OSCR

Clustered inputs engage dendritic nonlinearities and calcium signaling to support efficient place-field formation in CA1 pyramidal neurons.

Code ↔ Paper

22 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 22 matches
  1. [1] § STAR★METHODS › METHOD DETAILS › Inhibitory inputs ↔ Code_data_generation/Cluster_cells_validation_protocols.py, lines 769–832 · score 0.85 · hippocampal network, PVbasket, Ivy cells, 2.8 Hz, transmission, neurogliaform
  2. [2] § STAR★METHODS › METHOD DETAILS › Inhibitory inputs ↔ Code_data_generation/Cluster_make_connectivity.py, lines 393–435 · score 0.80 · hippocampal network, PVbasket, Ivy cells, neurogliaform, parvalbumin, NGF
  3. [3] § STAR★METHODS › METHOD DETAILS › Grid-like and place-like inputs structure ↔ Make_input/Cluster_make_grid_like_inputs.py, lines 148–233 · score 0.79 · 30–40 Hz, place field location, theta phase, tref, CA3, EC
  4. [4] § STAR★METHODS › METHOD DETAILS › Pyramidal neuron model validation ↔ Code_data_generation/Cluster_cells_validation_protocols.py, lines 317–398 · score 0.79 · voltage attenuation, passive properties, pyramidal neurons, Rin, validated, curve
  5. [5] § STAR★METHODS › METHOD DETAILS › Input distribution ↔ Code_data_generation/Cluster_make_connectivity_higher_w_infield.py, lines 130–253 · score 0.77 · chosen place field, probability distribution, clustered synapses, bias, place tuned, cumulative
  6. [6] § STAR★METHODS › METHOD DETAILS › Inhibitory inputs ↔ Make_input/poisson_input.py, lines 417–522 · score 0.76 · filtered Poisson distributed, theta cycle, theta modulated, spike trains, firing rates, phase
  7. [7] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Multivariate random forest ↔ Code_data_generation/Code_data_analysis/Analysis_synapse_distribution_ML.py, lines 462–545 · score 0.76 · Min Max Scaler, Random Forest regression, predict, trained, model, protocols
  8. [8] § STAR★METHODS › METHOD DETAILS › Inhibitory inputs ↔ Make_input/poisson_input_interneurons.py, lines 41–124 · score 0.75 · theta cycle, theta modulated, Poisson distributed, spike trains, firing rates, interneuron
  9. [9] § RESULTS › Functional role of input clustering in place cell computation ↔ Figure_6.py, lines 65–106 · score 0.74 · pairwise Mann Whitney, Benjamini Hochberg, Kruskal Wallis, FDR, Figure 6
  10. [10] § RESULTS › Synaptic clustering amplifies dendritic gain and sustains calcium influx under noisy input regimes ↔ Figure_4.py, lines 383–398 · score 0.72 · dendritic firing rates, dendritic spiking, somatic spiking, dendritic activity, activated synapses, Figure 4
  11. [11] § RESULTS › From reconstruction of synaptic distribution to modeling of neuronal functional properties ↔ Code_data_generation/Cluster_cells_validation_protocols.py, lines 769–832 · score 0.69 · PVbasket, Ivy cells, neurogliaform, parvalbumin, NGF, Bistratified
  12. [12] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Place cell quantification ↔ Code_data_generation/Code_data_analysis/place_cell_metrics.py, lines 225–294 · score 0.69 · linear track, firing rate maps, field Firing, Place Field, metric, peak
  13. [13] § STAR★METHODS › METHOD DETAILS › Somato-dendritic parameter distributions ↔ Code_data_generation/Cluster_cells_validation_protocols.py, lines 317–398 · score 0.68 · CA1 pyramidal neurons, passive properties, integration properties, uniformly, apical, location
  14. [14] § RESULTS › From reconstruction of synaptic distribution to modeling of neuronal functional properties ↔ Code_data_generation/Cluster_make_connectivity.py, lines 393–435 · score 0.67 · PVbasket, Ivy cells, neurogliaform, parvalbumin, NGF, Bistratified
  15. [15] § STAR★METHODS › METHOD DETAILS › Inputs design ↔ Make_input/poisson_input.py, lines 417–522 · score 0.64 · Gaussian filtered Poisson, Poisson distributed, spike trains, firing rate, peak, noise
  16. [16] § RESULTS › Dendritic subdomain-specific strategies differentially shape spatial tuning ↔ Code_data_generation/Code_data_analysis/Analysis_synapse_distribution_ML.py, lines 462–545 · score 0.62 · squared error, random forest, RF, MSE, regression, predict
  17. [17] § RESULTS › Dendritic subdomain-specific strategies differentially shape spatial tuning ↔ Figure_7.py, lines 39–124 · score 0.59 · squared error, random forest, RF, MSE, variable, Figure 7
  18. [18] § STAR★METHODS › METHOD DETAILS › Input distribution ↔ Figure_5.py, lines 317–340 · score 0.55 · PV basket, CCK, Bistratified, Bezaire, apical, probabilities
  19. [19] § RESULTS › Synaptic clustering amplifies dendritic gain and sustains calcium influx under noisy input regimes ↔ Figure_4.py, lines 419–434 · score 0.52 · NMDA charge, NMDA spike, activated synapses, CI, Figure 4, shuffled
  20. [20] § RESULTS › From reconstruction of synaptic distribution to modeling of neuronal functional properties ↔ Figure_3.py, lines 285–332 · score 0.52 · Passive properties, sag ratio, resistance, models, Figure 3
  21. [21] § RESULTS › Domain-specific analysis of synaptic organization in CA1 PNs ↔ Code_data_generation/Code_data_analysis/Analysis_synapse_distribution_ML.py, lines 230–319 · score 0.51 · density distribution, co tuned, uniformly, subdomains, shuffling, volume
  22. [22] § RESULTS › From reconstruction of synaptic distribution to modeling of neuronal functional properties ↔ Figure_5.py, lines 224–272 · score 0.50 · excitatory theta oscillation, spike trains, interneuron, activity, Figure 5

