How to train your neuron: developing a detailed, up-to-date, multipurpose model of hippocampal CA1 pyramidal cells

Code ↔ Paper

10 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 10 matches · 1 of them tie a paragraph to a whole file without given lines: a weak match, whose lines are not tinted

[1] § Methods › Tools › Model testing ↔ CA1_pyramidal_cell_codes_and_scripts.zip/model_comparison_SOMA_optimizer_multirun_AbsFeatures_subplots_new_style.py, lines 160 to 248 · score 0.81 · AP width, AP rise, voltage deflection, sag amplitude, AP amplitude, spiking · Report a mismatch
[2] § Methods › Tools › Model testing ↔ CA1_pyramidal_cell_codes_and_scripts.zip/model_comparison_SOMA_optimizer_multirun_AbsFeatures_subplots_new_style_2_sets.py, lines 199 to 271 · score 0.81 · AP width, AP rise, voltage deflection, sag amplitude, AP amplitude, spiking · Report a mismatch
[3] § Results › Model validation › Finding the correct voltage dependence of the NMDA receptor model using the oblique integration test ↔ CA1_pyramidal_cell_codes_and_scripts.zip/model_comparison_oblique_integration.py, lines 406 to 489 · score 0.65 · NMDA receptor, voltage dependence, Oblique Integration, Gao, Grunditz, Larkum · Report a mismatch
[4] § Results › Model validation › Finding the correct voltage dependence of the NMDA receptor model using the oblique integration test ↔ CA1_pyramidal_cell_codes_and_scripts.zip/model_comparison_oblique_integration.py, lines 406 to 489 · score 0.62 · Jahr Stevens, NMDA receptor, voltage dependence, Oblique Integration, CA1 · Report a mismatch
[5] § Methods › Model building › Morphology ↔ CA1_pyramidal_cell_model/we_want_spines_fully_active_ghk_50_Ra_split_head.hoc, lines 1 to 60 · score 0.61 · spine density, basal dendrites, neck, diameter, head, um · Report a mismatch
[6] § Results › Overview of the model-building process ↔ CA1_pyramidal_cell_codes_and_scripts.zip/plot_parameters3.py, lines 395 to 465 · score 0.56 · SK density, Kdr density, KD density, permeability, Ih, soma · Report a mismatch
[7] § Methods › Tools › Model testing ↔ hippounit/capabilities/cap_ReceivesEPSCstim.py, the whole file · a weak match · score 0.54 · excitatory post synaptic, EPSC, stimuli, locations, soma, dendritic · Report a mismatch
[8] § Results › Models of voltage-gated ion channels ↔ CA1_pyramidal_cell_codes_and_scripts.zip/plotting_model_traces_blocking.py, lines 122 to 181 · score 0.53 · voltage gated potassium, delayed rectifier, potassium channels, calcium dependent · Report a mismatch
[9] § Methods › Model building › Channel models › Potassium channels › Slow Afterhyperpolarization potassium (K-AHP) current ↔ CA1_pyramidal_cell_codes_and_scripts.zip/plotting_model_traces_blocking.py, lines 122 to 181 · score 0.53 · calcium dependent potassium, conductance calcium dependent, CA1 pyramidal cells, potassium channels, calcium channels, SK · Report a mismatch
[10] § Methods › Model building › Channel models › Potassium channels › A-type potassium channels ↔ CA1_pyramidal_cell_codes_and_scripts.zip/shaft_spine_compare_statistics_for_paper_plot.py, lines 49 to 97 · score 0.51 · proximal dendritic, distal dendritic, shifted, soma, cell · Report a mismatch

