Hippocampal CA3 thorny and a-thorny principal neuron models (Linaro et al in review)

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Accession:267307
This repository contains two populations of biophysically detailed models of murine hippocampal CA3 pyramidal neurons based on the two principal cell types that comprise this region. They are the result of a data-driven approach aimed at optimizing the model parameters by utilizing high-resolution morphological reconstructions and patch-clamp electrophysiology data together with a multi-objective optimization algorithm. The models quantitatively match the cell type-specific firing phenotypes and recapitulate the intrinsic population-level variability observed in the data. Additionally, the conductance values found by the optimization algorithm are consistent with differentially expressed ion channel genes in single-cell transcriptomic data for the two cell types. The models have further been employed to investigate the cell type-specific biophysical properties involved in the generation of complex-spiking output driven by synaptic input and to show that a-thorny bursting cells are capable of encoding more information in their firing output than their counterparts, thorny regular spiking neurons. Reference: Linaro D, Levy MJ, and Hunt, DL. Cell type-specific mechanisms of information transfer in data-driven biophysical models of hippocampal CA3 principal neurons. (2022) PLOS Computational Biology
Reference:
1 . Linaro D, Levy MJ, Hunt DL (in review) Cell type-specific mechanisms of information transfer in data-driven biophysical models of hippocampal CA3 principal neurons PLoS Computational Biology
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: Hippocampus;
Cell Type(s): Hippocampus CA3 pyramidal GLU cell;
Channel(s): I Na,t; I Na,p; I K; I K,Ca; I Calcium; I h;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Excitability; Synaptic Integration; Bursting; Action Potentials; Detailed Neuronal Models; Information transfer; Parameter Fitting;
Implementer(s): Linaro, Daniele [daniele.linaro at unige.it];
Search NeuronDB for information about:  Hippocampus CA3 pyramidal GLU cell; AMPA; NMDA; I Na,p; I Na,t; I K; I h; I K,Ca; I Calcium; 
Model files for the NEURON simulation environment for the paper

"Cell type-specific mechanisms of information transfer in data-driven biophysical models of hippocampal CA3 principal neurons"

by Daniele Linaro, Matthew J. Levy, and David L. Hunt

PLOS Computational Biology 2022



This repository contains the following folders:

1. mechanisms: the MOD-files used by the models.
2. morphologies: two cell morphologies in SWC format, one for each cell type.
3. individuals: two sets of parameters, one for each cell type, corresponding to several "individuals" obtained with a multi-objective optimization procedure.
4. configs: configuration files to be used with the script "synaptic_cooperativity.py".
5. dlutils: a small Python library necessary to run the code. This is part of the neuronopt package that can be freely downloaded from https://github.com/danielelinaro/neuronopt.



The following scripts can be used to simulate the models:

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current_injection.py

Runs a simple current injection protocol on a model cell.

Examples:

python3 current_injection.py -f morphologies/DH070813C1.swc -p individuals/DH070813C1/individual_0.json -c individuals/DH070813C1/parameters.json -n DH070813C1 --replace-axon yes --add-axon-if-missing yes --plot  -v --dur 1000 --delay 500 500

python3 current_injection.py -f morphologies/DH070213C3.swc -p individuals/DH070213C3/individual_0.json -c individuals/DH070213C3/parameters.json -n DH070213C3 --replace-axon yes --add-axon-if-missing yes --plot  -v --dur 1000 --delay 500 50


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fI_curve.py

Injects several steps of current in a model cell to compute its static input-output curve (f-I curve).

Examples:

python3 fI_curve.py -f morphologies/DH070813C1.swc -p individuals/DH070813C1/individual_1.json -c individuals/DH070813C1/parameters.json -n DH070813C1 --replace-axon yes --add-axon-if-missing yes --dur 1000 --delay 500 --tran 100 200:600:100

python3 fI_curve.py -f morphologies/DH070213C3.swc -p individuals/DH070213C3/individual_0.json -c individuals/DH070213C3/parameters.json -n DH070213C3 --replace-axon yes --add-axon-if-missing yes --dur 1000 --delay 500 --tran 0 0:250:50   


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measure_Rin.py

Injects a hyperpolarizing step of current into each compartment of a model cell to compute its input resistance. It can use SCOOP to run in parallel if the package is available on the platform.

Examples:

python3 -m scoop measure_Rin.py -f morphologies/DH070813C1.swc -p individuals/DH070813C1/individual_1.json -c individuals/DH070813C1/parameters.json -n DH070813C1 --replace-axon yes --add-axon-if-missing yes --dur 1000 --delay 500 --model-type passive --plot -o DH070813C1

python3 -m scoop measure_Rin.py -f morphologies/DH070213C3.swc -p individuals/DH070213C3/individual_0.json -c individuals/DH070213C3/parameters.json -n DH070213C3 --replace-axon yes --add-axon-if-missing yes --dur 1000 --delay 500 --model-type passive --plot -o DH070213C3


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measure_spine_AR.py

Injects an EPSP-shaped current into a spine head to measure the spine/dendrite amplitude ratio.

Examples:

python3 measure_spine_AR.py -F morphologies/DH070813C1.swc -p individuals/DH070813C1/individual_1.json -c individuals/DH070813C1/parameters.json -n DH070813C1 --replace-axon yes --add-axon-if-missing yes --model-type passive -O . -q 'apical[14](0.5)'

python3 ../neuronopt/measure_spine_AR.py -F morphologies/DH070213C3.swc -p individuals/DH070213C3/individual_0.json -c individuals/DH070213C3/parameters.json -n DH070213C3 --replace-axon yes --add-axon-if-missing yes --model-type passive -O . -q 'apical[16](0.5)'


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synaptic_cooperativity.py

Simulates the sequential activation of spines located on the dendritic tree of a model cell while also injecting an Ornstein-Uhlenbeck current in the soma to replicate ongoing background synaptic activity.

Examples:

python3 synaptic_cooperativity.py config/synaptic_inputs_config_thorny.json

python3 synaptic_cooperativity.py config/mutual_information_config_thorny.json



Each script has an extensive online help that can be accessed by typing

python3 <script-name> --help


For further questions on how to use this model please contact Daniele Linaro at the following email address: danielelinaro@gmail.com.

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