Interaural time difference detection by slowly integrating neurons (Vasilkov Tikidji-Hamburyan 2012)

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Accession:150445
For localization of a sound source, animals and humans process the microsecond interaural time differences of arriving sound waves. How nervous systems, consisting of elements with time constants of about and more than 1 ms, can reach such high precision is still an open question. This model shows that population of 10000 slowly integrating Hodgkin-Huxley neurons with inhibitory and excitatory inputs (EI neurons) can detect minute temporal disparities in input signals which are significantly less than any time constant in the system.
Reference:
1 . Vasilkov VA, Tikidji-Hamburyan RA (2012) Accurate detection of interaural time differences by a population of slowly integrating neurons. Phys Rev Lett 108:138104 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Auditory brainstem;
Cell Type(s): Hodgkin-Huxley neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Audition;
Implementer(s): Tikidji-Hamburyan, Ruben [ruben.tikidji.hamburyan at gmail.com] ; Vasilkov, Viacheslav [vasilkov.va at gmail.com];
#! /usr/bin/python
import sys,os,csv

if len(sys.argv) < 2:
	sys.stderr.write("USAGE:%s csv-file\n"%sys.argv[0])
	sys.exit(1)

ifd = open(sys.argv[1],"r")
ofd = open(sys.argv[1][:-4]+".dat","w")

line = ifd.readline()
line = line.split(",")
fITD = float(line[2])
tITD = float(line[3])
step = float(line[4])
N    = int((tITD-fITD)/step)
line = line[6:]

list = map(None,[tITD - (float(x)*step) for x in xrange(N+1)],line)
list = list[:-1]
list.sort()
for mp in  list:
	ofd.write("%g\t%s\n"%mp)
ifd.close()
ofd.close()