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The authors' code

Python · 489 lines · 19 KB · CC-BY-4.0 · 2 matches

import numpy
import matplotlib
import matplotlib.pyplot as plt
try:
import cPickle as pickle
except:
import pickle
import gzip
import math
import os
import json
import collections
import quantities
data_dict=collections.OrderedDict()
base_dir= '/mnt/csoport31-2/Modellezo_csapat/CA1_pyramidal/Spine_project_validation_results60/Basic_set_newopt/Active_spine_F_factor_with_R/For_NMDA_compare/'
save_dir = '/mnt/csoport31-2/Modellezo_csapat/CA1_pyramidal/Spine_project_validation_results60/Basic_set_newopt/Active_spine_F_factor_with_R/For_NMDA_compare/'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
for method in os.listdir(base_dir):
data_dict[method]={}
for models in os.listdir(base_dir + method + '/results/oblique_integration/'):
print(models)
data_dict[method][models]={}
data_dict[method][models]['sync_EPSP_amps']=pickle.load(gzip.GzipFile(base_dir + method + '/results/oblique_integration/'+models+'/oblique_model_epsp_amps_sync.p', "rb"))
data_dict[method][models]['async_EPSP_amps']=pickle.load(gzip.GzipFile(base_dir + method + '/results/oblique_integration/'+models+'/oblique_model_epsp_amps_async.p', "rb"))
data_dict[method][models]['sync_peak_derivs']=pickle.load(gzip.GzipFile(base_dir + method + '/results/oblique_integration/'+models+'/oblique_model_mean_peak_derivs_sync.p', "rb"))
data_dict[method][models]['async_peak_derivs']=pickle.load(gzip.GzipFile(base_dir + method + '/results/oblique_integration/'+models+'/oblique_model_mean_peak_derivs_async.p', "rb"))
with open(base_dir + method + '/results/oblique_integration/'+models+'/p_values.json') as f:
data_dict[method][models]['p_values']=json.load(f, object_pairs_hook=collections.OrderedDict)
with open(base_dir + method + '/results/oblique_integration/'+models+'/oblique_model_features.json') as f:
data_dict[method][models]['features']=json.load(f, object_pairs_hook=collections.OrderedDict)
with open(base_dir + method + '/results/oblique_integration/'+models+'/oblique_model_errors.json') as f:
data_dict[method][models]['errors']=json.load(f, object_pairs_hook=collections.OrderedDict)
# break
#print(data_dict)
#Loading observation - this contains the SD values not the SEM
data=pickle.load(gzip.GzipFile(base_dir + method + '/results/oblique_integration/'+models+'/oblique_features.p', "rb"))
observation = data[0]
#names = ['All spines modeled explicitly', 'Input recieving spines modeled explicitly', 'Spines modeled with F-factor']
for key, val in observation.items():
try:
observation[key] = float(val)
except ValueError:
quantity_parts = val.split(" ")
number = float(quantity_parts[0])
units = " ".join(quantity_parts[1:])
observation[key] = quantities.Quantity(number, units)
try:
colormap = plt.cm.spectral #http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html
except:
colormap = plt.cm.nipy_spectral
colors = colormap(numpy.linspace(0.1, 0.9, len(data_dict.keys())))
#input - output curves
#fig, axs = plt.subplots(3, len(data_dict.keys())//2)
fig, axs = plt.subplots(3, 3)
axs=axs.flatten()
for i, method in enumerate(data_dict.keys()):
for j, models in enumerate(data_dict[method].keys()):
axs[i].errorbar(data_dict[method][models]['sync_EPSP_amps']['all_expected_mean'],data_dict[method][models]['sync_EPSP_amps']['all_measured_mean'], yerr=data_dict[method][models]['sync_EPSP_amps']['all_sd'], marker='o', color=colors[i], elinewidth = 0.2)
axs[i].plot([0,8],[0, 8], linestyle="--")
axs[i].set_xlabel('expected EPSP (mV)', fontsize = 14)
axs[i].set_ylabel('measured EPSP (mV)', fontsize = 14)
#axs[i].set_ylim(-0,10)
#axs[i].set_xlim(-0,10)
axs[i].set_title(method, fontsize = 14)
fig.suptitle('Summary plot of mean input-output curves for synchronous inputs', fontsize = 20)
print(fig.get_size_inches())
fig.set_size_inches(10,9)
plt.tight_layout()
plt.savefig(save_dir + 'oblique_sync_input-output_all.svg', bbox_inches='tight')
# peak derivative plots
#fig2, axs2 = plt.subplots(3, len(data_dict.keys())//2)
fig2, axs2 = plt.subplots(3, 3)
axs2=axs2.flatten()
for i, method in enumerate(data_dict.keys()):
for j, models in enumerate(data_dict[method].keys()):
axs2[i].errorbar(range(len(data_dict[method][models]['sync_peak_derivs']['mean_peak_deriv'])),data_dict[method][models]['sync_peak_derivs']['mean_peak_deriv'], yerr=data_dict[method][models]['sync_peak_derivs']['sd_peak_deriv'], marker='o', color=colors[i], elinewidth = 0.2)
axs2[i].set_xlabel('number of inputs', fontsize = 14)
axs2[i].set_ylabel('dV/dt (V/s)', fontsize = 14)
#axs2[i].set_ylim(-0,10)
#axs2[i].set_xlim(-0,10)
axs2[i].set_title(method, fontsize = 14)
fig2.suptitle('Summary plot of mean peak dV/dt amplitude for synchronous inputs', fontsize = 20)
print(fig2.get_size_inches())
fig2.set_size_inches(10,9)
plt.tight_layout()
plt.savefig(save_dir + 'oblique_sync_peak_derivatives.svg', bbox_inches='tight')
# error plots - averaged over the 20 models
plt.figure()
methods = list(data_dict.keys())
models = list(data_dict[method].keys())
feat_errors_names = list(data_dict[methods[0]][models[0]]['errors'].keys())
y = range(len(feat_errors_names))
shift=0.1
for k, error in enumerate(feat_errors_names):
for i, method in enumerate(data_dict.keys()):
error_values = []
for j, models in enumerate(data_dict[method]):
error_values.append(data_dict[method][models]['errors'][error])
if k == 0:
plt.errorbar(numpy.mean(error_values), k+i*shift, xerr = numpy.std(error_values), marker='o', color=colors[i], label = method)
else:
plt.errorbar(numpy.mean(error_values), k+i*shift, xerr = numpy.std(error_values), marker='o', color=colors[i])
plt.tight_layout()
plt.yticks(y, feat_errors_names)
# plt.xlim(0,4)
plt.title('Feature errors')
plt.xlabel("# sd")
lgd = plt.legend(bbox_to_anchor=(1.0, 1.0), loc = 'upper left')
plt.savefig(save_dir + 'oblique_mean_errors_avg_of_models.svg', bbox_extra_artists=(lgd,), bbox_inches='tight')
# error plots
plt.figure()
methods = list(data_dict.keys())
models = list(data_dict[method].keys())
feat_errors_names = list(data_dict[methods[0]][models[0]]['errors'].keys())
