OSCR

Population-scale analysis of frequency-dependent calcium dynamics in retinal ganglion cells under electric field stimulation.

Code ↔ Paper

5 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 5 matches
  1. [1] § Methods › Neuron: retinal ganglion cell Ca model ↔ SingleCellRGCsCaModelNeuron/RGCModel.py, lines 88–127 · score 0.77 · low voltage activated, hyperpolarization activated, RGC model, Ca model, Fohlmeister, Ih
  2. [2] § Methods › Neuron: retinal ganglion cell Ca model ↔ SingleCellRGCsCaModelNeuron/RGCModel.py, lines 254–383 · score 0.66 · extracellular space, Reaction, buffering, bound, pump, concentration
  3. [3] § Methods › Neuron: retinal ganglion cell Ca model ↔ SingleCellRGCsCaModelNeuron/RGCModel.py, lines 88–127 · score 0.59 · Ca activated, voltage activated, calcium channel, pump, model, Neuron
  4. [4] § Methods › Electric field stimulation waveform design ↔ SingleCellRGCsCaModelNeuron/RGCModel.py, lines 485–632 · score 0.55 · intracellular Ca, GCaMP, probe, membrane, spiking, neurons
  5. [5] § Methods › Admittance method: constructing the experimental setup and electrodes ↔ SingleCellRGCsCaModelNeuron/RGCModel.py, lines 254–383 · score 0.53 · RGC model, volume, segment, space, compartments, membrane

