CA1 pyr cell: Inhibitory modulation of spatial selectivity+phase precession (Grienberger et al 2017)

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Spatially uniform synaptic inhibition enhances spatial selectivity and temporal coding in CA1 place cells by suppressing broad out-of-field excitation.
1 . Grienberger C, Milstein AD, Bittner KC, Romani S, Magee JC (2017) Inhibitory suppression of heterogeneously tuned excitation enhances spatial coding in CA1 place cells. Nat Neurosci 20:417-426 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Realistic Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Gap Junctions:
Receptor(s): NMDA;
Simulation Environment: NEURON; Python;
Model Concept(s): Active Dendrites; Detailed Neuronal Models; Place cell/field; Synaptic Integration; Short-term Synaptic Plasticity; Spatial Navigation; Feature selectivity;
Implementer(s): Milstein, Aaron D. [aaronmil at];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; NMDA;
ampa_kin.mod *
gaba_a_kin.mod *
kad.mod *
kap.mod *
kdr.mod *
nmda_kin5.mod *
pr.mod *
vecevent.mod *
__author__ = 'milsteina'
from specify_cells import *
import os
Builds a cell locally so each engine is ready to receive jobs one at a time, specified by an index corresponding to
which synapse to stimulate (coarse sampling of the full set of spines).

morph_filename = 'EB2-late-bifurcation.swc'

mech_filename = '043016 Type A - km2_NMDA_KIN5_Pr'

rec_filename = 'output''%m%d%Y%H%M')+'-pid'+str(os.getpid())

def stimulate_single_synapse(syn_index):
    :param syn_index: int
    :return: str
    start_time = time.time()
    branch = nodes[syn_index]
    sim.modify_rec(2, branch)
    sim.parameters['input_loc'] = branch.type
    syn = branch.synapses[0]
    with h5py.File(data_dir+rec_filename+'.hdf5', 'a') as f:
        sim.export_to_file(f, syn_index)  # playing an empty vector turns this synapse off for future runs while keeping the
                                 # VecStim source object in existence so it can be activated again
    print 'Process:', os.getpid(), 'completed Iteration:', syn_index, 'Node:',, 'in', \
        time.time() - start_time, 's'
    return rec_filename

equilibrate = 250.  # time to steady-state
duration = 350.
v_init = -67.
syn_type = 'EPSC'

#cell = CA1_Pyr(morph_filename, mech_filename, full_spines=False)
cell = CA1_Pyr(morph_filename, mech_filename, full_spines=True)

#cell.modify_mech_param('soma', 'cable', 'Ra', 200.)
#cell.modify_mech_param('trunk', 'pas', 'g', origin='soma')


nodes = cell.trunk  # cell.soma+cell.basal+cell.trunk+cell.apical+cell.tuft

for branch in nodes:
    syn = Synapse(cell, branch, [syn_type], stochastic=0) = 0.03

sim = QuickSim(duration, verbose=False)
sim.parameters['equilibrate'] = equilibrate
sim.parameters['duration'] = duration
sim.append_rec(cell, cell.tree.root, description='soma', loc=0.)

# look for a trunk bifurcation
trunk_bifurcation = [trunk for trunk in cell.trunk if cell.is_bifurcation(trunk, 'trunk')]
if trunk_bifurcation:
    trunk_branches = [branch for branch in trunk_bifurcation[0].children if branch.type == 'trunk']
    # get where the thickest trunk branch gives rise to the tuft
    trunk = max(trunk_branches, key=lambda node: node.sec(0.).diam)
    trunk = (node for node in cell.trunk if cell.node_in_subtree(trunk, node) and 'tuft' in (child.type
                                                                            for child in node.children)).next()
    trunk_bifurcation = [node for node in cell.trunk if 'tuft' in (child.type for child in node.children)]
    trunk = trunk_bifurcation[0]
tuft = (child for child in trunk.children if child.type == 'tuft').next()
#distal_trunk = trunk
#trunk = trunk_bifurcation[0]

sim.append_rec(cell, trunk, description='trunk', loc=0.)
sim.append_rec(cell, trunk, description='branch')  # placeholder for branch

spike_times = h.Vector([equilibrate])

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