LCN-HippoModel Documentation

This is the documentation of the Laboratorio de Circuitos Neuronales (LCN) Hippo-Model.

The complete code is available here for free usage.

Documentation consists of a brief explanation about the model and its purposes, followed by the description of the code.

About LCN-HippoModel

One of the most studied brain rhythm is the theta rhythm. Theta (4-12 Hz) is considered the “on-line” status of the hippocampus, a highly rhythmic activity that acts as a synchronizer mechanism for encoding and information processing. During a theta cycle different neuronal populations of the hippocampus fire sequentially: each one at a distinct preferred phase. Moreover, it has been recently discovered that pyramidal neurons of the CA1 hippocampal region, generally considered a homogeneous population, can be classified into deep and superficial: not only each cell type shows a different preferred phase along theta cycles, but also a different response in other hippocampal rhythm, sharp wave-ripples, an oscillation associated to the consolidation of memory. Understanding how this coordination holds up hippocampal functions as spatial navigation and memory, is a major question in the field.

The LCN-HippoModel is a biophysically realistic model of CA1 pyramidal cells aimed to get novel insights on firing dynamics in deep and superficial populations during the theta rhythm, and the role of the differential contribution of realistic excitatory and inhibitory synaptic inputs and intrinsic properties.

The model includes known excitatory and inhibitory inputs, using morphologically reconstructions from NeuroMorpho (a public database), and a precise distribution of the main ion channels via the Hodking-Huxley multi-compartment formalism in the NEURON+Python platform.

To provide diversity among the pyramidal cells, we generated several sets of intrinsic properties and, called individuals, through a genetic algorithm (GA). The GA allowed us to identify a range of ionic conductances (called genes in the genetic algorithm terminology) that target experimental values in each given morphology. So our individuals are some of those set of genes that targeted experimental constraints. This procedure was repeated with the synaptic properties, so we ended up with several intrinsic and synaptic individuals that would give us heterogenous responses.

The LCN-HippoModel offers an easy way to test hypotheses about the role of different synaptic and intrinsic factors in the firing dynamics of CA1 pyramidal cells by simply changing experimental parameters defined in the LCNhm_configurationfile.

Code description

The LCN-HippoModel consists basically in this four scripts:

• LCNhm_configurationfile
• LCNhm_main
• LCNhm_class
• LCNhm_functions

All of them are Python code, and use NEURON as an imported library.

There is a fifth script, that plots the morphology of the neuron, the recording sites and the membrane potentials, which is quite simple and can be sophisticated if needed. More description at the end of this page.

• LCNhm-plot

To run the simulation, you must to the open a terminal, go to the directory location where all the model is (eg.: /home/Projects/LCNhippomodel), and execute the main file:

>>> cd /home/Projects/LCNhippomodel/
>>> python LCNhm_main.py

This will run the simulation with the default parameters. For modification in the simulation properties or cell parameters, change the LCNhm_configurationfile, and run again the simulation.

To see the results, just add:

>>> python LCNhm_plot.py D20190730_T1030_First_Test

Let’s briefly describe them.

LCN-HippoModel Configuration File

target:

LCNhm_configurationfile

It’s the only script thought to be modified. It contains all the simulation parameters:

• DIR_LOCATION

  Directory location where all the model is (eg.: /home/Projects/LCNhippomodel)

• OPT_FOLDER_NAME

  Optional name for the folder in which results are going to be saved (eg.: testing_decreasing_CA3)

• SIMPROP_X

  Simulation properties, such the SIMPROP_START_TIME or the SIMPROP_TEMPERATURE.

• CELLPROP_X

  Artificial cell properties. There are subcategories * CELLPROP_MORPHOLOGY: choose the morphological NeuroMorpho reconstruction to be used

  • CELLPROP_INDIVIDUAL: choose the intrinsic individual (from the Genetic Algorithm) to be used (read About LCN-HippoModel for more info)
  • CELLPROP_INTRINSIC_IONCHS: choose ion channels to set in the cell
  • CELLPROP_INTRINSIC_EXPERIMENT: indicate multiplicative factors to maximum ion conductances and axial resistance, to make the computational experiments
  • CELLPROP_SYNAPTIC_INPUTS: choose synaptic inputs to set in the cell
  • CELLPROP_SYNAPTIC_EXPERIMENT: indicate multiplicative factors to maximum synaptic conductances, to make the computational experiments

• CURRENT_X

  Current clamp properties, such the CURRENT_DURATION or CURRENT_AMPLITUDE.

• RECORDING_X:

  Physical magnitudes (membrane potential, current potential, etc…) to be measured and recorded, such RECORDING_MAGNITUDE or RECORDING_LOCATION.

The script in the in format:

DIR_LOCATION = '/home/Projects/LCNhippomodel'
OPT_FOLDER_NAME = 'test'
SIMPROP_THETA_MODE = True
SIMPROP_THETA_PERIOD = 166.
# ...
CELLPROP_INDIVIDUAL = 0
CELLPROP_INTRINSIC_IONCHS = ['iNas','iA','iAHPs','iC','iCaL','iCaT','iKDR','iM','iHCN','iL']
# ...

so in order to change the parameters it’s just writing over the default values.

It’s convenient to read the specifications in the documentation, because error may arise if the format is not the adequate. For instance, if you want to read more about the possible magnitudes that can be recorded, you can to go Search Page, and type “RECORDING_MAGNITUDE”.

LCN-HippoModel Main File

target:

LCNhm_main

It is the main script, the core module of the LCN-HippoModel project.