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The authors' code

Python · 1,224 lines · 51 KB · GPL-3.0 · 4 matches

  1. # -*- coding: utf-8 -*-
  2. """
  3. Created on Tue Apr 23 11:39:07 2024
  5. @author: simon
  6. """
  8. from neuron import h
  9. from neuron.units import mV, ms
  10. from Class_CA1_Pyr_standardized import Cell
  11. import numpy as np
  12. import pickle
  13. import pandas as pd
  14. import os, sys
  15. import random
  16. from functools import wraps
  17. import pathlib
  20. # Get the path of the current script
  21. base_path = pathlib.Path().resolve().parent
  23. h.load_file("stdgui.hoc")
  24. h.load_file('stdlib.hoc') #mknrndll on anaconda prompt to compile the mod files
  25. h.load_file("stdrun.hoc")
  26. h.load_file('import3d.hoc')
  28. protocols = ['negative_current_soma', 'negative_current_dend',
  29. 'negative_current_cesium_bath_soma', 'negative_current_cesium_bath_dend',
  30. 'positive_current_soma', 'positive_current_dend', 'excitatory_synaptic_validation',
  31. 'NMDA_AMPA_ratio', 'inhibitory_synaptic_validation']
  33. protocol = protocols[0] #protocols[int(sys.argv[1])] #Use sys.argv to input parameters externally and loop through all protocols
  34. high_segmentation = True
  37. path = str(base_path / "Morphologies\\SWCs")+"\\"
  38. names = ["E_6_15_", "E_6_14_", "E_3_6_", "E_3_7_", "E_9_11_"]
  39. name = names[0] #names[int(sys.argv[2])] #Use sys.argv to input parameters externally and loop through all cells
  43. if protocol == 'dendritic_nonlinearity':
  44. hh_switch = False
  45. else:
  46. hh_switch = True
  48. ###################################################################################################################
  49. # F U N C T I O N D E F I N I T I O N
  50. ###################################################################################################################
  52. def find_closest_values(pd_serie, target_value):
  53. """
  54. pd_serie: pd.Series or a column of a dataframe.
  56. target_value: int or float that you want to find in the pd.Series/column.
  58. """
  60. idx = (pd_serie - target_value).abs().idxmin()
  61. return pd_serie.loc[idx]
  64. def input_resistance_calculation(voltage, time, stim_onset, stim_duration, inj_current):
  65. """
  66. voltage: np.array of the voltage trace
  67. time: np.array of the simulation time
  68. stim_onset: int when the stimulation starts
  69. stim_duration: int the duration of the current injection stim
  70. inj_current: amplitude of the injected current in nA
  72. """
  74. indx_v0 = np.abs(time - (stim_onset)).argmin()
  75. v0 = voltage[indx_v0]
  76. indx_v_max = np.abs(time - (stim_onset + stim_duration)).argmin()
  77. v_max= voltage[indx_v_max]
  78. R_Input= (np.abs((v_max-v0)))/np.abs(inj_current)
  80. return R_Input
  83. def sag_ratio_and_tau_calc(voltage, time, stim_onset, stim_duration, after_stim_duration, inj_current):
  84. """
  85. voltage: np.array of the voltage trace
  86. time: np.array of the simulation time
  87. stim_onset: int when the stimulation starts
  88. stim_duration: int the duration of the current injection stim
  89. after_stim_duration: int the duration of the simulation after the stimulus ends
  90. inj_current: amplitude of the injected current in nA
  92. """
  94. indx_v0 = np.abs(time - (stim_onset)).argmin()
  95. v0 = voltage[indx_v0]
  96. indx_v_max = np.abs(time - (stim_onset + stim_duration)).argmin()
  97. v_max= voltage[indx_v_max]
  98. v_peak = np.amax(np.abs(voltage))
  99. sag_ratio = (v_peak-np.abs(v_max))/(v_peak-np.abs(v0))
  101. v_decrease = abs(voltage[indx_v_max:])
  102. Diff_v = v_decrease-abs(voltage[np.abs(time - (stim_onset + stim_duration +
  103. after_stim_duration-1)).argmin()])
  104. Normalized_v = Diff_v/max(Diff_v)
  105. Calc_tau= time[indx_v_max + np.argmin(abs(Normalized_v-(1/np.e)))] - time[indx_v_max]
  107. return sag_ratio, Calc_tau
  110. def tau_rise_decay_calc(voltage, time, spike_onset, delay):
  111. """
  112. voltage: np.array of the voltage trace
  113. time: np.array of the simulation time
  114. stim_onset: int when the stimulation starts
  115. delay: delay of synaptic current from actual spiketime in ms
  117. """
  119. indx_v0 = np.abs(time - (spike_onset + delay)).argmin()
  120. v_psp = np.abs(voltage[indx_v0:] - voltage[indx_v0])
  121. time_psp = np.abs(time[indx_v0:] - time[indx_v0])
  122. indx_psp_peak = np.abs(v_psp).argmax()
  123. v_peak = v_psp[indx_psp_peak]
  125. v_decrease = v_psp[indx_psp_peak:]
  126. t_decrease = time_psp[indx_psp_peak:]
  127. Normalized_v_decrease = v_decrease/v_peak
  128. tau_decay = t_decrease[np.argmin(abs(Normalized_v_decrease-(1/np.e)))] - t_decrease[0]
  130. v_increase = v_psp[:indx_psp_peak]
  131. t_increase = time_psp[:indx_psp_peak]
  132. Normalized_v_increase = v_increase/v_peak
  133. tau_rise = t_increase[np.argmin(abs(Normalized_v_increase-0.9))] - t_increase[
  134. np.argmin(abs(Normalized_v_increase-0.1))]
  136. return tau_rise, tau_decay
  139. def timeit(func):
  140. @wraps(func)
  141. def wrapper(*args, **kwargs):
  142. start = time.perf_counter()
  143. func(*args, **kwargs)
  144. end = time.perf_counter()
  145. print(f"Time {start - end} sec")
  146. return wrapper
  148. # @timeit
  149. # def func1():
  150. # time.sleep(1)
  152. def calculate_segment_indexes(num_segments):
  153. if num_segments == 1:
  154. return [0.5]
  155. initial_segs = []
  156. final_segs = []
  157. segment_length = 1 / (num_segments)
  158. for i in range(int((num_segments-1)/2)):
  159. ind = i+1
  160. initial_segs.append(0.5-(segment_length*ind))
  161. final_segs.append(0.5+(segment_length*ind))
  162. inverted_initial_segs = initial_segs[::-1]
  163. indexes = inverted_initial_segs + [0.5] + final_segs
  164. return indexes
  166. ###################################################################################################################
  167. # C E L L S E T U P
  168. ###################################################################################################################
  170. if high_segmentation:
  171. segmentation = 0.001
  172. else:
  173. segmentation = 0.1
  176. c = Cell(path, name + "smooth_smaller_diam",'swc', 0,
  177. d_lambda = segmentation)
  180. if 'cesium_bath' in protocol:
  181. c.cesium_bath_present = 0.2
  182. cesium_protocol = 'cesium'
  183. else:
  184. c.cesium_bath_present = 1
  185. cesium_protocol = 'control'
  188. axon_group = c.axon
  189. trunk_group = c.apic
  190. oblique_group = c.dend
  191. tuft_group = c.dend_5
  192. basal_group = c.dend_7
  195. if protocol == 'NMDA_AMPA_ratio':
  196. # Channels inhibition (switch to false relevant channels) #################################
  197. for segment in c.soma:
  198. c.channel_setup('soma', segment, c.soma[0](1), hha2=False, pas=True, h=False,
  199. kap=False, km=False, cal=False, kca=False, mykca=False, cat=False,
  200. somacar=False)
  202. for segment in axon_group:
  203. c.channel_setup('axon', segment, c.soma[0](0), hha2=False, pas=True,
  204. km=False)
  206. for segment in trunk_group:
  207. c.channel_setup('trunk', segment, c.soma[0](1), hha_old=False, pas=True,
  208. h=False, kap=False, kad=False, km=False, cal=False, calH=False,
  209. car=False, kca=False, mykca=False, cat=False)
  211. for segment in oblique_group:
  212. c.channel_setup('oblique', segment, c.soma[0](1), hha_old=False, pas=True,
  213. h=False, kap=False, kad=False, km=False, calH=False, car=False, kca=False,
  214. mykca=False, cat=False, nap=False)
  216. for segment in tuft_group:
  217. c.channel_setup('tuft', segment, c.soma[0](1), hha_old=False, pas=True,