Paper

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The authors' code

Python · 661 lines · 28 KB · MIT · 5 matches

  1. # Imports
  2. from neuron import h, rxd
  3. import numpy as np
  4. import json
  5. from matplotlib import pyplot
  6. from neuron.units import mM, mV, nM, uM
  7. from neuron.rxd import v
  8. from neuron.rxd.rxdmath import vtrap, exp, log, tanh, fabs
  9. import os
  10. from tqdm import tqdm
  11. import csv
  13. # Add necessary .hoc to Neuron h backbone
  14. h.load_file('stdrun.hoc')
  15. h.load_file('import3d.hoc')
  16. h.load_file('stdlib.hoc')
  17. h.load_file('makecells_A2i.hoc')
  20. class RGCModel:
  22. # This Class model RGCs and add RXD adds-on to take care of Ca ion concentration and changes
  23. def __init__(self, JSONFile, MorphologySWCName):
  25. self.loadMorph(MorphologySWCName)
  26. self.padding = 100
  27. self.have_ncx = False
  28. self.have_cicr = False
  29. self.have_gcamp = False
  31. # Load configuration from the passed Json file
  32. with open(JSONFile, 'r') as fileObj:
  33. self.rxdDict = json.load(fileObj)
  35. def loadMorph(self, MorphologySWCName):
  36. '''
  37. :param MorphologySWCName:
  38. :return: None
  39. Make the model of the cell based on the morphology of the swc file
  40. '''
  41. # Load the morphology of the RGC type
  42. self.c = h.mkcell(MorphologySWCName)
  44. # Make each group of the neuron sections instance
  45. self.axonal = list(self.c.axonal)
  46. self.somaList = list(self.c.somatic)
  47. self.basalList = list(self.c.basal)
  48. self.apicalList = list(self.c.apical)
  49. self.dendritic5 = list(self.c.dendritic_5)
  50. self.dendritic6 = list(self.c.dendritic_6)
  51. self.dendritic7 = list(self.c.dendritic_7)
  53. # 'all' contains all components of the neuron
  54. self.all = self.axonal + self.dendritic5 + self.dendritic6 + self.dendritic7 + \
  55. self.somaList + self.basalList + self.apicalList
  57. def findExtrema(self):
  58. '''
  59. Finds the extrema of the morphology, and stores them into variables xmin, ymin, zmin, xmax, ymax, and zmax.
  60. These coordinates will be used in defining the extracellular space in 3D using rxd.Extracellular()
  61. :return: NONE
  62. '''
  63. self.xmin = self.ymin = self.zmin = self.xmax = self.ymax = self.zmax = None
  64. for sec in self.all:
  65. n3d = sec.n3d()
  66. xList = [sec.x3d(i) for i in range(n3d)]
  67. yList = [sec.y3d(i) for i in range(n3d)]
  68. zList = [sec.z3d(i) for i in range(n3d)]
  70. xMinSec, yMinSec, zMinSec = min(xList), min(yList), min(zList)
  71. xMaxSec, yMaxSec, zMaxSec = max(xList), max(yList), max(zList)
  73. if self.xmin is None:
  74. self.xmin, self.ymin, self.zmin = xMinSec, yMinSec, zMinSec
  75. self.xmax, self.ymax, self.zmax = xMaxSec, yMaxSec, zMaxSec
  76. else:
  77. self.xmin, self.ymin, self.zmin = min(self.xmin, xMinSec), min(self.ymin, yMinSec), min(self.zmin, zMinSec)
  78. self.xmax, self.ymax, self.zmax = max(self.xmax, xMaxSec), max(self.ymax, yMaxSec), max(self.zmax, zMaxSec)
  80. print('Extrema of the morphology has been calculated')
  82. def concInit(self, cytc, exc, erc):
  83. return lambda nd: exc if nd.region == self.ecs else (cytc if nd.region == self.cyt else erc)
  85. def concInitTemp(self, cytc, erc):
  86. return lambda nd: cytc if nd.region == self.cyt else erc
  88. def insertChannels(self, GNabar_spike, GKbar_spike, GAbar_spike, GCabar_spike, GKCbar_spike, GH, GT, testDiam):
  89. '''
  90. Insert channels into the cell. It will add the following channels:
  91. - Spike.mod (HH Model by Fohlmeister)
  92. - Na
  93. - Ca
  94. - K (delayed rectifier)
  95. - KA (A-type K channel)
  96. - KCa (Ca activated K channel)
  97. + Ca pumps are modeled in the RXD part
  98. - IT.mod
  99. Low-voltage-activated (LVA) calcium channels
  100. - Ih.mod
  101. Hyperpolarized activated channel
  103. :param: It will load any needed value from the rxdDict.
  104. :return:
  105. '''
  106. for section in self.all:
  107. if section.name().find('dend[') != -1:
  108. section.insert('pas')
  109. section.e_pas = -60
  110. section.g_pas = .00005
  111. section.Ra = 110
  112. section.nseg = 1
  114. section.insert('spike')
  115. section.gnabar_spike = 0.08
  116. section.gkbar_spike = 0.08
  117. section.gabar_spike = 3 * section.gkbar_spike
  118. section.gcabar_spike = 0.01
