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59 lines
2.1 KiB
59 lines
2.1 KiB
2 years ago
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import scipy.stats
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import numpy as np
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from .population import Population
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from .. import cells
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class Bushy(Population):
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"""Population of bushy cells.
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Cells are distributed uniformly from 2kHz to 64kHz.
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Note that `cf` is the mean value used when selecting SGCs to connect;
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it is NOT the measured CF of the cell (although it should be close).
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"""
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type = "bushy"
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def __init__(self, species="mouse", **kwds):
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freqs = self._get_cf_array(species)
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fields = [
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("cf", float),
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("input_sr", list), # distribution probability of SGC SR groups
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("sr", int),
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]
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super(Bushy, self).__init__(species, len(freqs), fields=fields, **kwds)
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self._cells["cf"] = freqs
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self._cells["input_sr"] = [np.tile([1.0, 1.0, 1.0], len(freqs))]
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def create_cell(self, cell_rec):
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""" Return a single new cell to be used in this population. The
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*cell_rec* argument is the row from self.cells that describes the cell
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to be created.
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"""
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return cells.Bushy.create(species=self.species, **self._cell_args)
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def connection_stats(self, pop, cell_rec):
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""" The population *pop* is being connected to the cell described in
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*cell_rec*. Return the number of presynaptic cells that should be
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connected and a dictionary of distributions used to select cells
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from *pop*.
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"""
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size, dist = Population.connection_stats(self, pop, cell_rec)
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from .. import populations
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if isinstance(pop, populations.SGC):
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# only select SGC inputs from a single SR group
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# (this relationship is hypothesized based on reconstructions of
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# endbulbs)
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sr_vals = pop.cells["sr"]
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u = np.random.choice(sr_vals) # assign input sr for this cell
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# print('u: ', u)
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# pick from one sr group for all inputs, with prob same as distribution in nerve
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dist["sr"] = (sr_vals == u).astype(float)
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self._cells["sr"] = u
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return size, dist
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