  218. h=False, kap=False, kad=False, km=False, calH=False, car=False, kca=False,
  219. mykca=False, cat=False, nap=False)
  221. for segment in basal_group:
  222. c.channel_setup('basal', segment, c.soma[0](1), hha2=False, pas=True,
  223. h=False, kap=False, kad=False)
  225. cyt, ca, cad = c.setup_molecules(c.all, cainf=5e-5, tau_ca=200, dCa=0.6)
  226. else:
  227. for segment in c.soma:
  228. c.channel_setup('soma', segment, c.soma[0](1), hha2=hh_switch, pas=True, h=True,
  229. kap=True, km=True, cal=True, kca=True, mykca=True, cat=True,
  230. somacar=True)
  232. for segment in axon_group:
  233. c.channel_setup('axon', segment, c.soma[0](0), hha2=hh_switch, pas=True,
  234. km=True)
  236. for segment in trunk_group:
  237. c.channel_setup('trunk', segment, c.soma[0](1), hha_old=True, pas=True,
  238. h=True, kap=True, kad=True, km=True, cal=True, calH=True,
  239. car=True, kca=True, mykca=True, cat=True)
  241. for segment in oblique_group:
  242. c.channel_setup('oblique', segment, c.soma[0](1), hha_old=True, pas=True,
  243. h=True, kap=True, kad=True, km=True, calH=True, car=True, kca=True,
  244. mykca=True, cat=True, nap=True)
  246. for segment in tuft_group:
  247. c.channel_setup('tuft', segment, c.soma[0](1), hha_old=True, pas=True,
  248. h=True, kap=True, kad=True, km=True, calH=True, car=True, kca=True,
  249. mykca=True, cat=True, nap=True)
  251. for segment in basal_group:
  252. c.channel_setup('basal', segment, c.soma[0](1), hha2=True, pas=True,
  253. h=True, kap=True, kad=True)
  255. cyt, ca, cad = c.setup_molecules(c.all, cainf=5e-5, tau_ca=200, dCa=0.6)
  258. ###################################################################################################################
  259. # M O N I T O R S
  260. ###################################################################################################################
  262. number_of_segments = 0
  263. cell_segment_names = {}
  264. for pp in c.all:
  265. cell_segment_names[f"{pp}"] = pp
  266. for segment_b in pp:
  267. number_of_segments += 1
  269. compartments = {}
  270. compartments_list = []
  271. comp_distances = np.zeros(len(c.all))
  272. for i, sec in enumerate(c.all):
  273. compartments[f"volt_{i}"] = h.Vector().record(sec(0.5)._ref_v)
  274. compartments[f"compartment_{i}"] = str(sec)
  275. compartments_list.append(sec)
  276. comp_distances[i] = c.fromtodistance(c.soma[0](1), sec(0.5))
  277. domain_list = [
  278. 'Trunk' if 'apic' in str(sec)
  279. else 'Basal' if 'dend_7' in str(sec)
  280. else 'Oblique' if 'dend[' in str(sec)
  281. else 'Tuft' if 'dend_5' in str(sec)
  282. else 'Soma' if 'soma' in str(sec)
  283. else 'Axon'
  284. for sec in c.all
  285. ]
  287. comp_df = pd.DataFrame(comp_distances, columns=['comp_distances'])
  288. comp_df['domain_list'] = domain_list
  290. apic_distances = np.zeros(len(c.apic))
  292. for i, el in enumerate(c.apic):
  293. distance = c.fromtodistance(c.soma[0](1), el(0.5))
  294. apic_distances[i] = distance
  295. index_238_apic = np.argmin(np.abs(apic_distances-238))
  296. voltage_238_apic = h.Vector().record(c.apic[index_238_apic](0.5)._ref_v)
  297. index_169_apic = np.argmin(np.abs(apic_distances-169))
  298. voltage_169_apic = h.Vector().record(c.apic[index_169_apic](0.5)._ref_v)
  299. index_30_apic = np.argmin(np.abs(apic_distances-30))
  301. voltage_soma = h.Vector().record(c.soma[0](0.5)._ref_v)
  302. time_ = h.Vector().record(h._ref_t)
  305. ###################################################################################################################
  306. # P R O T O C O L S
  307. ###################################################################################################################
  309. stim_onset = 300*ms
  310. stim_duration = 1000*ms
  311. after_stim_duration = 300*ms
  312. exp_R_input = 60 #Mohm From Magee (1998)
  314. ########################### NEGATIVE CURRENT ####################################################################
  317. if 'negative_current' in protocol:
  319. '''
  320. Postsynaptic integrative properties of dorsal CA1 pyramidal neuron subpopulations. Masurkar JNphys 2020.
  322. - Resting membrane potential was measured on break-in. The remaining measurements
  323. - were made after 2–5 min following break-in while the cell was maintained at −70 mV.
  324. - Input resistance Rin was measured by [...] −10-pA current injection.
  326. Arithmetic of Subthreshold Synaptic Summation in a Model CA1 Pyramidal Cell. Poirazi Neuron 2003
  328. - somatic and dendritic current injections, under control conditions (−200 pA,
  329. - solid curves) and with bath application of 3 mM Cs+ (−100 pA, dashed curves),
  330. - which we assumed blocked 80% of Ih. [...] current injections either at the
  331. - soma or 325 μm away in the main apical trunk.
  333. Factors mediating powerful voltage attenuation along CA1 pyramidal neuron dendrites. Golding J Physiol 2005
  335. -Figure 2, around 50% of attenuation at 238um distance from the soma.
  336. -The parameters that contribute to the high attenuation are: internal resistivity (high); non-uniform
  337. membrane resistivity (high in the soma, low in distal dendrites); non-uniform h conductance
  338. (low in the soma, high in distal dendrites).
  341. To verify the passive properties, run the simulations with the four combinations of
  342. stimulus location (comment/uncomment below) and cesium_bath presence (Ture/False above).
  343. After producing the four files, run the visualization code.
  344. '''
  346. if '_soma' in protocol:
  347. stim_location = 'Somatic_injection'
  348. I_negative_range = [-0.01, -0.2]
  349. elif '_dend' in protocol:
  350. stim_location = 'Dendritic_injection'
  351. I_negative_range = [-0.2]
  354. DATA = {}
  355. iclamps = []
  356. for i, I_negative in enumerate (I_negative_range):
  358. #Current injection
  359. if stim_location == 'Dendritic_injection':
  360. iclamps.append(h.IClamp(c.apic[index_238_apic](0.5)))
  361. else:
  362. iclamps.append(h.IClamp(c.soma[0](0.5)))
  365. iclamps[-1].delay = stim_onset
  366. iclamps[-1].dur = stim_duration
  367. iclamps[-1].amp = I_negative
  370. h.finitialize(c.v_init)
  371. h.fcurrent()
  372. h.continuerun(stim_onset + stim_duration + after_stim_duration)
  375. v_soma = np.array(voltage_soma)
  376. v_apic_238 = np.array(voltage_238_apic)
  377. time = np.array(time_)
  379. DATA[f'Voltage_soma_{I_negative}_{stim_location}'] = v_soma
  380. DATA[f'Voltage_apical_{I_negative}_{stim_location}'] = v_apic_238
  381. DATA[f'Time_{I_negative}_{stim_location}'] = time
  382. DATA[f'Compartments_{I_negative}_{stim_location}'] = compartments
  383. DATA["Inj_current_range"] = I_negative_range
  384. DATA["stim_onset"] = stim_onset
  385. DATA["stim_duration"] = stim_duration
  386. DATA["after_stim_duration"] = after_stim_duration
  387. DATA["comp_df"] = comp_df
  390. saving_path = str(base_path / f"Protocol_results\\Cell_validation\\{name}validation")
  392. if not os.path.exists(saving_path):
  393. os.makedirs(saving_path)
  395. with open(f"{saving_path}/{name}negative_current_{stim_location}_{cesium_protocol}.pkl", "wb") as file:
  396. pickle.dump(DATA, file, protocol=pickle.HIGHEST_PROTOCOL)
  398. comp_df.to_csv(r''+saving_path+'/'+name+'comp_df.txt', header=None, index=None, sep=' ', mode='a')
  400. ########################### POSITIVE CURRENT ####################################################################
  403. if 'positive_current' in protocol:
  405. '''
  406. Postsynaptic integrative properties of dorsal CA1 pyramidal neuron subpopulations. Masurkar JNphys 2020.