y = range(len(feat_errors_names))
shift=0.1
for k, error in enumerate(feat_errors_names):
for i, method in enumerate(data_dict.keys()):
for j, models in enumerate(data_dict[method]):
if k == 0 and j==0:
plt.plot(data_dict[method][models]['errors'][error], k+i*shift, marker='o', color=colors[i], label = method)
else:
plt.plot(data_dict[method][models]['errors'][error], k+i*shift, marker='o', color=colors[i])
plt.yticks(y, feat_errors_names)
# plt.xlim(0,4)
plt.tight_layout()
plt.title('Feature errors')
plt.xlabel("# sd")
lgd = plt.legend(bbox_to_anchor=(1.0, 1.0), loc = 'upper left')
plt.savefig(save_dir + 'oblique_mean_errors', bbox_extra_artists=(lgd,), bbox_inches='tight')
# plot p_values - averaged over the 20 models
plt.figure()
methods = list(data_dict.keys())
models = list(data_dict[method].keys())
feat_p_values_names = list(data_dict[methods[0]][models[0]]['p_values'].keys())
y = range(len(feat_errors_names))
shift=0.1
for k, feat_p_value in enumerate(feat_p_values_names):
for i, method in enumerate(data_dict.keys()):
p_values = []
for j, models in enumerate(data_dict[method]):
p_values.append(data_dict[method][models]['p_values'][feat_p_value])
if k == 0:
plt.errorbar(numpy.mean(p_values), k+i*shift, xerr = numpy.std(p_values), marker='o', color=colors[i], label = method)
else:
plt.errorbar(numpy.mean(p_values), k+i*shift, xerr = numpy.std(p_values), marker='o', color=colors[i])
#plt.semilogx(p_values, y, linestyle='None', marker='o', label = models)
plt.yticks(y, feat_p_values_names)
# plt.xlim(0,4)
plt.tight_layout()
plt.axvline(x=0.05, label='0.05', color='red')
plt.xlabel("p values")
lgd = plt.legend(bbox_to_anchor=(1.0, 1.0), loc = 'upper left')
plt.savefig(save_dir + 'oblique_p_values_avg_of_models.svg', bbox_extra_artists=(lgd,), bbox_inches='tight')
# plot p_values - log scale
plt.figure()
methods = list(data_dict.keys())
models = list(data_dict[method].keys())
feat_p_values_names = list(data_dict[methods[0]][models[0]]['p_values'].keys())
y = range(len(feat_errors_names))
shift=0.1
for k, feat_p_value in enumerate(feat_p_values_names):
for i, method in enumerate(data_dict.keys()):
for j, models in enumerate(data_dict[method]):
if k == 0 and j == 0:
plt.semilogx(data_dict[method][models]['p_values'][feat_p_value], k+i*shift, marker='o', color=colors[i], label = method)
else:
plt.semilogx(data_dict[method][models]['p_values'][feat_p_value], k+i*shift, marker='o', color=colors[i])
#plt.semilogx(p_values, y, linestyle='None', marker='o', label = models)
plt.yticks(y, feat_p_values_names)
# plt.xlim(0,4)
plt.tight_layout()
plt.axvline(x=0.05, label='0.05', color='red')
plt.xlabel("p values")
lgd = plt.legend(bbox_to_anchor=(1.0, 1.0), loc = 'upper left')
plt.savefig(save_dir + 'oblique_p_values.svg', bbox_extra_artists=(lgd,), bbox_inches='tight')
# plot features
for method in data_dict:
for models in data_dict[method]:
for feature in data_dict[method][models]['features'].keys():
if feature != 'num_of_locations':
for key, val in data_dict[method][models]['features'][feature].items():
try:
data_dict[method][models]['features'][feature][key] = float(val)
except ValueError:
quantity_parts = val.split(" ")
number = float(quantity_parts[0])
units = " ".join(quantity_parts[1:])
data_dict[method][models]['features'][feature][key] = quantities.Quantity(number, units)
try:
colormap = plt.cm.spectral #http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html
except:
colormap = plt.cm.nipy_spectral
colors = colormap(numpy.linspace(0.1, 0.9, len(data_dict.keys())))
import matplotlib.gridspec as gridspec
#gs = gridspec.GridSpec(2, 2,width_ratios=[4,1])
gs = gridspec.GridSpec(2, 5)
#fig, axes = plt.subplots(nrows=2, ncols=2)
fig = plt.figure(tight_layout=False)
fig.tight_layout()
stitle=fig.suptitle('Features', fontsize = 20)
plt.subplots_adjust(left = 0.1, top = 0.9, right = 0.9, bottom = 0.1, hspace = 0.5, wspace = 0.5)
#plt.subplot(1,4,1)
#plt.subplot(gs[0])
fig.add_subplot(gs[0, 0])
shift = -0.5
x = 2
for i, method in enumerate(data_dict.keys()):
for models in data_dict[method]:
feat=data_dict[method][models]['features']['threshold']['mean']
SD=data_dict[method][models]['features']['threshold']['sd']
plt.errorbar(x+shift, feat, yerr = SD, marker='o', color=colors[i], linestyle ='None')
shift += 0.3
x= 0.5
plt.errorbar(x, observation['mean_threshold'], yerr = observation['threshold_std'], marker='x',markersize=10, color = 'black', linestyle ='None', label = 'experiment')
plt.ylabel('Threshold (mV)', fontsize=14)
plt.tick_params(bottom=False, labelbottom=False)
#lgd = plt.legend(bbox_to_anchor=(0.01, 1.03), loc = 'lower left', fontsize = 12)
fig.add_subplot(gs[0, 1])
shift = -0.5
x = 2
for i, method in enumerate(data_dict.keys()):
for models in data_dict[method]:
feat=data_dict[method][models]['features']['proximal_threshold']['mean']
SD=data_dict[method][models]['features']['proximal_threshold']['sd']
#plt.errorbar(3, feature, yerr = SD, marker='o')
plt.errorbar(x+shift, feat, yerr = SD, marker='o', color=colors[i], linestyle ='None')
shift += 0.3
x= 0.5
plt.errorbar(x, observation['mean_prox_threshold'], yerr = observation['prox_threshold_std'], marker='x',markersize=10, color = 'black', linestyle ='None', label = 'experiment')
plt.ylabel('Proximal threshold (mV)', fontsize=14)
plt.tick_params(bottom=False, labelbottom=False)
#lgd = plt.legend(bbox_to_anchor=(0.01, 1.03), loc = 'lower left', fontsize = 12)
fig.add_subplot(gs[0, 2])
shift = -0.5
x = 2
for i, method in enumerate(data_dict.keys()):
for models in data_dict[method]:
feat=data_dict[method][models]['features']['distal_threshold']['mean']
SD=data_dict[method][models]['features']['distal_threshold']['sd']
plt.errorbar(x+shift, feat, yerr = SD, marker='o', color=colors[i], linestyle ='None')
shift += 0.3
x= 0.5
plt.errorbar(x, observation['mean_dist_threshold'], yerr = observation['dist_threshold_std'], marker='x',markersize=10, color = 'black', linestyle ='None', label = 'experiment')
plt.ylabel('Distal threshold (mV)', fontsize=14)
plt.tick_params(bottom=False, labelbottom=False)