  119. section.gkcbar_spike = 0.004 * section.gkbar_spike
  121. section.insert('Ih')
  122. section.ghbar_Ih = 3e-5
  124. section.insert('IT')
  125. section.gTbar_IT = 0.001
  127. print('Dend channel inserted...')
  129. # Axon hillock
  130. if section.name().find('dend_5') != -1:
  131. section.insert('pas')
  132. section.e_pas = -60
  133. section.g_pas = .00005
  134. section.Ra = 110
  136. section.nseg = 1
  138. section.insert('spike')
  139. section.gnabar_spike = 0.8
  140. section.gkbar_spike = 0.6
  141. section.gabar_spike = 3 * section.gkbar_spike
  142. section.gcabar_spike = 0
  143. section.gkcbar_spike = 0
  145. section.insert('Ih')
  146. section.ghbar_Ih = 0
  148. section.insert('IT')
  149. section.gTbar_IT = 0
  151. print('Axon hillock channel inserted...')
  153. # Sodium channel band
  154. if section.name().find('dend_6') != -1:
  155. section.insert('pas')
  156. section.e_pas = -60
  157. section.g_pas = .00005
  158. section.Ra = 110
  160. section.nseg = 1
  162. section.insert('spike')
  163. section.gnabar_spike = 2.4
  164. section.gkbar_spike = 0.8
  165. section.gabar_spike = 3 * section.gkbar_spike
  166. section.gcabar_spike = 0
  167. section.gkcbar_spike = 0
  169. section.insert('Ih')
  170. section.ghbar_Ih = 0
  172. section.insert('IT')
  173. section.gTbar_IT = 0
  175. print('SOCB channel inserted...')
  177. # Narrow segment
  178. if section.name().find('dend_7') != -1:
  179. section.insert('pas')
  180. section.e_pas = -60
  181. section.g_pas = .00005
  182. section.Ra = 110
  184. section.nseg = 1
  186. section.insert('spike')
  187. section.gnabar_spike = 0.9
  188. section.gkbar_spike = 0.7
  189. section.gabar_spike = 3 * section.gkbar_spike
  190. section.gcabar_spike = 0
  191. section.gkcbar_spike = 0
  193. section.insert('Ih')
  194. section.ghbar_Ih = 0
  196. section.insert('IT')
  197. section.gTbar_IT = 0
  199. print('Narrow segment channel inserted...')
  201. # Distal axon
  202. if section.name().find('axon') != -1:
  203. section.insert('pas')
  204. section.e_pas = -60
  205. section.g_pas = .00005
  206. section.Ra = 110
  208. section.nseg = 1
  210. section.insert('spike')
  211. section.gnabar_spike = 0.8
  212. section.gkbar_spike = 0.6
  213. section.gabar_spike = 3 * section.gkbar_spike
  214. section.gcabar_spike = 0
  215. section.gkcbar_spike = 0
  217. section.insert('Ih')
  218. section.ghbar_Ih = 0
  220. section.insert('IT')
  221. section.gTbar_IT = 0
  223. print('Distal axon channel inserted...')
  225. # Soma
  226. if section.name().find('soma') != -1:
  227. section.insert('pas')
  228. section.e_pas = -65
  229. section.g_pas = 0.0001
  230. section.Ra = 110
  231. section.nseg = 1
  232. section.diam = testDiam
  233. # print('Diam: ', section.diam)
  235. section.insert('spike')
  236. section.gnabar_spike = GNabar_spike
  237. section.gkbar_spike = GKbar_spike
  238. section.gabar_spike = GAbar_spike
  239. section.gcabar_spike = GCabar_spike
  240. section.gkcbar_spike = GKCbar_spike
  242. section.insert('Ih')
  243. section.ghbar_Ih = GH
  245. section.insert('IT')
  246. section.gTbar_IT = GT
  248. print('Soma channel inserted...')
  250. h.celsius = 36
  252. print('***** All channels inserted *****')
  254. def implement_RXD(self, CAT):
  256. #### Initial concentrations ####
  257. # Na
  258. naConcInitCyt = self.rxdDict["species"]["na"]["cyt"]
  259. naConcInitEcs = self.rxdDict["species"]["na"]["ecs"]
  261. # K
  262. kConcInitCyt = self.rxdDict["species"]["k"]["cyt"]
  263. kConcInitEcs = self.rxdDict["species"]["k"]["ecs"]
  265. # Ca
  266. caConcInitCyt = self.rxdDict["species"]["ca"]["cyt"]
  267. caConcInitEcs = self.rxdDict["species"]["ca"]["ecs"]
  268. caConcInitEr = self.rxdDict["species"]["ca"]["er"]
  269. caDepth = self.rxdDict["species"]["ca"]["depth"]
  270. caTau = self.rxdDict["species"]["ca"]["tca"]
  272. # NCX
  273. kNCXBindF = 93.827 # msec^-1 mM^-1
  274. kNCXBindR = 4.0 # msec^-1
  275. kNCXRel = 1000.0 # msec^-1 mM^-1
  277. # NCX
  278. if self.have_ncx:
  279. totalNCX = self.rxdDict["channelDensities"]["totalNCX"]
  280. ncx2CaInit = totalNCX * (caConcInitCyt ** 2) / ((kNCXBindR / kNCXBindF) + (caConcInitCyt ** 2))
  281. ncxInit = totalNCX - ncx2CaInit
  283. # Calcium-induced Ca release
  284. self.VCICR = 100000.0 # cm^-2 ms^-1