  408. - Firing frequency-input current (F-I) curves for each of these neurons [...] were produced
  409. by giving a range of current injections from 100 to 350 pA.
  413. '''
  416. DATA = {}
  418. if '_soma' in protocol:
  419. stim_location = 'Somatic_injection'
  420. I_positive_range = [0.02, 0.04, 0.06, 0.08, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5]
  421. elif '_dend' in protocol:
  422. stim_location = 'Dendritic_injection'
  423. I_positive_range = [-0.2, 0.2, 0.3, 0.4, 0.5, 0.8]
  426. for I_positive in I_positive_range:
  428. if stim_location == 'Somatic_injection':
  429. iclamps = h.IClamp(c.soma[0](0.5))
  430. else:
  431. iclamps = h.IClamp(c.apic[index_169_apic](0.5))
  432. iclamps.delay = stim_onset
  433. iclamps.dur = stim_duration
  434. iclamps.amp = I_positive
  436. h.finitialize(c.v_init)
  437. h.fcurrent()
  438. h.continuerun(stim_onset + stim_duration + after_stim_duration)
  440. v_soma = np.array(voltage_soma)
  441. v_apic_169 = np.array(voltage_169_apic)
  442. time = np.array(time_)
  444. DATA[f'Voltage_soma_{I_positive}'] = v_soma
  445. DATA[f'Voltage_apical_{I_positive}'] = v_apic_169
  446. DATA[f'Time_{I_positive}'] = time
  447. DATA[f'Compartments_{I_positive}'] = compartments
  448. DATA["Inj_current_range"] = I_positive_range
  449. DATA["stim_onset"] = stim_onset
  450. DATA["stim_duration"] = stim_duration
  451. DATA["after_stim_duration"] = after_stim_duration
  453. saving_path = str(base_path / f"Protocol_results/Cell_validation/{name}validation")
  455. if not os.path.exists(saving_path):
  456. os.makedirs(saving_path)
  458. with open(f"{saving_path}/{name}positive_current_{stim_location}.pkl", "wb") as file:
  459. pickle.dump(DATA, file, protocol=pickle.HIGHEST_PROTOCOL)
  462. ########################### EXCITATORY SYNAPSES VALIDATION ####################################################################
  465. if 'excitatory_synaptic_validation' in protocol:
  467. '''
  468. For apical inputs (Trunk, Schaffer collaterals), from "Somatic EPSP amplitude is independent of synapse
  469. location in hippocampal pyramidal neurons", Magee Narure Neuro 2000.
  471. - "EPSPs recorded simultaneously at the soma exhibited an average amplitude that was virtually independent of
  472. synapse location (Fig. 1e; ∼0.2 mV from 50 to 325 μm [...])"
  474. - "Somatic EPSP rise times show a more pronounced location dependence than do somatic EPSP decay times".
  475. Rise time goes from around 1ms from 100um syn activation to 2.5ms from 300um.
  476. Decay time goes from 20ms to 25ms in the same distance range.
  478. Basal inputs are inferred to have similar properties.
  479. '''
  480. compartments = {} #to remove not needed recordings for this validation protocol
  481. stim_duration = 0
  483. DATA = {}
  485. domains = ["Trunk_stimulation", "Basal_stimulation"] #
  486. input_distances = np.arange(50, 301, 25)
  488. # Synapse details
  489. volume_scaling = 1 #(synapse_list['Spine_Volume'][i]/e_syn_volume_average) * 0.6 + 0.4
  491. for stim_domain in domains:
  492. for distance in input_distances:
  495. # Create empty list for preallocation.
  496. syns, netcons = [], []
  497. inputs, vecstims = [], []
  500. if stim_domain == 'Trunk_stimulation':
  501. domain = c.apic
  502. elif stim_domain == 'Basal_stimulation':
  503. domain = c.dend_7
  504. if distance >= 200:
  505. # print('Skip distance for basal')
  506. continue
  508. sec_distances = []
  509. sections = []
  510. for i, el in enumerate(domain):
  511. for selected_section in el:
  512. distance_section = c.fromtodistance(c.soma[0](1), selected_section)
  513. sec_distances.append(distance_section)
  514. sections.append(selected_section)
  516. index_section = np.argmin(np.abs(np.array(sec_distances)-int(np.random.choice(
  517. np.arange(distance-5,distance+5),1))))
  518. # print('Synapse section', sections[index_section], 'distant', c.fromtodistance(
  519. # c.soma[0](1), sections[index_section]),'um from the soma')
  521. voltage_section = h.Vector().record(sections[index_section]._ref_v)
  523. syns.append(h.AmpaNmda(sections[index_section])) # add a synapse to the closest segment to the node that holds the synapse
  525. syns[-1].gamma_mg = 0.12
  526. syns[-1].eta_mg = 0.28011
  527. syns[-1].mg2o = 1.2
  528. syns[-1].half_mg =2.5 #4
  530. if stim_domain == 'Trunk_stimulation':
  532. #Setting AMPA conductance
  533. syns[-1].tau1_ampa = 0.61 # rise time
  534. syns[-1].tau2_ampa = 4.5 # decay time #??? Increasing AMPA with the distance causes a shortening of the decay time with the distance. Not biologically relevant!
  535. syns[-1].eampa = 0.0 # reversal potential NMDA
  536. syns[-1].gbar_ampa = (0.17e-3 * volume_scaling) + (distance * 3e-7)
  538. #Setting NMDA conductance
  539. syns[-1].tau1_nmda = 0.4 # rise time
  540. syns[-1].tau2_nmda = 43.0 # decay time
  541. syns[-1].enmda = 0.0 # reversal potential NMDA
  542. syns[-1].gbar_nmda = (0.50e-3 + (distance * 1.0e-6))
  544. elif stim_domain == 'Basal_stimulation':
  546. #Setting AMPA conductance
  547. syns[-1].tau1_ampa = 0.61 # rise time
  548. syns[-1].tau2_ampa = 4.5 # decay time
  549. syns[-1].eampa = 0.0 # reversal potential NMDA
  550. syns[-1].gbar_ampa = (0.14e-3 * volume_scaling) + (distance * 1.2e-7)
  552. #Setting NMDA conductance
  553. syns[-1].tau1_nmda = 14 # rise time
  554. syns[-1].tau2_nmda = 43.0 # decay time
  555. syns[-1].enmda = 0.0 # reversal potential NMDA
  556. syns[-1].gbar_nmda = (0.36e-3 + (distance * 1.0e-6))
  560. spiketimes = [stim_onset]
  561. inputs.append(h.Vector(spiketimes))
  563. vecstims.append(h.VecStim())
  564. vecstims[-1].play(inputs[-1]) # add the last created inputs to the last created VecStim
  566. pre_synaptic_input = vecstims[-1]
  568. netcons.append(h.NetCon(pre_synaptic_input, syns[-1]))
  569. netcons[-1].delay = 2.0 # delay in ms
  571. ampa_current = h.Vector().record(syns[-1]._ref_iampa)
  572. nmda_current = h.Vector().record(syns[-1]._ref_inmda)
  574. h.finitialize(c.v_init)
  575. h.fcurrent()
  576. h.continuerun(stim_onset + stim_duration + after_stim_duration)
  580. time = np.array(time_)
  581. v_soma = np.array(voltage_soma)
  582. v_section = np.array(voltage_section)
  584. DATA[f'Voltage_soma_{stim_domain}_{distance}um'] = v_soma
  585. DATA[f'Voltage_section_{stim_domain}_{distance}um'] = v_section
  586. DATA[f'Time_{stim_domain}_{distance}um'] = time
  587. DATA[f'Real_distance_{stim_domain}_{distance}um'] = c.fromtodistance(c.soma[0](1), sections[index_section])
  588. DATA["input_distances"] = input_distances
  589. DATA["domains"] = domains
  590. DATA["stim_onset"] = stim_onset
  591. DATA["stim_duration"] = stim_duration
  592. DATA["after_stim_duration"] = after_stim_duration
  595. saving_path = str(base_path / f"Protocol_results/Cell_validation/{name}validation")
  597. if not os.path.exists(saving_path):
  598. os.makedirs(saving_path)
  600. with open(f"{saving_path}/{name}syn_validation_amplitude_kinetic.pkl", "wb") as file:
  601. pickle.dump(DATA, file, protocol=pickle.HIGHEST_PROTOCOL)
  604. ########################### NMDA/AMPA RATIO ####################################################################
  607. if 'NMDA_AMPA_ratio' in protocol:
  609. '''
  610. NMDA/AMPA for Perforant Pathway synapses (on tuft) and Shaffer collaterals (on trunk)
  611. from "Pathway-Specific Properties of AMPA and NMDA-Mediated Transmission in CA1
  612. Hippocampal Pyramidal Cells", Otmakhova et al., 2002 J Neurosci.
  614. - The intracellular solution contained Cs+ to block most K+ channels and QX-314 to
