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]
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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# $Id:,v 1.2 2012/02/15 16:22:52 samn Exp $ 

from neuron import h
import os
import sys
import datetime
import shutil
import pickle
from math import sqrt, pi
import numpy
import types

h("objref p")
h("p = new PythonObject()")

    import pylab
    from pylab import plot, arange, figure
    my_pylab_loaded = True
except ImportError:
    print("Pylab not imported")
    my_pylab_loaded = False

def htype (obj): st=obj.hname(); sv=st.split('['); return sv[0]
def secname (obj): obj.push(); print(h.secname()) ; h.pop_section()
def psection (obj): obj.push(); print(h.psection()) ; h.pop_section()

allsecs=None #global list containing all NEURON sections, initialized via mkallsecs

# still need to generate a full allsecs
def mkallsecs ():
  """ mkallsecs - make the global allsecs variable, containing
      all the NEURON sections.
  global allsecs
  allsecs=h.SectionList() # no .clear() command
  for s in roots:
  return allsecs

#forall syntax - c gets executed, allsecs has Sections
def forall (c):
    """ NEURON forall syntax - iterates through all the sections available
        note that there's a dummy loop variable called s used in this function,
        so any command that needs to access a section should be via s.
        example: forall('print') , will print all the section names.
        Also note that this function uses a global list, 'allsecs', which may
        need to get re-initialized when new sections are created, via the mkallsecs
        function above.
    global allsecs
    if (type(allsecs)==type(None)): mkallsecs()
    for s in allsecs: exec(c)

#forsec syntax - executes command for each section who's name
# contains secname as a substring
def forsec (secref="soma",command=""): 
    """ NEURON forsec syntax - iterates over all sections which have a substring
        in their names matching secref argument. command is executed if match found.
        this function also utilizes the allsecs global variable.
    global allsecs
    if (type(allsecs)==type(None)): mkallsecs()
    if (type(secref)==types.StringTypes[0]):
        for s in allsecs:
            if > 0:
        for s in secref: exec(command)

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