model of DCN pyramidal neuron
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import scipy.stats
import numpy as np
from .population import Population
from .. import cells
class Tuberculoventral(Population):
type = "tuberculoventral"
def __init__(self, species="mouse", **kwds):
# Note that `cf` is the mean value used when selecting SGCs to connect;
# it is NOT the measured CF of the cell (although it should be close).
freqs = self._get_cf_array(species)
fields = [("cf", float), ("input_sr", list)]
super(Tuberculoventral, self).__init__(
species, len(freqs), fields=fields, **kwds
)
self._cells["cf"] = freqs
self._cells["input_sr"] = [np.tile([1.0, 1.0, 1.0], len(freqs))]
def create_cell(self, cell_rec):
""" Return a single new cell to be used in this population. The
*cell_rec* argument is the row from self.cells that describes the cell
to be created.
"""
return cells.Tuberculoventral.create(species=self.species, **self._cell_args)
def connection_stats(self, pop, cell_rec):
""" The population *pop* is being connected to the cell described in
*cell_rec*. Return the number of presynaptic cells that should be
connected and a dictionary of distributions used to select cells
from *pop*.
"""
size, dist = Population.connection_stats(self, pop, cell_rec)
from .. import populations
if isinstance(pop, populations.SGC):
# only select SGC inputs from low- and medium SR. See:
# Spectral Integration by Type II Interneurons in Dorsal Cochlear Nucleus
# George A. Spirou, Kevin A. Davis, Israel Nelken, Eric D. Young
# Journal of Neurophysiology Aug 1999, 82 (2) 648-663;
dist["sr"] = (pop.cells["sr"] < 2).astype(float)
return size, dist