  615. block Na+ channels, Ih channels, G-protein-coupled K+ (GABAB) channels and Ca2+
  616. currents (partially). Voltage clamp at -20 mV.
  618. - The NMDA/AMPA ratio was determined as the control area minus APV (NMDA antagonist)
  619. area, all divided by APV area (Fig. 4C, PP (8.6 +/- 0.8), SC input (5.3 +/- 0.9;)).
  621. - Basals inferred to be the same as the trunk.
  622. '''
  625. compartments = {} #to remove not needed recordings for this validation protocol
  626. stim_duration = 0
  628. DATA = {}
  630. domains = ["Trunk_stimulation", "Tuft_stimulation", "Basal_stimulation"]
  631. samples_n = 5
  632. nmda_inhibition = [True, False]
  634. # Synapse details
  635. volume_scaling = 1 #(synapse_list['Spine_Volume'][i]/e_syn_volume_average) * 0.6 + 0.4
  638. for stim_domain in domains:
  639. for samples in range(samples_n):
  641. if stim_domain == 'Trunk_stimulation':
  642. domain = c.apic
  643. elif stim_domain == 'Tuft_stimulation':
  644. domain = c.dend_5
  645. elif stim_domain == 'Basal_stimulation':
  646. domain = c.dend_7
  648. stimulated_branch = random.choice([branch for branch in domain])
  650. voltage_section = h.Vector().record(stimulated_branch(0.5)._ref_v)
  653. for nmda_inh in nmda_inhibition:
  655. if nmda_inh:
  656. APV = 0
  657. else:
  658. APV = 1
  661. # Create empty list for preallocation.
  662. syns, netcons = [], []
  663. inputs, vecstims = [], []
  665. syns.append(h.AmpaNmda(stimulated_branch(0.5)))
  666. syn_distance = c.fromtodistance(c.soma[0](1), stimulated_branch(0.5))
  669. if stim_domain == 'Trunk_stimulation':
  671. #Setting AMPA conductance
  672. syns[-1].tau1_ampa = 0.61 # rise time
  673. syns[-1].tau2_ampa = 4.5 # decay time
  674. syns[-1].eampa = 0.0 # reversal potential NMDA
  675. syns[-1].gbar_ampa = (0.17e-3 * volume_scaling) + (distance * 3e-7)
  677. #Setting NMDA conductance
  678. syns[-1].tau1_nmda = 14 # rise time
  679. syns[-1].tau2_nmda = 43.0 # decay time
  680. syns[-1].enmda = 0.0 # reversal potential NMDA
  681. syns[-1].gbar_nmda = APV * (0.50e-3 + (distance * 1.0e-6))
  683. elif stim_domain == 'Tuft_stimulation':
  685. #Setting AMPA conductance
  686. syns[-1].tau1_ampa = 0.61 # rise time
  687. syns[-1].tau2_ampa = 4.5 # decay time
  688. syns[-1].eampa = 0.0 # reversal potential NMDA
  689. syns[-1].gbar_ampa = 0.17e-3 * volume_scaling
  691. #Setting NMDA conductance
  692. syns[-1].tau1_nmda = 14 # rise time
  693. syns[-1].tau2_nmda = 43.0 # decay time
  694. syns[-1].enmda = 0.0 # reversal potential NMDA
  695. syns[-1].gbar_nmda = APV * 0.75e-3
  697. elif stim_domain == 'Basal_stimulation':
  699. #Setting AMPA conductance
  700. syns[-1].tau1_ampa = 0.61 # rise time
  701. syns[-1].tau2_ampa = 4.5 # decay time
  702. syns[-1].eampa = 0.0 # reversal potential NMDA
  703. syns[-1].gbar_ampa = (0.18e-3 * volume_scaling) + (distance * 1.2e-7)
  705. #Setting NMDA conductance
  706. syns[-1].tau1_nmda = 14 # rise time
  707. syns[-1].tau2_nmda = 43.0 # decay time
  708. syns[-1].enmda = 0.0 # reversal potential NMDA
  709. syns[-1].gbar_nmda = APV * (0.44e-3 + (distance * 1.0e-6))
  712. # Voltage clamp at -20 to reproduce experimental condition
  713. clampobj = h.SEClamp(c.soma[0](0.5))
  714. clampobj.dur1 = stim_onset + stim_duration + after_stim_duration
  715. clampobj.amp1 = -20
  716. clampobj.rs = 5
  718. clamp_current = h.Vector().record(clampobj._ref_i)
  721. spiketimes = [stim_onset]
  722. inputs.append(h.Vector(spiketimes))
  724. vecstims.append(h.VecStim())
  725. vecstims[-1].play(inputs[-1]) # add the last created inputs to the last created VecStim
  727. pre_synaptic_input = vecstims[-1]
  729. netcons.append(h.NetCon(pre_synaptic_input, syns[-1]))
  730. netcons[-1].delay = 2.0 # delay in ms
  732. ampa_current = h.Vector().record(syns[-1]._ref_iampa)
  733. nmda_current = h.Vector().record(syns[-1]._ref_inmda)
  735. h.finitialize(c.v_init)
  736. h.fcurrent()
  737. h.continuerun(stim_onset + stim_duration + after_stim_duration)
  741. time = np.array(time_)
  742. v_soma = np.array(voltage_soma)
  743. v_section = np.array(voltage_section)
  745. DATA[f'Voltage_soma_{stim_domain}_sample{samples}_{APV}'] = v_soma
  746. DATA[f'Voltage_section_{stim_domain}_sample{samples}_{APV}'] = v_section
  747. DATA[f'Time_{stim_domain}_sample{samples}_{APV}'] = time
  748. DATA["nmda_inhibition"] = nmda_inhibition
  749. DATA["domains"] = domains
  750. DATA["samples_n"] = samples_n
  751. DATA["stim_onset"] = stim_onset
  752. DATA["stim_duration"] = stim_duration
  753. DATA["after_stim_duration"] = after_stim_duration
  756. saving_path = str(base_path / f"Protocol_results/Cell_validation/{name}validation")
  758. if not os.path.exists(saving_path):
  759. os.makedirs(saving_path)
  761. with open(f"{saving_path}/{name}syn_validation_NMDA_AMPA_ratio.pkl", "wb") as file:
  762. pickle.dump(DATA, file, protocol=pickle.HIGHEST_PROTOCOL)
  766. ########################### INHIBITORY SYNAPSES VALIDATION ####################################################################
  769. if 'inhibitory_synaptic_validation' in protocol:
  771. '''
  772. - NGF: uIPSC average 4.9 pA current recorded in the soma after clamping at -50mV from
  773. "GABAB Receptor Modulation of Feedforward Inhibition through Hippocampal Neurogliaform
  774. Cells" T. Price 2008 JoN. (Voltage clamped at -62mV in model to stay below threshold,
  775. validated to ≈ half amplitude) Decay includes the GABAb component. Kinetics (rise time:
  776. 4.17 ± 0.24 ms; decay time: 36.5 ± 1.3 ms) from "Different transmitter transients
  777. underlie presynaptic cell type specificity of GABAA,slow and GABAA,fast" Szabadics
  778. et al 2007 PNAS
  780. - OLM: uIPSC average 2.6 pA current recorded in the soma after clamping at -70mV and
  781. kinetics (rise time: 6.2 ± 0.6 ms; decay time: 20.8 ± 1.7 ms) from "Cell surface
  782. domain specific postsynaptic currents evoked by identified GABAergic neurones in
  783. rat hippocampus in vitro" Maccaferri et al 2000 J. Physiol
  784. The actual amplitude in the paper is 26 pA but it would not make sense. The paper
  785. has been corrected but such correction is not available. Moreover, in "Dendritic
  786. inhibition mediated by O-LM and bistratified interneurons in the hippocampus"
  787. Müller et al Frontiers 2014 they cite Maccaferri claiming IPSC = 2.6 pA.
  790. - PVbasket: average 32.4 pA current (for superficialPC) recorded in the soma after
  791. clamping at -70mV with reversal at -26.3 from "Parvalbumin-Positive Basket
  792. Cells Differentiate Among Hippocampal Pyramidal Cells" Lee 2014 Neuron
  793. Kinetics (rise times: 0.53 ± 0.02 ms; decay time: 6.4 ± 0.33 ms, for fast
  794. spiking BC) from "Different transmitter transients underlie presynaptic cell
  795. type specificity of GABAA,slow and GABAA,fast" Szabadics et al 2007 PNAS
  797. - CCKbasket: average 42.9 pA current recorded in the soma after clamping at -70mV with
  798. reversal at -26.3 from "Parvalbumin-Positive Basket Cells Differentiate Among
  799. Hippocampal Pyramidal Cells" Lee 2014 Neuron
  800. Kinetics (rise times: 2.0 ± 0.8 ms; decay time: 8.15 ± 0.75 ms, average from
  801. two cases in TableS5) from "Ivy Cells: A Population of Nitric-Oxide-Producing,
  802. Slow-Spiking GABAergic Neurons and Their Involvement in Hippocampal Network
  803. Activity" P. Fuentealba 2008 Neuron
  805. - bistratified: average 0.8 mV current recorded in the soma after clamping at -50mV. Kinetics
  806. (rise times: 8.4 ± 3.2 ms; decay time: 42.1 ± 17.0 ms, average from two cases in
  807. Table2) from "Ivy Cells: A Population of Nitric-Oxide-Producing, Slow-Spiking
