Spike-Timing-Based Computation in Sound Localization (Goodman and Brette 2010)

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" ... In neuron models consisting of spectro-temporal filtering and spiking nonlinearity, we found that the binaural structure induced by spatialized sounds is mapped to synchrony patterns that depend on source location rather than on source signal. Location-specific synchrony patterns would then result in the activation of location-specific assemblies of postsynaptic neurons. We designed a spiking neuron model which exploited this principle to locate a variety of sound sources in a virtual acoustic environment using measured human head-related transfer functions. ..."
1 . Goodman DF, Brette R (2010) Spike-timing-based computation in sound localization. PLoS Comput Biol 6:e1000993 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s):
Gap Junctions:
Simulation Environment: Brian; Python;
Model Concept(s): Coincidence Detection; Synchronization;
Implementer(s): Goodman, Dan F. M. ;
from brian import *
from brian.hears import *
from brian.tools import datamanager
import os, sys, time, pickle

# shared samplerate, we use this for everything resampling if necessary
samplerate = 44.1*kHz

# base path for data, and derived DataManager class which uses it
datapath, _ = os.path.split(__file__)
datapath = os.path.normpath(os.path.join(datapath, './data'))

# convenience function to get the IRCAM database, replace the file path
# with the location you downloaded it to.
def get_ircam():
    ircam_locations = [
    for path in ircam_locations:
        if os.path.exists(path):
        raise IOError('Cannot find IRCAM HRTF location, add to ircam_locations in shared.py')
    ircam = IRCAM_LISTEN(path)
    return ircam

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