In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia (Sherif et al 2020)

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"Using a hippocampal CA3 computer model with 1200 neurons, we examined the effects of alterations in NMDAR, HCN (Ih current), and GABAAR on information flow (measured with normalized transfer entropy), and in gamma activity in local field potential (LFP). We found that altering NMDARs, GABAAR, Ih, individually or in combination, modified information flow in an inverted-U shape manner, with information flow reduced at low and high levels of these parameters. Theta-gamma phase-amplitude coupling also had an inverted-U shape relationship with NMDAR augmentation. The strong information flow was associated with an intermediate level of synchrony, seen as an intermediate level of gamma activity in the LFP, and an intermediate level of pyramidal cell excitability"
1 . Sherif MA, Neymotin SA, Lytton WW (2020) In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia. NPJ Schizophr 6:25 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron;
Channel(s): I h;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s): NR2A GRIN2A;
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON;
Model Concept(s): Schizophrenia;
Implementer(s): Sherif, Mohamed [ at];
Search NeuronDB for information about:  Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; AMPA; NMDA; I h; Gaba; Glutamate;
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# Hippocampal CA3 model (Sherif et al, 2020)

This is the code for the CA3 hippocampal model in the paper by [Sherif et al](, 2020, in npj Schizophrenia.

The code has been updated to use Python3. In addition to [NEURON](, the following python packages are needed:

(You can use conda or pip to install them).

After you install the required packages, compile the mod files:


To plot a figure similar to figure 1 in the paper with the control conditions, you will need first to run a control simulation. The configuraiton file with the parameters for the model are in fig1simulationConfig.cfg file.
The following code will run a simulation that is 3 seconds long (it takes around one minute to run on my machine with 8 cores), and save the resulting output file in directory ./data/batch.

After the file has been generated (or if you want to plot the provided output sample file), you can run the following code:

    python -i
Then inside python:

    simstr = 'controlSimulation' # name of simulation that just ran - set in the configuration file.

    loadedSim = loadSimH5py(simstr, datadir = './data/batch/')
    myfig, rastsp, lfpsp, psdsp = plotloadedsimProfiling(loadedSim, 0)
    rastsp.set_xlim(2000, 3000) # plot the last 1000 ms (1 second).

    # annotate y-axis of raster for the different neuronal populations
    rastsp.annotate('PYR', xy=(-0.1, 0.33), xycoords='axes fraction', color = 'red', fontsize = 12, fontweight='bold')
    rastsp.annotate('PV', xy=(-0.1, 0.73), xycoords='axes fraction', color = 'green', fontsize = 12, fontweight='bold')
    rastsp.annotate('OLM', xy=(-0.1, 0.88), xycoords='axes fraction', color = 'blue', fontsize = 12, fontweight='bold')

    # change color of LFP voltage plot to black
    lfpline = lfpsp.get_lines()[0]

    # change color of PSD plot to black
    psdline = psdsp.get_lines()[0]

You should get a figure similar to the one below:
![Alt text](fig1sample.png?raw=true "Optional Title")