Its function is to:

1. Load the parameters from the LCNhm_configurationfile
2. Create the pyramidal cell according to the configuration parameters through the LCNhm_class
3. Run the simulation
4. Save the specified physical magnitudes inside DIR_LOCATION/LCNhm-results/FolderName

The FolderName is define by one of the LCNhm_functions, and looks like:

< D(ate)YearMonthDay _ T(ime)HourMinute _ OPT_FOLDER_NAME >

Eg.: ./ LCNhm-results / D20190730_T1030_First_Test

It will add suffixes if the folder already exists:

Eg.: ./ LCNhm-results / D20190730_T1030_First_Test_001

Eg.: ./ LCNhm-results / D20190730_T1030_First_Test_002

Eg.: ./ LCNhm-results / D20190730_T1030_First_Test_003

Eg.: ./ LCNhm-results / D20190730_T1031_First_Test

Eg.: ./ LCNhm-results / D20190730_T1031_First_Test_001

It will contain:

• TimeSpikes.txt: Time of spikes. If there are no spikes, it will write NaN
• Recordings_Vmem.txt: Recordings of the membrane potential (if specified in the LCNhm_configurationfile)
• Recordings_Imem.txt: Recordings of the membrane current (if specified in the LCNhm_configurationfile)
• Recordings_Pos.txt: Spatial positions (if specified in the LCNhm_configurationfile)

Membrane potentials (Vmem) and membrane currents (Imem) along time of each recording site is saved in different rows. Position is saved in different rows for each recording site

LCN-HippoModel Neuron-Class File

target:

LCNhm_class

This script shapes the class from which our neurons will be built.

Neurons will have the following attributes:

• MorphoName: Morphology name, it must be one of the following: n128, sup1, n409 or n127
• MorphoData: Dataset containing all the morphological data
• SomaList: List of NEURON somatic Sections
• AxonList: List of NEURON axonic Sections
• DendList: List of NEURON Basal dendritic Sections
• ApicList: List of NEURON apical dendritic Sections
• SomApicList: List of somatoapical NEURON apical dendritic Sections
• SectionsList: List of all NEURON Sections, the union of SomaList, AxonList, DendList, ApicList
• TopolDict: Dictionary with all topological information in the form
• CurrentObject: List of NEURON objects containing all current clamps information

Access to all these properties is possible by:

Eg.:

>>> Pyramidal = neuron_class(MorphoName = CELLPROP_MORPHOLOGY,
                         IntrinsicFactors = IntrinsicFactors,
                         SynapticFactors = SynapticFactors,
                         CurrentFactors = CurrentFactors,
                         DirLocation = DIR_LOCATION )

>>> print Pyramidal.MorphoName

sup1

The way the neuron is built is following these steps:

1. Import and set morphological data from ./LCNhm-neurondata/CELLPROP_MORPHOLOGY.swc

   These .swcs cointain information about the 3D structure and width of the cell.

   You can obtain more morphologies on the public databas NeuroMorpho

   Classification into four main morphological categories (soma, axon, basal and apical dendrites).

   Every category will be compartimentalized in multiple sections, each of which will have different and specific ion channels, synaptic inputs, diameter, spines, etc… For instance, one apical dendrite that starts on the somatoapical trunk, will be compartimentalized in 10 sections, where the nearest section to the trunk will be thicker than the furthest.

2. Set biophysics (ion channels, membrane capacitance, membrane resistivity, etc…)

   What ion channels to use can be chosen, as well as a factor to increase/decrease the conductance density.

   See LCNhm_configurationfile.CELLPROP_INTRINSIC_IONCHS and LCNhm_configurationfile.CELLPROP_INTRINSIC_EXPERIMENT: for further details.

3. Set synapses (excitatory/inhibitory inputs, place of synaptic boutons, maximum conductance, etc…)

   Synaptic inputs can be chosen, as well as a factor to increase/decrease the maximum conductance.

   See LCNhm_configurationfile.CELLPROP_SYNAPTIC_INPUTS and LCNhm_configurationfile.CELLPROP_SYNAPTIC_EXPERIMENT: for further details.

4. Set current clamp (place, duration, intensity, etc…)

   A square-pulse clamp is set according to parameters written in LCNhm_configurationfile.

   See LCNhm_configurationfile.CURRENT_DURATION, LCNhm_configurationfile.CURRENT_DELAY, LCNhm_configurationfile.CURRENT_AMPLITUDES, LCNhm_configurationfile.CURRENT_SECTION, LCNhm_configurationfile.CURRENT_LOCATION, for further details.

LCN-HippoModel Functions File

target:

LCNhm_functions

This script has all functions except from the LCNhm_class and its methods.

Within its miscellaneous purposes, there are:

• One function naming and making the folder: make_folder
• Functions related to how to distribute intrinsic properties along the cell: gradient_membrane_resistance, gradient_spine_scale, gradient_V12 and gradient_gHCN
• Function of how to distribute the synaptic releases along a theta cycle: synaptic_time_probability_distribution
• Functions that record and save data from the simulation: recordings, save_spiking_times, save_recordings and save_parameters

LCN-HippoModel Plot File

This script is quite simple; it takes data from the LCNhm_main.FolderName LCNhm-results’s subfolder, and plot it. It shows on the left, the cell morphology and the recording sites, and on the right the membrane potentials.

To properly use this, you must type on the terminal python LCNhm_plot.py followed by the name of the experiment that you want to be plotted (LCNhm_configurationfile.DIR_LOCATION).

Eg.:

>>> python LCNhm_plot.py D20190730_T1030_First_Test

Indices and tables

• Index
• Module Index
• Search Page