#lgd = plt.legend(bbox_to_anchor=(0.01, 1.03), loc = 'lower left', fontsize = 12)
fig.add_subplot(gs[0, 3])
shift = -0.5
x = 2
for i, method in enumerate(data_dict.keys()):
for models in data_dict[method]:
feat=data_dict[method][models]['features']['amplitude_at_th']['mean']
SDs=data_dict[method][models]['features']['amplitude_at_th']['sd']
#plt.errorbar(7, feature, yerr = SD, marker='o')
plt.errorbar(x+shift, feat, yerr = SD, marker='o', color=colors[i], linestyle ='None')
shift += 0.3
x= 0.5
plt.errorbar(x, observation['mean_amp_at_th'], yerr = observation['amp_at_th_std'], marker='x',markersize=10, color = 'black', linestyle ='None', label = 'experiment')
plt.ylabel('Amplitude at threshold (mV)', fontsize=14)
plt.tick_params(bottom=False, labelbottom=False)
#lgd = plt.legend(bbox_to_anchor=(0.01, 1.03), loc = 'lower left', fontsize = 12)
#plt.subplot(gs[1])
fig.add_subplot(gs[0, 4])
shift = -0.5
x = 2
for i, method in enumerate(data_dict.keys()):
for models in data_dict[method]:
feat=data_dict[method][models]['features']['peak_dV/dt_at_th']['mean']
SD=data_dict[method][models]['features']['peak_dV/dt_at_th']['sd']
plt.errorbar(x + shift, feat, yerr = SD, marker='o', color=colors[i], linestyle ='None')
shift += 0.3
x= 0.5
obs_feat = observation['mean_peak_deriv']
obs_sd = observation['peak_deriv_std']
plt.errorbar(x, obs_feat, yerr = obs_sd, marker='x',markersize=10, color = 'black', linestyle ='None', label='experiment')
plt.ylabel('peak dV/dt at threshold (V/s)',fontsize=14)
plt.tick_params(bottom=False, labelbottom=False)
plt.subplot(gs[1,0])
shift = -0.5
x = numpy.array([2])
for i, method in enumerate(data_dict.keys()):
for models in data_dict[method]:
feat=data_dict[method][models]['features']['degree_of_nonlinearity_at_th']['mean']
SD=data_dict[method][models]['features']['degree_of_nonlinearity_at_th']['sd']
plt.errorbar(x + shift, feat, yerr = SD, marker='o', color=colors[i], linestyle ='None')
shift += 0.3
x= 0.5
obs_feat = observation['mean_nonlin_at_th']
obs_sd = observation['nonlin_at_th_std']
plt.errorbar(x, obs_feat, yerr = obs_sd, marker='x',markersize=10, color = 'black', linestyle ='None')
plt.ylabel('Degree of nonlinearity at threshold (%)', fontsize = 14)
plt.tick_params(bottom=False, labelbottom=False)
plt.subplot(gs[1,1])
shift = -0.5
x = numpy.array([2])
for i, method in enumerate(data_dict.keys()):
for models in data_dict[method]:
feat=data_dict[method][models]['features']['suprath_degree_of_nonlinearity']['mean']
SD=data_dict[method][models]['features']['suprath_degree_of_nonlinearity']['sd']
plt.errorbar(x + shift, feat, yerr = SD, marker='o', color=colors[i], linestyle ='None')
shift += 0.3
x= 0.5
obs_feat = observation['mean_nonlin_suprath']
obs_sd = observation['nonlin_suprath_std']
plt.errorbar(x, obs_feat, yerr = obs_sd, marker='x',markersize=10, color = 'black', linestyle ='None')
plt.ylabel('Suprathreshold degree of nonlinearity (%)', fontsize = 14)
plt.tick_params(bottom=False, labelbottom=False)
plt.subplot(gs[1,2])
shift = -0.5
x = numpy.array([2])
for i, method in enumerate(data_dict.keys()):
for models in data_dict[method]:
feat=data_dict[method][models]['features']['asynch_degree_of_nonlin_at_th']['mean']
SD=data_dict[method][models]['features']['asynch_degree_of_nonlin_at_th']['sd']
plt.errorbar(x + shift, feat, yerr = SD, marker='o', color=colors[i], linestyle ='None')
shift += 0.3
x= 0.5
obs_feat = observation['mean_async_nonlin']
obs_sd = observation['async_nonlin_std']
plt.errorbar(x, obs_feat, yerr = obs_sd, marker='x',markersize=10, color = 'black', linestyle ='None')
plt.ylabel('Degree of nonlinearity asynchron. (%)', fontsize = 14)
plt.tick_params(bottom=False, labelbottom=False)
plt.subplot(gs[1,3])
shift = -0.5
x = numpy.array([2])
for i, method in enumerate(data_dict.keys()):
print(i)
for j, models in enumerate(data_dict[method].keys()):
feat=data_dict[method][models]['features']['time_to_peak_at_th']['mean']
SD=data_dict[method][models]['features']['time_to_peak_at_th']['sd']
if j==0:
plt.errorbar(x+shift, feat, yerr = SD, marker='o', color=colors[i], linestyle ='None', label = method)
else:
plt.errorbar(x+shift, feat, yerr = SD, marker='o', color=colors[i], linestyle ='None')
shift += 0.3
x= 0.5
obs_feat = observation['mean_time_to_peak']
obs_sd = observation['time_to_peak_std']
plt.errorbar(x, obs_feat, yerr = obs_sd, marker='x',markersize=10, color = 'black', linestyle ='None', label='experiment')
plt.ylabel('time to peak at threshold (ms)', fontsize = 14)
plt.tick_params(bottom=False, labelbottom=False)
#plt.ylim(0.0,16)
lgd = plt.legend(bbox_to_anchor=(1.0, 1.0), loc = 'upper left', ncol=1, fontsize = 12)
#lgd = plt.legend(bbox_to_anchor=(1.0, 1.0), loc = 'upper left')
fig.set_size_inches(16, 8)
#print(fig.get_size_inches())
#plt.savefig(save_dir + 'oblique_mean_absolute_features_v2', bbox_extra_artists=(lgd, fig,), bbox_inches='tight')
plt.savefig(save_dir + 'oblique_mean_absolute_features_Final.svg', bbox_inches='tight')
'''NMDA voltage dependance traces'''
v = numpy.arange(-120, 60, 1)
plt.figure()
for i, method in enumerate(data_dict.keys()):
if method == 'Gao':
mgblock = 1 / (1 + numpy.exp(0.072 * (-v)) * (1 / 3.57))
elif method == 'Grunditz':
mgblock = 1 / (1 + numpy.exp(0.08 * (-v)) * (1 / 0.69))
elif method == 'Jahr_Stevens':
mgblock = 1 / (1 + numpy.exp(0.062 * (-v)) * (1 / 3.57 ))
elif method == 'Larkum':
mgblock = 1 / (1 + numpy.exp(0.08 * (-v)) * (1 * 0.3))
elif method == 'Magee':
mgblock = 1 / (1 + numpy.exp(0.062 * (-v)) * (1 / 3.57 )) #same as Jahr-Stevens in this case
elif method == 'Major':
mgblock = 1 / (1 + numpy.exp(0.08 * (-v)) * (1 / 5 ))
elif method == 'Otmakhova':
mgblock = 1 / (1 + numpy.exp(0.1 * (-v)) * (1 / 15 ))
elif method == 'Tar':
mgblock = 1 / (1 + numpy.exp(((-v)-6)/18))
else:
mgblock = 1 / (1 + numpy.exp(0.071 * (-v)) * (1 / 4.3))
plt.plot(v, mgblock, color=colors[i])
plt.tight_layout()
plt.xlabel('Voltage (mV)', fontsize = 14)
plt.ylabel('Mg block', fontsize = 14)
plt.title('Voltage dependence of different NMDA receptors', fontsize = 20)
plt.savefig(save_dir + 'NMDA_voltage_dependencies.svg', bbox_inches='tight')
plt.show()