  285. self.KCICR = 0.0002 # mM
  286. tauCICR = 1.2 # ms
  287. KtCICR = 0.0002 # mM
  289. # ER fraction in the cells' Cyt
  290. self.erFraction = self.rxdDict["region"]["erFraction"]
  291. self.cytFraction = 1 - self.erFraction
  293. ########################################
  294. # RXD
  295. '''
  296. Code that uses RXD should define the following criteria: WHERE the reaction is happening, WHO (what species) are we interested
  297. in, and WHAT about those species of interest? With that being said, the following code
  298. answers, 'where?' The reaction is happening in: the cytoplasm, cell membrane, and extracellular fluid.
  299. '''
  301. ##########################################################################################
  302. ######################################### WHERE? #########################################
  303. ##########################################################################################
  304. self.cyt = rxd.Region(self.all, name='cyt', nrn_region='i',
  305. geometry=rxd.FractionalVolume(self.cytFraction, surface_fraction=1.0))
  307. self.er = rxd.Region(self.all, name='er',
  308. geometry=rxd.FractionalVolume(self.erFraction)) # Endoplasmic Reticulum
  309. self.erCytMem = rxd.Region(self.all, name='erCytMem', geometry=rxd.ScalableBorder(0.1,
  310. on_cell_surface=False)) # Membrane between Cytoplusm and ER
  311. self.mem = rxd.Region(self.all, name='mem', geometry=rxd.membrane())
  313. self.ecs = rxd.Extracellular(self.xmin - self.padding, self.ymin - self.padding, self.zmin - self.padding,
  314. self.xmax + self.padding, self.ymax + self.padding, self.zmax + self.padding,
  315. dx=50) # Extracellular Space
  317. print('RXD regions implemented')
  319. ##########################################################################################
  320. ######################################### WHAT? ##########################################
  321. ##########################################################################################
  322. # Ions
  323. # self.k = rxd.Species([self.cyt, self.ecs], name="k", d=1, charge=1,
  324. # initial=self.concInit(kConcInitCyt, kConcInitEcs,0))
  325. # self.na = rxd.Species([self.cyt, self.ecs], name="na", d=1, charge=1,
  326. # initial=self.concInit(naConcInitCyt, naConcInitEcs, 0))
  327. self.ca = rxd.Species([self.cyt, self.ecs, self.er], name='ca', d=1, charge=2,
  328. initial=self.concInit(caConcInitCyt, caConcInitEcs, caConcInitEr))
  330. self.cai = self.ca[self.cyt]
  331. self.cao = self.ca[self.ecs]
  332. self.caer = self.ca[self.er]
  334. # Na/Ca Exchange (NCX)
  335. if self.have_ncx:
  336. self.ncx = rxd.Species([self.cyt], name='ncx', initial=ncxInit) # Free NCX inside Cyt
  337. self.ncx2Ca = rxd.Species([self.cyt], name='ncx2Ca', initial=ncx2CaInit) # NCX bound to Ca inside Cyt
  339. # Ca-induced Ca release
  340. if self.have_cicr:
  341. CICRThresh = (1 + tanh(2 * 10000 * (self.cai - KtCICR))) / 2
  342. self.scale_cicr = rxd.Parameter([self.erCytMem],
  343. value=lambda nd: self.VCICR * nd.segment.diam / 4.0)
  344. CICRFluxInf = CICRThresh * self.cai / (self.KCICR + self.cai) * (self.caer - self.cai)
  345. self.CICRState = rxd.State([self.erCytMem], name='CICRstate', initial=1.749e-9)
  347. print('RXD species created')
  349. # GCaMP
  350. if self.have_gcamp:
  351. self.gcamp = rxd.Species([self.cyt], name='GCaMP', initial=0.05) # 0.05 mM = 50 μM
  352. self.cagcamp = rxd.Species([self.cyt], name='CaGCaMP', initial=0)
  354. ##########################################################################################
  355. ########################################## HOW? ##########################################
  356. ##########################################################################################
  358. # Ca Pump
  359. self.CaPump = rxd.MultiCompartmentReaction(self.cao, self.cai,
  360. 6.02 * 10 ** 8 * (caConcInitCyt - self.cai) / CAT,
  361. membrane=self.mem, scale_by_area=False, membrane_flux=True)
  362. # Na/Ca Exchange (NCX)
  363. if self.have_ncx:
  364. self.ncxBinding = rxd.Reaction(2*self.ca + self.ncx, self.ncx2Ca, kNCXBindF, kNCXBindR, region=[self.cyt])
  365. self.ncxRelease = rxd.MultiCompartmentReaction(self.ncx2Ca[self.cyt], self.ncx[self.cyt] + 2*self.cao, kNCXRel, membrane=self.mem, scale_by_area=False, membrane_flux=True)
