model of DCN pyramidal neuron
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"""
Test connection between two cell populations.
Usage: python test_populations.py <pre_celltype> <post_celltype>
This script:
1. Creates two cell populations (pop1 and pop2)
2. Connects pop1 => pop2
3. Instantiates a single cell in pop2
4. Automatically generates presynaptic cells and synapses from pop1
5. Stimulates presynaptic cells and records postsynaptically
This is a high-level approach to generating networks in that the supporting
cells (those in pop1) are created automatically based on expected patterns
of connectivity in the cochlear nucleus. A lower-level approach is demonstrated
in test_synapses.py, in which the individual pre- and postsynaptic cells are
manually created and connected.
"""
from cnmodel import populations
from cnmodel.protocols import PopulationTest
import pyqtgraph as pg
import sys
def testpopulation():
if len(sys.argv) < 3:
print("Usage: python test_populations.py <pre_celltype> <post_celltype>")
sys.exit(1)
pop_types = {
"sgc": populations.SGC,
"bushy": populations.Bushy,
"tstellate": populations.TStellate,
"dstellate": populations.DStellate,
"pyramidal": populations.Pyramidal,
"tuberculoventral": populations.Tuberculoventral,
}
pops = []
for cell_type in sys.argv[1:3]:
if cell_type not in pop_types:
print(
'\nUnsupported cell type: "%s". Options are %s'
% (cell_type, list(pop_types.keys()))
)
sys.exit(-1)
pops.append(pop_types[cell_type]())
pt = PopulationTest()
pt.run(pops)
pt.show()
if sys.flags.interactive == 0:
pg.QtGui.QApplication.exec_()
if __name__ == "__main__":
testpopulation()