model_comparison_oblique_integration.py, under CC-BY-4.0 · at the source

Overview

Authors: Luca Tar^1,2, Sára Sáray², Máté Mohácsi^1,2, Tamás F. Freund^1,2, Szabolcs Káli^1,2

HUN-REN Institute of Experimental Medicine, Budapest, Hungary
Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary

Journal: Frontiers in computational neuroscience, volume 20, article 1897214

Dates: received 1 June 2026; accepted 19 August 2026; published online 22 September 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI · PMCID PMC13638379

Status: code verified (how the code is found and verified)

Categories: histology / microscopy (modality), none (in silico) (organism)

Methods: Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Single-unit activity, calcium imaging

Keywords: compartmental modeling, computer simulations, dendritic spines, hippocampus, model validation, parameter optimization

Citations: not cited yet (Europe PMC); 127 references in the paper

Abstract

Anatomically and biophysically detailed models of neurons have been widely used to study information processing in these cells. Most studies focused on understanding specific phenomena, while more general models that aim to capture various cellular processes simultaneously remain rare even though such models are required to predict neuronal behavior under more complex, natural conditions. In this study, we aimed to develop a detailed, data-driven, general-purpose biophysical model of hippocampal CA1 pyramidal neurons. We leveraged extensive morphological, biophysical and physiological data available for this cell type, and established a systematic workflow for model construction and validation that relies on our recently developed software tools. The model is based on a high-quality morphological reconstruction and includes a diverse curated set of ion channel models. After incorporating the available constraints on the distribution of ion channels, the remaining free parameters were optimized using the Neuroptimus tool to fit a variety of electrophysiological features extracted from somatic whole-cell recordings. Validation using HippoUnit confirmed the model’s ability to replicate key electrophysiological features, including somatic voltage responses to current input, the attenuation of synaptic potentials and backpropagating action potentials, and nonlinear synaptic integration in oblique dendrites. Our model also included active dendritic spines, modeled either explicitly or by merging their biophysical mechanisms into those of the parent dendrite. We found that many aspects of neuronal behavior were unaffected by the level of detail in modeling spines, but modeling nonlinear synaptic integration accurately required the explicit modeling of spines. Our data-driven model of CA1 pyramidal cells matching diverse experimental constraints is a general tool for the investigation of the activity and plasticity of these cells and can also be a reliable component of detailed models of the hippocampal network. Our systematic approach to building and validating multi-purpose models should apply to other cell types as well.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 10 matches between paragraphs and lines of code.