  367. # CICR
  368. if self.have_cicr:
  369. self.dCICR = rxd.Rate(self.CICRState, (CICRFluxInf - self.CICRState) / tauCICR, regions=[self.erCytMem])
  370. # self.CICRFlux = rxd.MultiCompartmentReaction(self.caer, self.cai, self.scale_cicr * self.CICRState,
  371. # membrane=self.erCytMem)
  372. self.CICRFlux = rxd.MultiCompartmentReaction(self.caer, self.cai, self.CICRState,
  373. membrane=self.erCytMem)
  375. # GCaMP
  376. if self.have_gcamp:
  377. # Kinetic rates (per ms)
  378. kon = 1e8 / 1e3 # convert M⁻¹s⁻¹ to mM⁻¹ms⁻¹ (divide by 1e3)
  379. koff = 37.5 / 1e3 # convert s⁻¹ to ms⁻¹ (divide by 1e3)
  380. # Buffer reaction
  381. self.GCaMPBinding = rxd.Reaction(self.gcamp + self.cai, self.cagcamp, kon, koff, region=[self.cyt])
  383. print('RXD reactions specified - RXD setup complete')
  385. def stimulate(self, stimNeuron, measureNeuron, location, current_vector, time_vector, sim_dur, dt):
  386. '''
  387. :param stimNeuron:
  388. :param measureNeuron:
  389. :param location:
  390. :param current_vector:
  391. :param time_vector:
  392. :param sim_dur:
  393. :param dt:
  394. :return:
  395. '''
  397. # Following vectors are necessary in order to create graphs.
  398. ### Time and Membraine Potential ###
  399. self.tvec = h.Vector().record(h._ref_t)
  400. self.vvec = h.Vector().record(measureNeuron(location)._ref_v)
  402. ### K, Na, and Ca ###
  403. self.kivec = h.Vector().record(measureNeuron(location)._ref_ki)
  404. self.kovec = h.Vector().record(measureNeuron(location)._ref_ko)
  405. self.naovec = h.Vector().record(measureNeuron(location)._ref_nao)
  406. self.naivec = h.Vector().record(measureNeuron(location)._ref_nai)
  407. self.caovec = h.Vector().record(measureNeuron(location)._ref_cao)
  408. self.caivec = h.Vector().record(measureNeuron(location)._ref_cai)
  409. self.cairxdvec = h.Vector().record(self.cai.nodes(measureNeuron(location))._ref_concentration)
  410. self.ecavec = h.Vector().record(measureNeuron(location)._ref_eca)
  411. self.caervec = h.Vector().record(self.caer.nodes(measureNeuron(location))._ref_concentration)
  413. ### NCX ###
  414. if self.have_ncx:
  415. self.ncxvec = h.Vector().record(self.ncx.nodes(measureNeuron(location))._ref_value)
  416. self.ncx2cavec = h.Vector().record(self.ncx2Ca.nodes(measureNeuron(location))._ref_value)
  418. ### CICR ###
  419. if self.have_cicr:
  420. self.cicrvec = h.Vector().record(
  421. self.CICRState[self.erCytMem].nodes(measureNeuron(location))._ref_value)
  423. ### GCaMP ###
  424. if self.have_gcamp:
  425. self.gcampvec = h.Vector().record(self.gcamp.nodes(measureNeuron(location))._ref_value)
  426. self.cagcampvec = h.Vector().record(self.cagcamp.nodes(measureNeuron(location))._ref_value)
  428. ### Spike.mod ###
  429. self.spike_mvec = h.Vector().record(measureNeuron(location).spike._ref_m)
  430. self.spike_nvec = h.Vector().record(measureNeuron(location).spike._ref_n)
  431. self.spike_hvec = h.Vector().record(measureNeuron(location).spike._ref_h)
  432. self.spike_pvec = h.Vector().record(measureNeuron(location).spike._ref_p)
  433. self.spike_qvec = h.Vector().record(measureNeuron(location).spike._ref_q)
  434. self.spike_cvec = h.Vector().record(measureNeuron(location).spike._ref_c)
  436. self.inavec = h.Vector().record(measureNeuron(location)._ref_ina)
  437. self.ikvec = h.Vector().record(measureNeuron(location)._ref_ik)
  438. self.icavec = h.Vector().record(measureNeuron(location)._ref_ica)
  440. print('Recording vectors created')
  442. # Create a current clamp
  443. iclamp = h.IClamp(stimNeuron(location))
  444. iclamp.dur = sim_dur
  446. # Vector to play the waveform
  447. stim_vec = h.Vector(current_vector)
  448. time_vec = h.Vector(time_vector)
  449. stim_vec.play(iclamp._ref_amp, time_vec, 1)
  451. # Run simulation
  452. h.dt = dt
  453. h.finitialize(-75)
  454. for i in tqdm(range(len(time_vec))):
  455. h.fadvance()
  457. print('Simulation complete')
  459. def calculateAIC(self, log_dir, stimulation_config, stim_start, stim_end):
  460. caVec = 1000 * self.caivec.as_numpy()
  461. tVec = self.tvec.as_numpy()
  462. mask = (tVec >= stim_start) & (tVec <= stim_end)
  463. maskbaseline = (stim_start/2 < tVec) & (tVec < stim_start)
  464. AIC = np.mean(caVec[mask])