  808. GABAergic Neurons and Their Involvement in Hippocampal Network Activity" P. Fuentealba
  809. 2008 Neuron (Voltage clamped at -62mV in model to stay below threshold, validated to ≈
  810. half amplitude).
  813. - Ivy: average 8.0 pA current recorded in the soma after clamping at -50mV. Kinetics
  814. (rise times: 2.8 ± 0.2 ms; decay time: 16.05 ± 2.45 ms, average from two cases in
  815. TableS5) from "Ivy Cells: A Population of Nitric-Oxide-Producing, Slow-Spiking
  816. GABAergic Neurons and Their Involvement in Hippocampal Network Activity" P. Fuentealba
  817. 2008 Neuron (Voltage clamped at -62mV in model to stay below threshold, validated to
  818. ≈ half amplitude).
  820. '''
  822. stim_duration = 0
  824. DATA = {}
  826. presynaptic_interneurons = ["NGF", "OLM", "PVbasket", "CCKbasket", "bistratified", "Bistratified", "Ivy"]
  827. n_repetitions = 5
  831. for intern in presynaptic_interneurons:
  832. for n in range(n_repetitions):
  835. # Create empty list for preallocation.
  836. syns, netcons = [], []
  837. inputs, vecstims = [], []
  838. clampobj = []
  840. if intern == 'NGF':
  841. compartment_idx = random.choice(comp_df.loc[(comp_df['domain_list'] == 'Tuft')
  842. ].index)
  844. elif intern == 'OLM':
  845. compartment_idx = random.choice(comp_df.loc[(comp_df['domain_list'] == 'Tuft')
  846. ].index)
  848. elif intern == 'PVbasket':
  849. compartment_idx = random.choice(comp_df.loc[(comp_df['domain_list'] == 'Trunk') & (
  850. comp_df['comp_distances'] >= 80) & (comp_df['comp_distances'] <= 90)
  851. ].index)
  853. elif intern == 'CCKbasket':
  854. compartment_idx = random.choice(comp_df.loc[(comp_df['domain_list'] == 'Trunk') & (
  855. comp_df['comp_distances'] >= 150) & (comp_df['comp_distances'] <= 160)
  856. ].index)
  858. elif intern == 'bistratified':
  859. compartment_idx = random.choice(comp_df.loc[(comp_df['domain_list'] == 'Trunk') & (
  860. comp_df['comp_distances'] >= 95) & (comp_df['comp_distances'] <= 105)
  861. ].index)
  863. elif intern == 'Bistratified':
  864. compartment_idx = random.choice(comp_df.loc[(comp_df['domain_list'] == 'Trunk') & (
  865. comp_df['comp_distances'] >= 95) & (comp_df['comp_distances'] <= 105)
  866. ].index)
  868. elif intern == 'Ivy':
  869. compartment_idx = random.choice(comp_df.loc[(comp_df['domain_list'] == 'Basal') & (
  870. comp_df['comp_distances'] >= 85) & (comp_df['comp_distances'] <= 110)
  871. ].index)
  873. else:
  874. compartment_idx = random.choice(comp_df.loc[(comp_df['domain_list'] == 'Trunk') & (
  875. comp_df['comp_distances'] >= 95) & (comp_df['comp_distances'] <= 105)
  876. ].index)
  877. # print("Not a valid interneuron, using bistratified cells")
  879. stimulated_branch = compartments_list[compartment_idx]
  881. voltage_section = h.Vector().record(stimulated_branch(0.5)._ref_v)
  883. syn_distance = c.fromtodistance(c.soma[0](1), stimulated_branch(0.5))
  884. # print('Synapse section', stimulated_branch, 'distant', syn_distance,
  885. # 'um from the soma')
  886. syns.append(h.Exp2Syn(stimulated_branch(0.5)))
  890. if intern == 'NGF':
  892. syns[-1].tau1 = 1.17 # rise time
  893. syns[-1].tau2 = 36.5 # decay time #decay includes the GABAb component.
  894. syns[-1].e = -80 # reversal potential GABA #??? reverse potential should be -72mV for GABAa and -87mV for GABAb component. Since I cannot reproduce both components, I will set it in between the two.
  895. syn_weight = 0.6e-3
  898. clampobj.append(h.SEClamp(c.soma[0](0.5)))
  899. clampobj[-1].dur1 = stim_onset + stim_duration + after_stim_duration
  900. clampobj[-1].amp1 = -62
  901. clampobj[-1].rs = 5
  903. clamp_current = h.Vector().record(clampobj[-1]._ref_i)
  904. clamp_on = True
  906. elif intern == 'OLM':
  908. syns[-1].tau1 = 1. # rise time
  909. syns[-1].tau2 = 16. # decay time
  910. syns[-1].e = -80 # reversal potential GABA
  911. syn_weight = 0.62e-3
  914. clampobj.append(h.SEClamp(c.soma[0](0.5)))
  915. clampobj[-1].dur1 = stim_onset + stim_duration + after_stim_duration
  916. clampobj[-1].amp1 = -70
  917. clampobj[-1].rs = 5
  919. clamp_current = h.Vector().record(clampobj[-1]._ref_i)
  920. clamp_on = True
  922. elif intern == 'PVbasket':
  924. syns[-1].tau1 = 0.1 # rise time
  925. syns[-1].tau2 = 3.1 # decay time
  926. syns[-1].e = -26.3 # reversal potential GABA #??? high cloride in the pipet, they calculate this reversal
  927. syn_weight = 2.1e-3
  930. clampobj.append(h.SEClamp(c.soma[0](0.5)))
  931. clampobj[-1].dur1 = stim_onset + stim_duration + after_stim_duration
  932. clampobj[-1].amp1 = -70
  933. clampobj[-1].rs = 5
  935. clamp_current = h.Vector().record(clampobj[-1]._ref_i)
  936. clamp_on = True
  938. elif intern == 'CCKbasket':
  940. syns[-1].tau1 = 0.5 # rise time
  941. syns[-1].tau2 = 3. # decay time
  942. syns[-1].e = -26.3 # reversal potential GABA #??? high cloride in the pipet, they calculate this reversal
  943. syn_weight = 3.3e-3
  946. clampobj.append(h.SEClamp(c.soma[0](0.5)))
  947. clampobj[-1].dur1 = stim_onset + stim_duration + after_stim_duration
  948. clampobj[-1].amp1 = -70
  949. clampobj[-1].rs = 5
  951. clamp_current = h.Vector().record(clampobj[-1]._ref_i)
  952. clamp_on = True
  954. elif intern == 'bistratified':
  956. syns[-1].tau1 = 0.5 # rise time
  957. syns[-1].tau2 = 23.4 # decay time
  958. syns[-1].e = -80 # reversal potential GABA
  959. syn_weight = 3.5e-3 # Tuned in order to produce average 0.86 mV depolarization in the soma from "Dendritic inhibition mediated by O-LM and bistratified interneurons in the hippocampus" Muller 2014 FSN
  961. clamp_on = False
  963. elif intern == 'Bistratified':
  965. syns[-1].tau1 = 0.5 # rise time
  966. syns[-1].tau2 = 23.4 # decay time
  967. syns[-1].e = -80 # reversal potential GABA
  968. syn_weight = 3.5e-3 # Tuned in order to produce average 0.86 mV depolarization in the soma from "Dendritic inhibition mediated by O-LM and bistratified interneurons in the hippocampus" Muller 2014 FSN
  971. clampobj.append(h.SEClamp(c.soma[0](0.5)))
  972. clampobj[-1].dur1 = stim_onset + stim_duration + after_stim_duration
  973. clampobj[-1].amp1 = -70
  974. clampobj[-1].rs = 5
  976. clamp_current = h.Vector().record(clampobj[-1]._ref_i)
  977. clamp_on = True
  979. elif intern == 'Ivy':
  981. syns[-1].tau1 = 0.4 # rise time
  982. syns[-1].tau2 = 10.8 # decay time
  983. syns[-1].e = -80 # reversal potential GABA
  984. syn_weight = 0.8e-3
  987. clampobj.append(h.SEClamp(c.soma[0](0.5)))
  988. clampobj[-1].dur1 = stim_onset + stim_duration + after_stim_duration
  989. clampobj[-1].amp1 = -62
  990. clampobj[-1].rs = 5
  992. clamp_current = h.Vector().record(clampobj[-1]._ref_i)
  993. clamp_on = True
  995. else:
  997. syns[-1].tau1 = 0.5 # rise time
  998. syns[-1].tau2 = 23.4 # decay time
  999. syns[-1].e = -80 # reversal potential GABA
  1000. syn_weight = 3.5e-3 # Tuned in order to produce average 0.86 mV depolarization in the soma from "Dendritic inhibition mediated by O-LM and bistratified interneurons in the hippocampus" Muller 2014 FSN
  1002. clamp_on = False
  1004. # print("Not a valid interneuron, using bistratified cells")
  1007. spiketimes = [stim_onset]
  1008. inputs.append(h.Vector(spiketimes))
  1010. vecstims.append(h.VecStim())
  1011. vecstims[-1].play(inputs[-1]) # add the last created inputs to the last created VecStim
  1013. pre_synaptic_input = vecstims[-1]
  1015. netcons.append(h.NetCon(pre_synaptic_input, syns[-1]))
  1016. netcons[-1].delay = 2.0 # delay in ms
  1018. netcons[-1].weight[0] = syn_weight
  1019. current = h.Vector().record(syns[-1]._ref_i)
  1021. h.finitialize(c.v_init)