lucatar/hippounit

License: BSD-3-Clause

State: the link answers, verified on 7 October 2026

Evidence: files inventoried

Commit: 539a384e4666d83ce49b1c117fd949c84a09b460, 25 February 2026

Languages: Python (36), Jupyter (1), NEURON (1)

Size: 84 files, 38 scripts

Software Heritage: not archived

Found in: the text, “Model testing”

Holds: README, license file, environment (setup.py, docs/requirements.txt), tests, documentation, 1 notebook

Not found: CITATION.cff, continuous integration

Tools: NumPy (17 files), Matplotlib (8 files), SciPy (7 files), NEURON (3 files)

Availability: 1 check, the latest on 7 October 2026: the link answers

7 October 2026: the link answers

40 files

docs/source/Migliore_2011_all_tests.ipynb, Jupyter, 302 lines
docs/source/conf.py, Python, 150 lines
hippounit/__init__.py, Python, 6 lines
hippounit/capabilities/__init__.py, Python, 15 lines
hippounit/capabilities/cap_InitialiseModel.py, Python, 10 lines
hippounit/capabilities/cap_NumOfPossibleLocations.py, Python, 21 lines
hippounit/capabilities/cap_ProvidesGoodObliques.py, Python, 39 lines
hippounit/capabilities/cap_ProvidesRandomDendriticLocations.py, Python, 41 lines
hippounit/capabilities/cap_ProvidesRecordingLocationsOnTrunk.py, Python, 47 lines
hippounit/capabilities/cap_ReceivesCurrentStimuli_ProvidesResponse.py, Python, 41 lines
hippounit/capabilities/cap_ReceivesCurrentStimuli_ProvidesResponse_MultipleLocations.py, Python, 46 lines
hippounit/capabilities/cap_ReceivesEPSCstim.py, Python, 36 lines, 1 match
hippounit/capabilities/cap_ReceivesMultipleSquareCurrents.py, Python, 12 lines
hippounit/capabilities/cap_ReceivesMultipleSynapses.py, Python, 33 lines
hippounit/capabilities/cap_ReceivesSynapse.py, Python, 16 lines
hippounit/capabilities/cap_ReceivesSynapseGivenPathway.py, Python, 15 lines
hippounit/capabilities/cap_RunSimulation_ReturnTraces.py, Python, 11 lines
hippounit/capabilities/cap_ThetaSynapticStimuli.py, Python, 10 lines
hippounit/classify_apical_sections.py, Python, 232 lines
hippounit/plottools.py, Python, 143 lines
hippounit/scores/__init__.py, Python, 14 lines
hippounit/scores/score_P_Value_ObliqueIntegration.py, Python, 77 lines
hippounit/scores/score_ZScore_ObliqueIntegration.py, Python, 53 lines
hippounit/scores/score_ZScore_PSPAttenuation.py, Python, 51 lines
hippounit/scores/score_ZScore_PathwayInteraction.py, Python, 64 lines
hippounit/scores/score_ZScore_backpropagatingAP.py, Python, 120 lines
hippounit/scores/score_ZScore_depolblock.py, Python, 69 lines
hippounit/scores/score_ZScore_somaticSpiking.py, Python, 60 lines
hippounit/tests/__init__.py, Python, 15 lines
hippounit/tests/default_NMDAr/NMDA_CA1_pyr_SC.mod, NEURON, 111 lines
hippounit/tests/test_BackpropagatingAPTest.py, Python, 917 lines
hippounit/tests/test_DepolarizationBlockTest.py, Python, 661 lines
hippounit/tests/test_ObliqueIntegrationTest.py, Python, 2,038 lines
hippounit/tests/test_PSPAttenuationTest.py, Python, 620 lines
hippounit/tests/test_PathwayInteraction.py, Python, 1,619 lines
hippounit/tests/test_SomaticFeaturesTest.py, Python, 562 lines
hippounit/utils.py, Python, 1,383 lines
setup.py, Python, 30 lines
LICENSE.txt, License, 10 lines
README.md, Text, 179 lines

At the source: github.com/lucatar/hippounit/tree/hippounit_spine_project

lucatar/hippounit_CA1_demo

License: none: the authors keep all their rights

State: the link answers, verified on 7 October 2026

Evidence: files inventoried

Commit: 5c63b5aa970e6a6ed068d24c19d146ee919cce7a, 13 August 2026

Languages: Python (34), NEURON (30), Jupyter (18)

Size: 102 files, 82 scripts

Software Heritage: not archived

Found in: the text, “Model testing”

Holds: README, 18 notebooks

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NEURON (37 files), NumPy (7 files), Matplotlib (1 file), pandas (1 file), SciPy (1 file)