  465. F0 = np.mean(np.power(caVec[maskbaseline], 2.5) / (0.375**2.5 + np.power(caVec[maskbaseline], 2.5)))
  466. F = np.mean(np.power(caVec[mask], 2.5) / (0.375**2.5 + np.power(caVec[mask], 2.5)))
  467. AICG = ((F/F0) - 1)
  469. print('AIC = ', AIC)
  470. print('AICG = ', AICG)
  472. if self.have_gcamp:
  473. cagcampVec = 1000 * self.cagcampvec.as_numpy()
  474. AIG = np.mean(cagcampVec[mask])
  475. print('AIG = ', AIG)
  477. with open(log_dir, 'a', newline='') as f:
  478. writer = csv.writer(f)
  479. writer.writerow([stimulation_config['waveform'], stimulation_config['frequency'],
  480. stimulation_config['amplitude'], AICG])
  481. if self.have_gcamp:
  482. writer.writerow([stimulation_config['waveform'], stimulation_config['frequency'],
  483. stimulation_config['amplitude'], AIG])
  485. def plot_results(self, save_dir):
  487. # Prepare the saving directory
  488. if not os.path.exists(save_dir):
  489. os.makedirs(save_dir)
  491. # Figures
  492. ### Membrane Potential ###
  493. fig = pyplot.figure(figsize=(16, 6))
  494. pyplot.plot(self.tvec.as_numpy(), self.vvec.as_numpy(), '-b', label='k')
  495. pyplot.title("Membrane Potential")
  496. pyplot.xlabel('t (ms)')
  497. pyplot.ylabel('Voltage (mV)')
  498. pyplot.savefig(save_dir + 'MemPotTest.svg', format="svg")
  500. ## Ca ###
  501. # # Ca outside the cell
  502. # fig = pyplot.figure(figsize=(16, 6))
  503. # pyplot.plot(self.tvec.as_numpy(), 1000 * self.caovec.as_numpy(), '-g', label='ca')
  504. # pyplot.xlabel('time (ms)')
  505. # pyplot.ylabel('Concentration (uM)')
  506. # pyplot.title('Extracellular Ca')
  507. # pyplot.savefig(save_dir+'ExtraCaTest.svg', format="svg")
  509. # Ca inside Cyt
  510. fig = pyplot.figure(figsize=(16, 6))
  511. pyplot.plot(self.tvec.as_numpy(), 1000 * self.caivec.as_numpy(), '-g', label='ca')
  512. pyplot.xlabel('time (ms)')
  513. pyplot.ylabel('Concentration (uM)')
  514. pyplot.title('Intracellular Ca')
  515. pyplot.savefig(save_dir+'IntraCaTest.svg', format="svg")
  517. # # F(t)
  518. # fig = pyplot.figure(figsize=(16, 6))
  519. # pyplot.plot(self.tvec.as_numpy(),
  520. # np.power(self.caivec.as_numpy(), 2.5) /
  521. # (0.375 ** 2.5 + np.power(self.caivec.as_numpy(), 2.5)), '-g', label='F(t)')
  522. # pyplot.xlabel('time (ms)')
  523. # pyplot.ylabel('F(t)')
  524. # pyplot.title('Fluorescence (f(t))')
  525. # pyplot.savefig(save_dir+'Ft.svg', format="svg")
  527. ### NCX ###
  528. if self.have_ncx:
  529. fig = pyplot.figure(figsize=(16, 6))
  530. pyplot.plot(self.tvec.as_numpy(), 1000*self.ncxvec.as_numpy(), '-b', label='NCX')
  531. pyplot.plot(self.tvec.as_numpy(), 1000*self.ncx2cavec.as_numpy(), '-r', label='NCX2Ca')
  532. pyplot.xlabel('time (ms)')
  533. pyplot.ylabel('Concentration (uM)')
  534. pyplot.title('Free NCX and bound NCX inside Cyt')
  535. pyplot.legend()
  536. pyplot.savefig(save_dir+'NCXTest.svg', format="svg")
  538. ### CICR ###
  539. if self.have_cicr:
  540. fig = pyplot.figure(figsize=(16, 6))
  541. pyplot.plot(self.tvec.as_numpy(), 1000*self.cicrvec.as_numpy(), '-b', label='CICR')
  542. # pyplot.plot(rgc1.tvec.as_numpy(), 1000*rgc1.cicrfvec.as_numpy(), '-b', label='CICRFlux')
  543. pyplot.xlabel('t (ms)')
  544. pyplot.ylabel('State Value')
  545. pyplot.title('CICR state inside erCytMem')
  546. pyplot.legend()
  547. pyplot.savefig(save_dir+'CICRTest.svg', format="svg")
  549. ### GCaMP ###
  550. if self.have_gcamp:
  551. fig = pyplot.figure(figsize=(16, 6))
  552. pyplot.plot(self.tvec.as_numpy(), 1000*self.gcampvec.as_numpy(), '-b', label='Free GCaMP')
  553. pyplot.plot(self.tvec.as_numpy(), 1000*self.cagcampvec.as_numpy(), '-r', label='Ca + GCaMP')
  554. pyplot.xlabel('time (ms)')
  555. pyplot.ylabel('Concentration (uM)')
  556. pyplot.title('Free GCaMP and bound GCaMP inside Cyt')
  557. pyplot.legend()
  558. pyplot.savefig(save_dir+'GCaMPTest.svg', format="svg")
  560. # # Ca inside ER
  561. # fig = pyplot.figure(figsize=(16, 6))
  562. # pyplot.plot(self.tvec.as_numpy(), 1000 * self.caervec.as_numpy(), '-g', label='k')
  563. # pyplot.xlabel('time (ms)')
  564. # pyplot.ylabel('Concentration (uM)')
  565. # pyplot.title('ER Ca')
  566. # pyplot.savefig(save_dir+'ERCaTest.svg', format="svg")
  568. # # Ca inside Cyt Recorded by RXD
  569. # fig = pyplot.figure(figsize=(16, 6))
  570. # pyplot.plot(self.tvec.as_numpy(), 1000 * self.cairxdvec.as_numpy(), '-g', label='ca')