  1022. h.fcurrent()
  1023. h.continuerun(stim_onset + stim_duration + after_stim_duration)
  1025. time = np.array(time_)
  1026. v_soma = np.array(voltage_soma)
  1027. v_section = np.array(voltage_section)
  1028. I_syn = np.array(current)
  1030. if clamp_on == True:
  1031. DATA[f'Clamp_current_{intern}_syn_{n}'] = np.array(clamp_current)
  1032. DATA[f'Clamp_on_{intern}_{n}'] = clamp_on
  1033. DATA[f'Voltage_soma_{intern}_syn_{n}'] = v_soma
  1034. DATA[f'Voltage_section_{intern}_syn_{n}'] = v_section
  1035. DATA[f'Time_{intern}_syn_{n}'] = time
  1036. DATA[f'Synaptic_current_{intern}_syn_{n}'] = I_syn
  1037. DATA["interneurons"] = presynaptic_interneurons
  1038. DATA["n_repetitions"] = n_repetitions
  1039. DATA["stim_onset"] = stim_onset
  1040. DATA["stim_duration"] = stim_duration
  1041. DATA["after_stim_duration"] = after_stim_duration
  1044. saving_path = str(base_path / f"Protocol_results/Cell_validation/{name}validation")
  1046. if not os.path.exists(saving_path):
  1047. os.makedirs(saving_path)
  1049. with open(f"{saving_path}/{name}interneurons_validation_amplitude.pkl", "wb") as file:
  1050. pickle.dump(DATA, file, protocol=pickle.HIGHEST_PROTOCOL)
  1053. ########################### DENDRITIC NONLINEARITY PROTOCOL ####################################################################
  1055. if 'dendritic_nonlinearity' in protocol:
  1057. '''
  1058. Tuning the NMDA and the dendritic sodium to validate dendritic non-linearity from
  1059. "Integrative Properties of Radial Oblique Dendrites in Hippocampal CA1 Pyramidal Neurons",
  1060. Attila Losonczy, Jeffrey C. Magee, 2006 Neuron.
  1062. E_6_14:
  1063. Oblique 51, measured EPSP supralinear with 25 synapses to about 8 mV, dV/dt up to 3.5 mV/ms.
  1064. '''
  1066. stimulated_domain = 'Oblique'
  1067. ind_section = 6
  1068. synapse_number = [1, 4, 8, 12, 15, 18, 22, 25, 28, 30, 32, 35]
  1069. NMDA_switch = 1
  1071. if stimulated_domain == 'Tuft':
  1072. selected_branch = tuft_group[ind_section]
  1073. elif stimulated_domain == 'Trunk':
  1074. selected_branch = trunk_group[ind_section]
  1075. elif stimulated_domain == 'Oblique':
  1076. selected_branch = oblique_group[ind_section]
  1077. elif stimulated_domain == 'Basal':
  1078. selected_branch = basal_group[ind_section]
  1080. selected_branch_apic = c.apic[np.argmax(apic_distances)] #index_169_apic] # selected_branch #
  1081. ICa_apic = h.Vector().record(selected_branch_apic(0.5)._ref_ica)
  1082. ICa_apic_car = h.Vector().record(selected_branch_apic(0.5)._ref_ica_car)
  1083. ICa_apic_calh = h.Vector().record(selected_branch_apic(0.5)._ref_ica_calH)
  1084. Ikad_apic = h.Vector().record(selected_branch_apic(0.5)._ref_ik_kad) #c.apic[index_238_apic]
  1085. Ikap_apic = h.Vector().record(selected_branch_apic(0.5)._ref_ik_kap)
  1086. Ikm_apic = h.Vector().record(selected_branch_apic(0.5)._ref_ik_km)
  1087. Ikca_apic = h.Vector().record(selected_branch_apic(0.5)._ref_ik_kca)
  1088. Imykca_apic = h.Vector().record(selected_branch_apic(0.5)._ref_ik_mykca)
  1089. IK_apic = h.Vector().record(selected_branch_apic(0.5)._ref_ik_kvs)
  1090. INa_apic = h.Vector().record(selected_branch_apic(0.5)._ref_ina_nas)
  1091. act_Na_par = h.Vector().record(selected_branch_apic(0.5)._ref_m_nas)
  1092. inact_Na_par = h.Vector().record(selected_branch_apic(0.5)._ref_h_nas)
  1093. act_K_par = h.Vector().record(selected_branch_apic(0.5)._ref_n_kvs)
  1097. DATA = {}
  1098. distances = []
  1101. DATA['time'] = []
  1102. DATA['v_soma'] = []
  1103. DATA['v_section'] = []
  1104. DATA['derivative_soma'] = []
  1105. DATA['depol_soma'] = []
  1107. stim_onset = 150*ms
  1108. stim_duration = 10*ms
  1109. after_stim_duration = 100*ms
  1112. for n_syns in synapse_number:
  1114. selected_branch.nseg = 201
  1115. seg_id = calculate_segment_indexes(201)[101:]
  1116. segs_with_syns = seg_id[:n_syns]
  1118. # Create empty list for preallocation.
  1119. syns, netcons = [], []
  1120. inputs, vecstims = [], []
  1122. diameter = selected_branch.diam
  1123. length = selected_branch.L
  1125. voltage_section = h.Vector().record(selected_branch(0.5)._ref_v)
  1127. for i_s, segs in enumerate(segs_with_syns):
  1129. stim_dend = selected_branch(segs)
  1131. distances.append(c.fromtodistance(c.soma[0](1), stim_dend))
  1132. distance = distances[-1]
  1134. syns.append(h.AmpaNmda(stim_dend))
  1136. syns[-1].gamma_mg = 0.12
  1137. syns[-1].eta_mg = 0.28011
  1138. syns[-1].mg2o = 1.2
  1139. syns[-1].half_mg =2.5 #4
  1141. if stimulated_domain == 'Trunk' or stimulated_domain == 'Oblique':
  1143. #Setting AMPA conductance
  1144. syns[-1].tau1_ampa = 0.61 # rise time
  1145. syns[-1].tau2_ampa = 4.5 # decay time #??? Increasing AMPA with the distance causes a shortening of the decay time with the distance. Not biologically relevant!
  1146. syns[-1].eampa = 0.0 # reversal potential NMDA
  1147. syns[-1].gbar_ampa = 0.17e-3 + (distance * 3e-7)
  1149. #Setting NMDA conductance
  1150. syns[-1].tau1_nmda = 2.4 # rise time
  1151. syns[-1].tau2_nmda = 43.0 # decay time
  1152. syns[-1].enmda = 0.0 # reversal potential NMDA
  1153. syns[-1].gbar_nmda = (0.50e-3 + (distance * 1e-6)) * NMDA_switch
  1156. elif stimulated_domain == 'Tuft':
  1158. #Setting AMPA conductance
  1159. syns[-1].tau1_ampa = 0.61 # rise time
  1160. syns[-1].tau2_ampa = 4.5 # decay time
  1161. syns[-1].eampa = 0.0 # reversal potential AMPA/NMDA
  1162. syns[-1].gbar_ampa = 0.06e-3
  1164. #Setting NMDA conductance
  1165. syns[-1].tau1_nmda = 2.4 # rise time
  1166. syns[-1].tau2_nmda = 43. # decay time
  1167. syns[-1].enmda = 0.0 # reversal potential AMPA/NMDA
  1168. syns[-1].gbar_nmda = (0.6e-3) * NMDA_switch
  1171. elif stimulated_domain == 'Basal':
  1173. #Setting AMPA conductance
  1174. syns[-1].tau1_ampa = 0.61 # rise time
  1175. syns[-1].tau2_ampa = 4.5 # decay time
  1176. syns[-1].eampa = 0.0 # reversal potential NMDA
  1177. syns[-1].gbar_ampa = (0.14e-3 + (distance * 1.2e-7))
  1179. #Setting NMDA conductance
  1180. syns[-1].tau1_nmda = 2.4 # rise time
  1181. syns[-1].tau2_nmda = 43.0 # decay time
  1182. syns[-1].enmda = 0.0 # reversal potential NMDA
  1183. syns[-1].gbar_nmda = (0.36e-3 + (distance * 1e-6)) * NMDA_switch
  1187. spiketimes = [stim_onset + 0.1*i_s]
  1188. inputs.append(h.Vector(spiketimes))
  1190. vecstims.append(h.VecStim())
  1191. vecstims[-1].play(inputs[-1]) # add the last created inputs to the last created VecStim
  1193. pre_synaptic_input = vecstims[-1]
  1195. netcons.append(h.NetCon(pre_synaptic_input, syns[-1]))
  1196. netcons[-1].delay = 2.0 # delay in ms
  1198. ampa_current = h.Vector().record(syns[-1]._ref_iampa)
  1199. nmda_current = h.Vector().record(syns[-1]._ref_inmda)
  1201. h.finitialize(c.v_init)
  1202. h.fcurrent()
  1203. h.continuerun(stim_onset + stim_duration + after_stim_duration)
  1205. time = np.array(time_)
  1206. v_soma = np.array(voltage_soma)
  1207. v_section = np.array(voltage_section)
  1208. dist = distances[0]
  1210. DATA['time'].append(time)
  1211. DATA['v_soma'].append(v_soma)
  1212. DATA['v_section'].append(v_section)
  1213. DATA['derivative_soma'].append(max(np.gradient(v_soma, time)))
  1214. indx_v0 = np.abs(time - (stim_onset)).argmin()
  1215. DATA['depol_soma'].append(max(np.abs(v_soma[indx_v0:]-v_soma[indx_v0-1])))
  1218. saving_path = str(base_path / f"Protocol_results/Cell_validation/{name}validation")
  1220. if not os.path.exists(saving_path):
  1221. os.makedirs(saving_path)
  1223. with open(f"{saving_path}/{name}dendritic_nonlinearity.pkl", "wb") as file:
  1224. pickle.dump(DATA, file, protocol=pickle.HIGHEST_PROTOCOL)

