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88 lines
3.1 KiB
88 lines
3.1 KiB
2 years ago
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import logging
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import numpy as np
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import pyqtgraph.multiprocess as mp
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from .population import Population
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from .. import cells
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class SGC(Population):
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"""A population of spiral ganglion cells.
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The cell distribution is uniform from 2kHz to 64kHz, evenly divided between
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spontaneous rate groups.
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"""
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type = "sgc"
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def __init__(self, species="mouse", model="dummy", **kwds):
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# Completely fabricated cell distribution: uniform from 2kHz to 40kHz,
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# evenly divided between SR groups. We only go up to 40kHz because the
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# auditory periphery model does not support >40kHz.
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freqs = self._get_cf_array(species)
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fields = [("cf", float), ("sr", int)] # 0=low sr, 1=mid sr, 2=high sr
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super(SGC, self).__init__(
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species, len(freqs), fields=fields, model=model, **kwds
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)
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self._cells["cf"] = freqs
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# old version:
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# evenly distribute SR groups
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# self._cells['sr'] = np.arange(len(freqs)) % 3
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# new version:
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# draw from distributions matching approximate SR distribution
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sr_vals = self.get_sgcsr_array(freqs, species="mouse")
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self._cells["sr"] = sr_vals
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def set_seed(self, seed):
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self.next_seed = seed
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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.SGC.create(
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species=self.species,
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cf=cell_rec["cf"],
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sr=cell_rec["sr"],
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**self._cell_args
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)
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def connect_pop_to_cell(self, pop, index):
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# SGC does not support any inputs
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assert len(self.connections) == 0
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def set_sound_stim(self, stim, parallel=False):
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"""Set a sound stimulus to generate spike trains for all (real) cells
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in this population.
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"""
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real = self.real_cells()
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logging.info("Assigning spike trains to %d SGC cells..", len(real))
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if not parallel:
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for i, ind in enumerate(real):
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# logging.info("Assigning spike train to SGC %d (%d/%d)", ind, i, len(real))
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cell = self.get_cell(ind)
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cell.set_sound_stim(stim, self.next_seed)
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self.next_seed += 1
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else:
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seeds = range(self.next_seed, self.next_seed + len(real))
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self.next_seed = seeds[-1] + 1
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tasks = zip(seeds, real)
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trains = [None] * len(tasks)
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# generate spike trains in parallel
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with mp.Parallelize(
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enumerate(tasks),
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trains=trains,
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progressDialog="Generating SGC spike trains..",
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) as tasker:
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for i, x in tasker:
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seed, ind = x
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cell = self.get_cell(ind)
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train = cell.generate_spiketrain(stim, seed)
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tasker.trains[i] = train
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# collected all trains; now assign to cells
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for i, ind in enumerate(real):
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cell = self.get_cell(ind)
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cell.set_spiketrain(trains[i])
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