  571. # pyplot.xlabel('time (ms)')
  572. # pyplot.ylabel('Concentration (uM)')
  573. # pyplot.title('Intracellular Ca (Recorded by RXD)')
  574. # # pyplot.savefig(os.path.join('IntraCaTest.svg'), format="svg")
  575. #
  576. # # Ca Nerst voltage
  577. # fig = pyplot.figure(figsize=(16, 6))
  578. # pyplot.plot(self.tvec.as_numpy(), self.ecavec.as_numpy(), '-g', label='ca')
  579. # pyplot.xlabel('time (ms)')
  580. # pyplot.ylabel('V')
  581. # pyplot.title('Ca Nerst voltage')
  582. # # pyplot.savefig(os.path.join('IntraCaTest.svg'), format="svg")
  583. #
  585. #
  586. # ### Na ###
  587. # # Na outside the cell
  588. # fig = pyplot.figure(figsize=(16, 6))
  589. # pyplot.plot(self.tvec.as_numpy(), 1000 * self.naovec.as_numpy(), '-g', label='na')
  590. # pyplot.xlabel('time (ms)')
  591. # pyplot.ylabel('Concentration (uM)')
  592. # pyplot.title('Extracellular Na')
  593. # # pyplot.savefig(os.path.join('ExtraCaTest.svg'), format="svg")
  594. #
  595. # # Na inside Cyt
  596. # fig = pyplot.figure(figsize=(16, 6))
  597. # pyplot.plot(self.tvec.as_numpy(), 1000 * self.naivec.as_numpy(), '-g', label='na')
  598. # pyplot.xlabel('time (ms)')
  599. # pyplot.ylabel('Concentration (uM)')
  600. # pyplot.title('Intracellular Na')
  601. # # pyplot.savefig(os.path.join('IntraCaTest.svg'), format="svg")
  602. #
  603. # ### K ###
  604. # # K outside the cell
  605. # fig = pyplot.figure(figsize=(16, 6))
  606. # pyplot.plot(self.tvec.as_numpy(), 1000 * self.kovec.as_numpy(), '-g', label='k')
  607. # pyplot.xlabel('time (ms)')
  608. # pyplot.ylabel('Concentration (uM)')
  609. # pyplot.title('Extracellular K')
  610. # # pyplot.savefig(os.path.join('ExtraCaTest.svg'), format="svg")
  611. #
  612. # # K inside Cyt
  613. # fig = pyplot.figure(figsize=(16, 6))
  614. # pyplot.plot(self.tvec.as_numpy(), 1000 * self.kivec.as_numpy(), '-g', label='k')
  615. # pyplot.xlabel('time (ms)')
  616. # pyplot.ylabel('Concentration (uM)')
  617. # pyplot.title('Intracellular K')
  618. # # pyplot.savefig(os.path.join('IntraCaTest.svg'), format="svg")
  619. #
  620. ### Spike ###
  621. fig = pyplot.figure(figsize=(16, 6))
  622. pyplot.plot(self.tvec.as_numpy(), self.spike_pvec.as_numpy(), label='p-gate')
  623. pyplot.plot(self.tvec.as_numpy(), self.spike_qvec.as_numpy(), label='q-gate')
  624. pyplot.plot(self.tvec.as_numpy(), self.spike_cvec.as_numpy(), label='c-gate')
  625. pyplot.plot(self.tvec.as_numpy(), self.spike_mvec.as_numpy(), label='m-gate')
  626. pyplot.plot(self.tvec.as_numpy(), self.spike_hvec.as_numpy(), label='h-gate')
  627. pyplot.plot(self.tvec.as_numpy(), self.spike_nvec.as_numpy(), label='n-gate')
  628. pyplot.xlabel('time (ms)')
  629. pyplot.ylabel('Open Probability')
  630. pyplot.title('Spike model parameters')
  631. pyplot.legend()
  632. pyplot.savefig(save_dir+'SpikeOpenProbTest.svg', format="svg")
  634. # fig = pyplot.figure()
  635. # pyplot.plot(self.tvec.as_numpy(), self.spike_ina.as_numpy(), '-b', label='ina')
  636. # pyplot.plot(self.tvec.as_numpy(), self.spike_ik.as_numpy(), '-r', label='ik')
  637. # pyplot.plot(self.tvec.as_numpy(), self.spike_ica.as_numpy(), '-g', label='ica')
  638. # pyplot.xlabel('time (ms)')
  639. # pyplot.ylabel('Ions Currents')
  640. # pyplot.title('Spike model ion currents')
  641. # pyplot.legend()
  642. # # pyplot.savefig(os.path.join('SpikeCurrentTest.svg'), format="svg")
  644. # ### IT ###
  645. # fig = pyplot.figure()
  646. # pyplot.plot(self.tvec.as_numpy(), self.it_ica.as_numpy(), '-b', label='ica')
  647. # pyplot.xlabel('time (ms)')
  648. # pyplot.ylabel('Ca ion Currents (mA/cm2)')
  649. # pyplot.title('IT model ion current')
  650. # pyplot.legend()
  651. # # pyplot.savefig(os.path.join('ItcurrentTest.svg'), format="svg")
  653. # fig = pyplot.figure(figsize=(16, 6))
  654. # pyplot.plot(self.tvec.as_numpy(), self.inavec.as_numpy(), '-b', label='ina')
  655. # pyplot.plot(self.tvec.as_numpy(), self.ikvec.as_numpy(), '-r', label='ik')
  656. # pyplot.plot(self.tvec.as_numpy(), self.icavec.as_numpy(), '-g', label='ica')
  657. # pyplot.xlabel('time (ms)')
  658. # pyplot.ylabel('Ions Currents')
  659. # pyplot.title('Membrane currents (mA/cm2)')
  660. # pyplot.legend()
  661. # # pyplot.savefig(os.path.join('CurrentTest.svg'), format="svg")