Cluster_cells_validation_protocols.py at commit 77cf670, under GPL-3.0 · at the source

Overview

Authors: Simone Tasciotti1,2, Daniel Maxim Iascone3,4, Spyridon Chavlis2, Luke A Hammond3,5, Yardena Katz3, Attila Losonczy3,6,7, Franck Polleux3,6, Panayiota Poirazi2,8
ORCID iDs: Daniel Maxim Iascone, Panayiota Poirazi
  1. Department of Biology, University of Crete, Heraklion, Greece
  2. Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, Greece
  3. Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
  4. Present address: Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA
  5. Present address: Department of Neurology, The Ohio State University, Wexner Medical School, Columbus, Ohio, USA
  6. Department of Neuroscience, Columbia University, New York, NY, USA
  7. Present address: O’Donnell Brain Institute at UT Southwestern Medical Center, Harry Hines Blvd. Dallas, Texas, USA
  8. Lead contact
Institutions: University of Crete (Greece); Foundation for Research and Technology Hellas (Greece); Mortimer B. Zuckerman Mind Brain Behavior Institute (United States); Columbia University (United States); FORTH Institute of Molecular Biology and Biotechnology (Greece)
Journal: Cell reports, volume 45, issue 8, article 117793
Dates: published online 5 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.celrep.2026.117793 · PMID 42560818 · PMCID PMC13586326 · OpenAlex W7172502162
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), none (in silico) (organism), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Machine learning, Preprocessing, Evoked potentials, Graphs, Single-unit activity, calcium imaging, Smoothing, state filtering, decompositions
Keywords: Morphology, CA1, Pyramidal Neuron, Computational Efficiency, Biophysical Model, Dendritic Computation, Synaptic Clustering, Dendritic Nonlinearity, Cp: Neuroscience, Dendritic Domains
Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: NINDS NIH HHS (R01 NS121106, R01 NS133381, F31 NS101820, R01 NS131728, R35 NS127232, U01 NS115530, RF1 NS133381); National Institute of Neurological Disorders and Stroke (R01NS131728, U01NS115530, R01NS121106, R35 NS127232); Stavros Niarchos Foundation; NIDDK NIH HHS (K99 DK142063); Horizon 2020 Framework Programme; NIMH NIH HHS (R01 MH124867, F32 MH125600, R01 MH124047); NIA NIH HHS (RF1 AG080818); Hellenic Foundation for Research and Innovation (28056, COFLEX 14941 HFRI P.N. 80147/17.1.24); National Institutes of Health Blueprint for Neuroscience Research (F31NS101820, R01MH124867); European Commission (014941); BRAIN Initiative (R01NS133381); National Institutes of Health (F32MH125600, K99DK142063); National Institute of Mental Health (R01MH124047); NOMIS Stiftung; EU Framework Programme for Research and Innovation Marie Skłodowska-Curie Actions (860949); National Institute on Aging (RF1AG080818); National Institute of Mental Health and Neurosciences
Citations: not cited yet (Europe PMC); 150 references in the paper
Research resources: Mouse: C57BL/6 RRID:IMSR_JAX:000664

Abstract

How the spatial arrangement of synaptic inputs shapes neuronal feature selectivity remains a fundamental question. Here, we map the three-dimensional distribution of excitatory and inhibitory synapses across the dendritic arbor of CA1 pyramidal neurons in vivo and build biophysical models to probe their impact on place-cell emergence. Excitatory synapses are non-uniformly distributed, forming structural clusters preferentially on terminal apical and basal dendrites, whereas inhibitory synapses are uniformly arranged. Relative to dispersed configurations, clustered inputs generate higher-quality, stable place fields while recruiting ~13% fewer active synapses for equivalent somatic output, and cause elevated voltage-gated calcium influx and NMDA-receptor activation. Notably, disrupting clustering permits recovery of somatic excitability but not dendritic calcium dynamics, implicating clustering in calcium-dependent plasticity. Synaptic organization further determines integration strategy: clustered inputs preferentially engage apical dendritic nonlinearities, whereas distributed inputs rely on basal summation. These results establish synaptic clustering as a core mechanism for efficient, compartmentalized spatial computation.

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

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Its files are read in the Code ↔ Paper reader above, with 22 matches between paragraphs and lines of code.

Zenodo 20738191

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the resources table
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NEURON (36 files), NumPy (31 files), pandas (24 files), SciPy (19 files), Matplotlib (16 files), seaborn (11 files), Pingouin (4 files), Brian 2 (2 files), SHAP (2 files), scikit-learn (1 file), statannotations (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
64 files
At the source:

zenodo.com

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: the link answers
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Found in: the text, “Footnotes”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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At the source: Zenodo.com

poirazi-lab/tasciotti_et_al_2026

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 77cf67010d0e6bce4e83a891fb6a49318b5b0ac4, 17 June 2026
Languages: Python (33), NEURON (29)
Size: 67 files, 62 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NEURON (36 files), NumPy (31 files), pandas (24 files), SciPy (19 files), Matplotlib (16 files), seaborn (11 files), Pingouin (4 files), Brian 2 (2 files), SHAP (2 files), scikit-learn (1 file), statannotations (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
64 files
At the source: github.com/Poirazi-Lab/Tasciotti_et_al_2026/tree/v1.0.0

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Version 3, 28 September 2026

  • Publisher: — → Cell Press

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 10 keywords, 17 funders, 148 references, 1 RRID.

Cite

This paper

Tasciotti, S., Iascone, D. M., Chavlis, S., Hammond, L. A., Katz, Y., Losonczy, A., Polleux, F., & Poirazi, P. (2026). Clustered inputs engage dendritic nonlinearities and calcium signaling to support efficient place-field formation in CA1 pyramidal neurons. Cell reports, 45(8), 117793. https://doi.org/10.1016/j.celrep.2026.117793

BibTeX

@article{tasciotti2026clustered,
author = {Tasciotti, Simone and Iascone, Daniel Maxim and Chavlis, Spyridon and Hammond, Luke A and Katz, Yardena and Losonczy, Attila and Polleux, Franck and Poirazi, Panayiota},
title = {{Clustered inputs engage dendritic nonlinearities and calcium signaling to support efficient place-field formation in CA1 pyramidal neurons}},
journal = {Cell reports},
year = {2026},
month = aug,
volume = {45},
number = {8},
pages = {117793},
publisher = {Cell Press},
issn = {2211-1247},
doi = {10.1016/j.celrep.2026.117793},
url = {https://doi.org/10.1016/j.celrep.2026.117793},
pmid = {42560818},
pmcid = {PMC13586326}
}

RIS

TY - JOUR
AU - Tasciotti, Simone
AU - Iascone, Daniel Maxim
AU - Chavlis, Spyridon
AU - Hammond, Luke A
AU - Katz, Yardena
AU - Losonczy, Attila
AU - Polleux, Franck
AU - Poirazi, Panayiota
TI - Clustered inputs engage dendritic nonlinearities and calcium signaling to support efficient place-field formation in CA1 pyramidal neurons
T2 - Cell reports
J2 - Cell Rep
PY - 2026
DA - 2026/08/05
VL - 45
IS - 8
SP - 117793
SN - 2211-1247
PB - Cell Press
DO - 10.1016/j.celrep.2026.117793
UR - https://doi.org/10.1016/j.celrep.2026.117793
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.celrep.2026.117793",
"type": "article-journal",
"title": "Clustered inputs engage dendritic nonlinearities and calcium signaling to support efficient place-field formation in CA1 pyramidal neurons",
"container-title": "Cell reports",
"author": [
{
"family": "Tasciotti",
"given": "Simone"
},
{
"family": "Iascone",
"given": "Daniel Maxim"
},
{
"family": "Chavlis",
"given": "Spyridon"
},
{
"family": "Hammond",
"given": "Luke A"
},
{
"family": "Katz",
"given": "Yardena"
},
{
"family": "Losonczy",
"given": "Attila"
},
{
"family": "Polleux",
"given": "Franck"
},
{
"family": "Poirazi",
"given": "Panayiota"
}
],
"container-title-short": "Cell Rep",
"volume": "45",
"issue": "8",
"page": "117793",
"DOI": "10.1016/j.celrep.2026.117793",
"PMID": "42560818",
"PMCID": "PMC13586326",
"ISSN": "2211-1247",
"publisher": "Cell Press",
"URL": "https://doi.org/10.1016/j.celrep.2026.117793",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
5
]
]
}
}

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