Source code for cnmodel.populations.bushy

import scipy.stats
import numpy as np

from .population import Population
from .. import cells


class Bushy(Population):
    """Population of bushy cells.
    
    Cells are distributed uniformly from 2kHz to 64kHz.
    
    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).
    """
    type = 'bushy'
    
    def __init__(self, species='mouse', **kwds):
        freqs = self._get_cf_array(species)
        fields = [
            ('cf', float),
            ('sgc_sr', int),   # preferred SR group for SGC inputs
        ]
        super(Bushy, self).__init__(species, len(freqs), fields=fields, **kwds)
        self._cells['cf'] = freqs
        self._cells['sgc_sr'] = np.arange(len(freqs)) % 3
    
    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.Bushy.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 a single SR group
            # (this relationship is hypothesized based on reconstructions of
            # endbulbs)
            sr_vals = pop.cells['sr']
            dist['sr'] = (sr_vals == cell_rec['sgc_sr']).astype(float)

        return size, dist