Effects of the membrane AHP on the Lateral Superior Olive (LSO) (Zhou & Colburn 2010)

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Accession:143114
This simulation study investigated how membrane afterhyperpolarization (AHP) influences spiking activity of neurons in the Lateral Superior Olive (LSO). The model incorporates a general integrate-and-fire spiking mechanism with a first-order adaptation channel. Simulations focus on differentiating the effects of GAHP, tauAHP, and input strength on (1) spike interval statistics, such as negative serial correlation and chopper onset, and (2) neural sensitivity to interaural level difference (ILD) of LSO neurons. The model simulated electrophysiological data collected in cat LSO (Tsuchitani and Johnson, 1985).
Reference:
1 . Zhou Y, Colburn HS (2010) A modeling study of the effects of membrane afterhyperpolarization on spike interval statistics and on ILD encoding in the lateral superior olive. J Neurophysiol 103:2355-71 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Auditory brainstem;
Cell Type(s): Lateral Superior Olive (LSO) cell; Abstract integrate-and-fire leaky neuron;
Channel(s): I_AHP;
Gap Junctions:
Receptor(s): GabaA; Glutamate;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Action Potential Initiation; Simplified Models; Spike Frequency Adaptation; Depolarization block; Audition;
Implementer(s): Zhou, Yi [yizhou at bu.edu];
Search NeuronDB for information about:  GabaA; Glutamate; I_AHP;
TITLE Internal noisy channel

:
: Include internal noisy channels with density form
: With cut off frequency 

: in=-norm(mean,std)  

: by Yi Zhou for MSO use


INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
	POINT_PROCESS  current_gauss
	
	NONSPECIFIC_CURRENT  in  : negative current depolarizes the membrane
	RANGE del,dur
        
	RANGE in
	RANGE rand
	RANGE mean, std0, std
	RANGE f0  : the sampling frequency
	RANGE N_smooth : =1000/f0/dt
	RANGE count
	RANGE noise_seed
	
}


UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
}

PARAMETER {
	del=0 (ms)
	dur=100 (ms) <0,1e9>
	
	mean=0         (nA)  : default
	std0=1e-4       (nA)  : default
	std=1e-4
	noise_seed=1
    
	f0=4000   (1/s)   : default 4000Hz 
	
}



ASSIGNED {
	v      (mV)
	dt	(ms) 

	in	(nA)
	
	rand
	count
        N_smooth
	
}

PROCEDURE seed(x) {
	set_seed(x)
}


BREAKPOINT {

	if (t<del+dur && t>del) {

		if(count/N_smooth==1){  :error with the first N_smooth-1 zeros paddings, i.e., 1/f0 ms
			rand = normrand(mean, std)
			in  = -rand   : depolaring current
			count=0
		}else{
			count=count+1
		} 
	} else {
	  in=0 
	}

:printf("count=%g\n",count)

}



INITIAL {
	
	N_smooth=floor(1000/f0/dt)*2  : break point called twice,here 1000 is a scalar

		:### equalize the power of the sampled white noise
		:### std_a/std_b=sqrt(f_a/f_b) with f_a=4000 and std0=std_a : see notebook p186
	std=std0/sqrt(4000/f0)  
        	:###note that for Wiener process should be std_a/std_b=sqrt(f_b/f_a)
	
	rand = normrand(mean, std)
	in  = -rand

	count=0
	
	}

UNITSON




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