/s10827-026-00935-8",
"PMID": "42126468",
"PMCID": "PMC13233636",
"ISSN": "0929-5313",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.1007/s10827-026-00935-8",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
13
]
]
}
}

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

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