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44 lines
1.6 KiB
44 lines
1.6 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 TStellate(Population):
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type = "tstellate"
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def __init__(self, species="mouse", **kwds):
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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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freqs = self._get_cf_array(species)
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fields = [("cf", float), ("input_sr", list)]
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super(TStellate, 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.TStellate.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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# Select SGC inputs from a all SR groups
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sr_vals = pop.cells["sr"]
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# print('SRs for TS: ', np.bincount(sr_vals)/sr_vals.shape[0], np.unique(sr_vals))
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dist["sr"] = (sr_vals < 3).astype(float)
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return size, dist
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