Availability: 1 check, the latest on 7 October 2026: the link answers

7 October 2026: the link answers

38 files

CA1_pyramidal_cell_model/biophys_na_inhomo_minimal_model_full_soma_active_spine_rework.hoc, NEURON, 330 lines
CA1_pyramidal_cell_model/load_model_na_inhomo_minimal_model_full_soma_all_spine_true_diam_active_spine_KA_fact_50Ra_inject.hoc, NEURON, 112 lines
CA1_pyramidal_cell_model/load_model_na_inhomo_minimal_model_full_soma_f_fact_true_diam_active_spine_KA_fact_50Ra.hoc, NEURON, 102 lines
CA1_pyramidal_cell_model/load_model_na_inhomo_minimal_model_full_soma_f_fact_true_diam_active_spine_KA_fact_50Ra_inject.hoc, NEURON, 116 lines
CA1_pyramidal_cell_model/loader_script_opt_Luca_model_soma_and_axon.py, Python, 167 lines
CA1_pyramidal_cell_model/mod_files/CaL_pool2_ghk.mod, NEURON, 243 lines
CA1_pyramidal_cell_model/mod_files/CaN_BG_pool2_ghk.mod, NEURON, 218 lines
CA1_pyramidal_cell_model/mod_files/H_CA1pyr_dist.mod, NEURON, 209 lines
CA1_pyramidal_cell_model/mod_files/H_CA1pyr_prox.mod, NEURON, 208 lines
CA1_pyramidal_cell_model/mod_files/KM_new.mod, NEURON, 155 lines
CA1_pyramidal_cell_model/mod_files/K_AHP3_lpool.mod, NEURON, 213 lines
CA1_pyramidal_cell_model/mod_files/K_A_dist_new.mod, NEURON, 204 lines
CA1_pyramidal_cell_model/mod_files/K_A_prox_new.mod, NEURON, 204 lines
CA1_pyramidal_cell_model/mod_files/K_DRS4_params_voltage_dep.mod, NEURON, 155 lines
CA1_pyramidal_cell_model/mod_files/Leak_pyr.mod, NEURON, 97 lines
CA1_pyramidal_cell_model/mod_files/NMDA_L.mod, NEURON, 133 lines
CA1_pyramidal_cell_model/mod_files/Na_BG_axon.mod, NEURON, 197 lines
CA1_pyramidal_cell_model/mod_files/Na_BG_dend.mod, NEURON, 197 lines
CA1_pyramidal_cell_model/mod_files/Na_BG_dend_fast.mod, NEURON, 197 lines
CA1_pyramidal_cell_model/mod_files/Na_BG_soma.mod, NEURON, 197 lines
CA1_pyramidal_cell_model/mod_files/bk_ch_pool.mod, NEURON, 102 lines
CA1_pyramidal_cell_model/mod_files/cacum.mod, NEURON, 57 lines
CA1_pyramidal_cell_model/mod_files/cacum_lpool.mod, NEURON, 46 lines
CA1_pyramidal_cell_model/mod_files/cacum_npool.mod, NEURON, 46 lines
CA1_pyramidal_cell_model/mod_files/cad_mod_for_ltp.mod, NEURON, 117 lines
CA1_pyramidal_cell_model/mod_files/car.mod, NEURON, 110 lines
CA1_pyramidal_cell_model/mod_files/cat3.mod, NEURON, 123 lines
CA1_pyramidal_cell_model/mod_files/kd_params3.mod, NEURON, 83 lines
CA1_pyramidal_cell_model/mod_files/sKCa.mod, NEURON, 115 lines
CA1_pyramidal_cell_model/morphology.hoc, NEURON, 5,951 lines
CA1_pyramidal_cell_model/we_want_spines_fully_active_ghk_50_Ra_split_head.hoc, NEURON, 352 lines, 1 match
SPINE_PROJECT_basic_set_new_PSP_and_depol_model_rework_new_K_stuff.ipynb, Jupyter, 194 lines
SPINE_PROJECT_basic_set_new_PSP_and_depol_model_rework_new_K_stuff_Ra100.ipynb, Jupyter, 194 lines
SPINE_PROJECT_basic_set_new_PSP_and_depol_model_rework_new_K_stuff_blocking.ipynb, Jupyter, 194 lines
SPINE_PROJECT_basic_set_new_neuroptimus_bAP2_model_rework_new_K_stuff.ipynb, Jupyter, 142 lines
SPINE_PROJECT_basic_set_new_neuroptimus_bAP2_model_rework_new_K_stuff_channel_blocking.ipynb, Jupyter, 142 lines
SPINE_PROJECT_basic_set_new_neuroptimus_bAP2_model_rework_new_K_stuff_fast_na.ipynb, Jupyter, 142 lines
repository limit reached (2,000 files or 30 MB): the rest is at the source (45 files)
README.md, Text, 18 lines

At the source: github.com/lucatar/hippounit_CA1_demo

Zenodo 21907403

License: CC-BY-4.0

State: the link answers, verified on 7 October 2026

Evidence: files inventoried

Size: 5 files

Software Heritage: not checked

Found in: the text, “Overview of the model-building process”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Matplotlib (30 files), NumPy (30 files), pandas (14 files), NEURON (9 files), SciPy (8 files), seaborn (3 files)

Availability: 1 check, the latest on 7 October 2026: the link answers (HTTP 200)

