Reconstructed neuron (cerebellar, hippocampal, striatal) sims using predicted diameters (Reed et al)

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Accession:267048
Many neuron morphologies in NeuroMorpho.org do not contain accurate dendritic diameters that are needed for simulations. We used a set of archives which did have realistic morphologies to derive equations predicting dendritic diameter, and to create morphologies using the predictions. The equations and new morphologies are derived by 1. extracting morphology features from swc files (morph_feature_extract.py) 2. using multiple regression to derive equations predicting diameter, (morph_feature_extract.py ) 3. using the equations to create the new morphology files from original swc file (shape_shifter.py). The python programs are all available from github.com/neurord/ShapeShifter We simulated the original morphologies and the morphologies with predicted diameter in Moose, evaluating the response to current injection and synaptic input. The code provided implements those simulations
Reference:
1 . Reed JD, Blackwell KT (2021) Prediction of Neural Diameter From Morphology to Enable Accurate Simulation. Front Neuroinform 15:666695 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Hippocampus CA1 pyramidal GLU cell; Cerebellum Purkinje GABA cell; Striatal projection neuron;
Channel(s):
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: MOOSE/PyMOOSE;
Model Concept(s):
Implementer(s): Blackwell, Avrama [avrama at gmu.edu]; Reed, Jonathon;
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; Cerebellum Purkinje GABA cell; AMPA;
Reference: Prediction of Neural Diameter From Morphology to Enable Accurate Simulation
Jonathan D Reed and Kim Blackwell
Fronteirs in Neuroinformatics, 2021

Many neuron morphologies in NeuroMorpho.org do not contain accurate dendritic diameters that are needed for simulations. We used a set of archives which did have realistic morphologies to derive equations predicting dendritic diameter, and to create morphologies using the predictions.
The equations and new morphologies are derived by
1. extracting morphology features from swc files (morph_feature_extract.py)
2. using multiple regression to derive equations predicting diameter, (morph_feature_extract.py )
3. using the equations to create the new morphology files from original swc file (shape_shifter.py).
The python programs are all available from github.com/neurord/ShapeShifter

We simulated the original morphologies and the morphologies with predicted diameter in Moose, evaluating the response to current injection and synaptic input. 

Usage:
A. current injection:
python3 MooseSim_2020.py pfile_name stim_delay stim_dur stim_amplitude sim_time
example:
python3 MooseSim_2020.py ri06_mod.CNG_org 5e-3 1e-3 1.5e-9 100e-3

B. synaptic input - make stim_amplitude 0:
python3 MooseSim_2020.py pfile_name stim_delay stim_dur 0 sim_time comp_name syn_conductance
example:
python3 MooseSim_2020.py Purkinje-slice-ageP35-1.CNG_org 10e-3 80e-3 0 400e-3 12_3 10e-9
python3 MooseSim_2020.py Purkinje-slice-ageP35-1.CNG_pred 10e-3 80e-3 0 400e-3 12_3 10e-9
python3 MooseSim_2020.py Purkinje-slice-ageP35-1.CNG_pred_i 10e-3 80e-3 0 400e-3 12_3 10e-9

After simulating both original and predicted morphologies, generate graphs comparing the morphologies by running
python3 compare_morph_sims.py

In compare_morph_sims.py, it is necessary to uncomment the file name and synapse compartment according to the files to be compared.

Running the three MooseSim_2020 simulations for the Purkinje file (in B above) and then compare_morph_sims.py will generate Fig 10B from the publication.


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