Distinct integration properties of noisy inputs in active dendritic subunits (Poleg-Polsky 2019)

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Accession:259732
The brain operates surprisingly well despite the noisy nature of individual neurons. The central mechanism for noise mitigation in the nervous system is thought to involve averaging over multiple noise-corrupted inputs. Subsequently, there has been considerable interest recently to identify noise structures that can be integrated linearly in a way that preserves reliable signal encoding. By analyzing realistic synaptic integration in biophysically accurate neuronal models, I report a complementary de-noising approach that is mediated by focal dendritic spikes. Dendritic spikes might seem to be unlikely candidates for noise reduction due to their miniscule integration compartments and poor averaging abilities. Nonetheless, the extra thresholding step introduced by dendritic spike generation increases neuronal tolerance for a broad category of noise structures, some of which cannot be resolved well with averaging. This property of active dendrites compensates for compartment size constraints and expands the repertoire of conditions that can be processed by neuronal populations.
Reference:
1 . Poleg-Polsky A (2019) Dendritic spikes expand the range of well-tolerated population noise structures. J Neurosci [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse; Dendrite; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L2/3 pyramidal GLU cell; Neocortex primary motor area pyramidal layer 5 corticospinal cell;
Channel(s): I Na,t; I Potassium;
Gap Junctions:
Receptor(s): AMPA; GabaA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Synaptic Integration; Active Dendrites; Information transfer;
Implementer(s): Polsky, Alon [alonpol at tx.technion.ac.il];
Search NeuronDB for information about:  Neocortex L2/3 pyramidal GLU cell; GabaA; AMPA; NMDA; I Na,t; I Potassium; Gaba; Glutamate;
COMMENT
//******************************************//
// Created by Alon Poleg-Polsky 			//
//    alon.poleg-polsky@ucdenver.edu		//
//		2018								//
//******************************************//
ENDCOMMENT

TITLE Glutamatergic synapse with network activation

NEURON {
	POINT_PROCESS glutamate
	NONSPECIFIC_CURRENT inmda,iampa
	RANGE e ,gAMPAmax,gNMDAmax,inmda,iampa

	RANGE gnmda,gampa,dend,pos,locx,locy,local_v
	RANGE stim,tt

	GLOBAL n, gama,tau_ampa,Pr
	GLOBAL tau1,tau2
	GLOBAL Voff,Vset

}

UNITS {
	(nA) 	= (nanoamp)
	(mV)	= (millivolt)
	(nS) 	= (nanomho)
	(mM)    = (milli/liter)
        F	= 96480 (coul)
        R       = 8.314 (volt-coul/degC)
 	PI = (pi) (1)
	(mA) = (milliamp)
	(um) = (micron)

}

PARAMETER {
	gNMDAmax=0.5	(nS)
	gAMPAmax=0.5	(nS)
	e= 0.0	(mV)
	tau1=50	(ms)	
	tau2=2	(ms)	
	tau_ampa=1	(ms)	
	n=0.25 	(/mM)	
	gama=0.08 	(/mV) 
	dt (ms)
	v		(mV)
	dend=0
	pos=0
	locx=0
	locy=0
	Pr=.8
	Voff      =0		:0 - voltage dependent 1- voltage independent
	Vset      =-60		:set voltage when voltage independent		
	stim=0	
}

ASSIGNED {
	inmda		(nA)  
	iampa		(nA)  
	gnmda		(nS)
	local_v
	tt
}
STATE {
	A 		(nS)
	B 		(nS)
	gampa 	(nS)

}

INITIAL {
    gnmda=0 
    gampa=0 
	A=0
	B=0
	stim=0
	tt=0
}    

BREAKPOINT {  
	if((stim==1)&&(tt<t)){
		if(scop_random()<=Pr){
			state_discontinuity( A, A+ gNMDAmax)
			state_discontinuity( B, B+ gNMDAmax)
			state_discontinuity( gampa, gampa+ gAMPAmax)
			tt=t+2
		}
	}
	SOLVE state METHOD cnexp
	local_v  =v*(1-Voff)+Vset*Voff	:temp voltage
	gnmda    =(A-B)/(1+n*exp(-gama*local_v) )
	inmda =(1e-3)*gnmda* (v-e)
	iampa= (1e-3)*gampa* (v- e)
}

DERIVATIVE state {
	A'=-A/tau1
	B'=-B/tau2
	gampa'=-gampa/tau_ampa
}

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