7 October 2026: the link answers (HTTP 200)

32 files

CA1_pyramidal_cell_codes_and_scripts.zip/ampa_nmda_ratio.py, Python, 39 lines
CA1_pyramidal_cell_codes_and_scripts.zip/load_pickle.py, Python, 9 lines
CA1_pyramidal_cell_codes_and_scripts.zip/loader_script_opt_Luca_model_SHAFT_syn_nezegeto.py, Python, 661 lines
CA1_pyramidal_cell_codes_and_scripts.zip/loader_script_opt_Luca_model_SPINE_syn_KA_block_nezegeto.py, Python, 883 lines
CA1_pyramidal_cell_codes_and_scripts.zip/loader_script_opt_Luca_model_SPINE_syn_KA_block_nezegeto_kontrol.py, Python, 877 lines
CA1_pyramidal_cell_codes_and_scripts.zip/loader_script_opt_Luca_model_SPINE_syn_nezegeto.py, Python, 859 lines
CA1_pyramidal_cell_codes_and_scripts.zip/loader_script_opt_Luca_model_soma_and_axon.py, Python, 167 lines
CA1_pyramidal_cell_codes_and_scripts.zip/loader_script_synapses_excit_spine_nezegeto.py, Python, 506 lines
CA1_pyramidal_cell_codes_and_scripts.zip/loader_script_synapses_excit_theta.py, Python, 505 lines
CA1_pyramidal_cell_codes_and_scripts.zip/loader_script_synapses_excit_theta_no_nmda.py, Python, 505 lines
CA1_pyramidal_cell_codes_and_scripts.zip/loader_script_synapses_excit_theta_zero.py, Python, 505 lines
CA1_pyramidal_cell_codes_and_scripts.zip/model_comparison_PSPatt_published_models.py, Python, 219 lines
CA1_pyramidal_cell_codes_and_scripts.zip/model_comparison_PSPatt_published_models_2_sets.py, Python, 323 lines
CA1_pyramidal_cell_codes_and_scripts.zip/model_comparison_PSPatt_published_models_2_sets_Ra_comp.py, Python, 323 lines
CA1_pyramidal_cell_codes_and_scripts.zip/model_comparison_SOMA_optimizer_multirun_AbsFeatures_subplots_new_style.py, Python, 703 lines, 1 match
CA1_pyramidal_cell_codes_and_scripts.zip/model_comparison_SOMA_optimizer_multirun_AbsFeatures_subplots_new_style_2_sets.py, Python, 955 lines, 1 match
CA1_pyramidal_cell_codes_and_scripts.zip/model_comparison_bAP_published_models.py, Python, 275 lines
CA1_pyramidal_cell_codes_and_scripts.zip/model_comparison_bAP_published_models_2_sets.py, Python, 383 lines
CA1_pyramidal_cell_codes_and_scripts.zip/model_comparison_bAP_published_models_fast_na.py, Python, 275 lines
CA1_pyramidal_cell_codes_and_scripts.zip/model_comparison_depol_block_published_models.py, Python, 155 lines
CA1_pyramidal_cell_codes_and_scripts.zip/model_comparison_oblique_integration.py, Python, 489 lines, 2 matches
CA1_pyramidal_cell_codes_and_scripts.zip/plot_oblique_traces.py, Python, 114 lines
CA1_pyramidal_cell_codes_and_scripts.zip/plot_oblique_traces_for_nmda_compare.py, Python, 63 lines
CA1_pyramidal_cell_codes_and_scripts.zip/plot_parameters2.py, Python, 367 lines
CA1_pyramidal_cell_codes_and_scripts.zip/plot_parameters3.py, Python, 470 lines, 1 match
CA1_pyramidal_cell_codes_and_scripts.zip/plot_params_vs_features.py, Python, 74 lines
CA1_pyramidal_cell_codes_and_scripts.zip/plotting_exp_traces.py, Python, 60 lines
CA1_pyramidal_cell_codes_and_scripts.zip/plotting_model_traces.py, Python, 66 lines
CA1_pyramidal_cell_codes_and_scripts.zip/plotting_model_traces_blocking.py, Python, 232 lines, 2 matches
CA1_pyramidal_cell_codes_and_scripts.zip/shaft_spine_compare_statistics.py, Python, 152 lines
CA1_pyramidal_cell_codes_and_scripts.zip/shaft_spine_compare_statistics_for_paper_plot.py, Python, 106 lines, 1 match
README.txt, Text, 14 lines

At the source: zenodo.org/records/21907403

modeldb:2031080

License: none: the authors keep all their rights

State: the link answers, verified on 7 October 2026

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Found in: the text, “Overview of the model-building process”

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7 October 2026: the link answers (HTTP 200)

At the source: modeldb.science/2031080

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Tar, L., Sáray, S., Mohácsi, M., Freund, T. F., & Káli, S. (2026). How to train your neuron: developing a detailed, up-to-date, multipurpose model of hippocampal CA1 pyramidal cells. Frontiers in computational neuroscience, 20, 1897214.

BibTeX

@article{tar2026how, author = {Tar, Luca and Sáray, Sára and Mohácsi, Máté and Freund, Tamás F. and Káli, Szabolcs}, title = {{How to train your neuron: developing a detailed, up-to-date, multipurpose model of hippocampal CA1 pyramidal cells}}, journal = {Frontiers in computational neuroscience}, year = {2026}, month = oct, volume = {20}, pages = {1897214}, publisher = {Frontiers Media SA}, issn = {1662-5188}, pmcid = {PMC13638379} }

RIS

TY - JOUR AU - Tar, Luca AU - Sáray, Sára AU - Mohácsi, Máté AU - Freund, Tamás F. AU - Káli, Szabolcs TI - How to train your neuron: developing a detailed, up-to-date, multipurpose model of hippocampal CA1 pyramidal cells T2 - Frontiers in computational neuroscience J2 - Front Comput Neurosci PY - 2026 DA - 2026/10/07 VL - 20 SP - 1897214 SN - 1662-5188 PB - Frontiers Media SA LA - en ER -

CSL-JSON

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How to train your neuron: developing a detailed, up-to-date, multipurpose model of hippocampal CA1 pyramidal cells

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