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];
strdef model, simpref, file_prefix
simpref		= "hh10000-delaygrad-scan11-step0.01.csv"
file_prefix	= "HH10000,delaygrad,-1,1,0.01," 

model = "hhcell.hoc"
dt = 0.005 		// integration step 5 mks(us)
tstop = 80		// full time of simulation
ncells = 10000		// number of cells
NITD = -1.0		//a scanning range in ms,
PITD =  1.0		//left and right boundary
scan_step=0.01		//scanning step 10 mks(us)
Relevant_ITD = 0.8	//a boundary (symmetric) of biological relevant intervals

run_flag = 0
gui_flag = 0		// 0 - batch mode




//Optimal values
//for HH  ex=0.002 ih=0.00389
//for MSO ex=0.009 ih=0.00395

//Synaptic conduct. linearly distribution and normal deviation
//Delay of spike propagation linearly distribution and normal deviation

//Left Excitation
LE_conduc_a		= 0.002		
LE_conduc_b		= 0.002
LE_delay_a		= 2
LE_delay_b		= 3
LE_delay_sd		= 0.0000

//Right Inhibition
RI_conduc_a		= 0.00389
RI_conduc_b		= 0.00389
RI_delay_a		= 3
RI_delay_b		= 2
RI_delay_sd		= 0.0000

//Right Excitation
RE_conduc_a		= 0.0
RE_conduc_b		= 0.0
RE_conduc_sd		= 0.0
RE_delay_a		= 2
RE_delay_b		= 2
RE_delay_sd		= 0.0000

//Left Inhibition
LI_conduc_a		= 0.00000
LI_conduc_b		= 0.00000
LI_conduc_sd		= 0.000
LI_delay_a		= 2
LI_delay_b		= 2
LI_delay_sd		= 0.0000


//Number of stimuli, interval between
//and Jitter Variation
RI_stimuli_number	= 1.0
RI_isi			= 2.0
LI_stimuli_number	= 1
LI_isi			= 2
RE_stimuli_number	= 1
RE_isi			= 2	//(ms)
RE_jitter_sd		= 0.0000	//max value of phase noise

LE_conduc_sd		= 0.0
RI_conduc_sd		= 0.0
RI_jitter_sd		= 0.0
LE_stimuli_number 	= 1.0
LE_isi			= 2.0

/*---------------------------------------------------------*/
//Neuron noise current paramters
nrn_idc 		= 0
nrn_isd			= 0.0

/*---------------------------------------------------------*/


load_file(model)
load_file("main.hoc")