RGCModel.py at commit 7108c21, under MIT · at the source

Overview

Authors: Omid Sharafi1,2, Timothy Silliman1,3, Hadi Mokhtari Dowlatabad1,2, Gengle Niu4, Steven T. Walston1,4, Jean-Marie C. Bouteiller1,3, Kimberly K. Gokoffski1,4,5, Gianluca Lazzi1,2,3,4
ORCID iDs: Gengle Niu
  1. Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California,Los Angeles, USA
  2. Department of Electrical and Computer Engineering, University of Southern California,Los Angeles, USA
  3. Alfred E. Mann Department of Biomedical Engineering, University of Southern California,Los Angeles, USA
  4. Department of Ophthalmology, USC Roski Eye Institute, Keck School of Medicine, University of Southern California,Los Angeles, USA
  5. Department of Ophthalmology and Vision Science, Gavin Herbert Eye Institute, University of California, Irvine,Irvine, USA
Institutions: University of Southern California (United States); University of California, Irvine (United States)
Journal: Scientific reports, volume 16, issue 1, article 18948
Dates: received 28 January 2026; accepted 15 April 2026; published online 24 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-49531-x · PMID 42031950 · PMCID PMC13276083 · OpenAlex W7155569662
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: optical imaging (calcium, voltage, 2-photon) (modality), mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Machine learning
Keywords: Biological techniques, Biophysics, Neuroscience, Physiology
MeSH: Calcium*, Calcium Signaling*, Electric Stimulation*, Retinal Ganglion Cells*, Animals, Mice (* major topic)
Topic: Neuroscience and Neural Engineering (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: NSF (2121164); NEI/NIH (R01EY035375); Research to Prevent Blindness Foundation Disney Award for Amblyopia; unrestricted grant to the Department of Ophthalmology from Research to Prevent Blindness; NEI (P30EY029220)
Citations: not cited yet (Europe PMC); 63 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

Omid-SH/RGCaEFS

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 7108c21b04a5bc0fdbe3eec9396b7b493d019813, 9 February 2026
Languages: Python (8), NEURON (4), Shell (1), Jupyter (1)
Size: 189 files, 14 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (7 files), NEURON (5 files), Matplotlib (3 files), OpenCV (3 files), tifffile (3 files), pandas (2 files), PyTorch (1 file), scikit-learn (1 file), seaborn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
16 files
At the source: github.com/Omid-SH/RGCaEFS

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

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

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  • it points to the authors' code: Omid-SH/RGCaEFS
  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41598-026-49531-x.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 4 keywords, 6 MeSH terms, 5 funders, 59 references.

Cite

This paper

Sharafi, O., Silliman, T., Mokhtari Dowlatabad, H., Niu, G., Walston, S. T., Bouteiller, J.-M. C., Gokoffski, K. K., & Lazzi, G. (2026). Population-scale analysis of frequency-dependent calcium dynamics in retinal ganglion cells under electric field stimulation. Scientific reports, 16(1), 18948. https://doi.org/10.1038/s41598-026-49531-x

BibTeX

@article{sharafi2026population,
author = {Sharafi, Omid and Silliman, Timothy and Mokhtari Dowlatabad, Hadi and Niu, Gengle and Walston, Steven T. and Bouteiller, Jean-Marie C. and Gokoffski, Kimberly K. and Lazzi, Gianluca},
title = {{Population-scale analysis of frequency-dependent calcium dynamics in retinal ganglion cells under electric field stimulation}},
journal = {Scientific reports},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {18948},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-49531-x},
url = {https://doi.org/10.1038/s41598-026-49531-x},
pmid = {42031950},
pmcid = {PMC13276083}
}

RIS

TY - JOUR
AU - Sharafi, Omid
AU - Silliman, Timothy
AU - Mokhtari Dowlatabad, Hadi
AU - Niu, Gengle
AU - Walston, Steven T.
AU - Bouteiller, Jean-Marie C.
AU - Gokoffski, Kimberly K.
AU - Lazzi, Gianluca
TI - Population-scale analysis of frequency-dependent calcium dynamics in retinal ganglion cells under electric field stimulation
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/04/24
VL - 16
IS - 1
SP - 18948
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-49531-x
UR - https://doi.org/10.1038/s41598-026-49531-x
LA - en
ER -

CSL-JSON

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"title": "Population-scale analysis of frequency-dependent calcium dynamics in retinal ganglion cells under electric field stimulation",
"container-title": "Scientific reports",
"author": [
{
"family": "Sharafi",
"given": "Omid"
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{
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"volume": "16",
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"PMCID": "PMC13276083",
"ISSN": "2045-2322",
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"date-parts": [
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24
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]
}
}

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