model of DCN pyramidal neuron
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import scipy.stats
import numpy as np
from .population import Population
from .. import cells
class TStellate(Population):
type = "tstellate"
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(TStellate, 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.TStellate.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):
# Select SGC inputs from a all SR groups
sr_vals = pop.cells["sr"]
# print('SRs for TS: ', np.bincount(sr_vals)/sr_vals.shape[0], np.unique(sr_vals))
dist["sr"] = (sr_vals < 3).astype(float)
return size, dist