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	//
// 2017								//
//**********************************//
ENDCOMMENT
TITLE Stochastic Hodgkin and Huxley model & M-type potassium & T-and L-type Calcium channels incorporating channel noise .

COMMENT

Based on - Accurate and fast simulation of channel noise in conductance-based model neurons. Linaro, D., Storace, M., and Giugliano, M. 
Added: 
	Km T L channels
	fixed minor bugs and grouped variables into arrays 

ENDCOMMENT

UNITS {
    (mA) = (milliamp)
    (mV) = (millivolt)
    (S)  = (siemens)
    (pS) = (picosiemens)
    (um) = (micrometer)
} : end UNITS


NEURON {
    SUFFIX HH
    USEION na READ ena WRITE ina
    USEION k READ ek WRITE ik
:	NONSPECIFIC_CURRENT ileak
:	RANGE eleak, gleak,NFleak
    RANGE gnabar, gkbar
    RANGE gna, gk
	RANGE nm,  gkmbar
	RANGE m_exp, h_exp, n_exp, km_exp
	RANGE km_inf, tau_km
    RANGE m_inf, h_inf, n_inf	

    GLOBAL seed    

	GLOBAL vshift

	GLOBAL taukm,NF
    THREADSAFE
} : end NEURON


PARAMETER {
    gnabar  = 0.12   (S/cm2)
    gkbar   = 0.036  (S/cm2)
	gkmbar = .002   	(S/cm2)
    :gleak=0.00001		(S/cm2)
	:eleak=-60 			(mV)
	:NFleak=1
    gamma_na = 10  (pS)		: single channel sodium conductance
    gamma_k  = 10  (pS)		: single channel potassium conductance
    gamma_km  = 10  (pS)		: single channel potassium conductance
    seed = 1              : always use the same seed
	vshift=0				:Voltage shift of the recorded memebrane potential (to offset for liquid junction potential
	taukm=1					:speedup of Km channels
	NF=1					:Noise Factor (set to 0 to zero the noise part)
	hslow=100
	hfast=0.3

} : end PARAMETER


STATE {
    m h n nm 

} : end STATE


ASSIGNED {
    ina        (mA/cm2)
	ikdr	(mA/cm2)
	ikm	 (mA/cm2)
	ik		(mA/cm2)
	:ileak	(mA/cm2)
    gna        (S/cm2)
    gk         (S/cm2)
	gkm         (S/cm2)
    ena        (mV)
    ek         (mV)
   
    dt         (ms)
    area       (um2)
    celsius    (degC)
    v          (mV)
        
    m_exp h_exp n_exp km_exp 
    m_inf h_inf n_inf km_inf 
    tau_m (ms) tau_h (ms) tau_n (ms) tau_km (ms) 



} : end ASSIGNED

INITIAL {
    
    rates(v)
    m = m_inf
    h = h_inf
    n = n_inf
	nm=km_inf
	

    set_seed(seed)
} : end INITIAL


BREAKPOINT {
    SOLVE states
    gna = gnabar * ((m)*(m)*(m)*(h))

    ina = gna * (v - ena)
    gk = gkbar * ((n)*(n)*(n)*(n))

    ikdr  = gk * (v - ek)
	gkm=gkmbar*((nm))

	ikm = gkm*(v-ek)
	ik=ikm+ikdr
	

	:ileak  = (gleak) * (v - eleak)
} : end BREAKPOINT


PROCEDURE states() {
    rates(v+vshift)
	m = m + m_exp * (m_inf - m)
	h = h + h_exp * (h_inf - h)
    n = n + n_exp * (n_inf - n)
	nm = nm + km_exp*(km_inf-nm)


    VERBATIM
    return 0;
    ENDVERBATIM
} : end PROCEDURE states()


PROCEDURE rates(vm (mV)) { 
    LOCAL a,b,i
    
    UNITSOFF
    
    
 :NA m
	a =-.6 * vtrap((vm+30),-10)	
	b = 20 * (exp((-1*(vm+55))/18))
	tau_m = 1 / (a + b)
	m_inf =addnoise( a * tau_m)

 
:NA h
	a = 0.4 * (exp((-1*(vm+50))/20))
	b = 6 / ( 1 + exp(-0.1 *(vm+20)))
	tau_h=hslow/((1+exp((vm+30)/4))+(exp(-(vm+50)/2)))+hfast
	h_inf=addnoise( 1/(1 + exp((vm + 44)/4)))
	
   
:K n (non-inactivating, delayed rectifier)
	a = -0.02 * vtrap((vm+40),-10)
	b = 0.4 * (exp((-1*(vm + 50))/80))
	tau_n = 1 / (a + b)
	n_inf =addnoise( a * tau_n)

:Km
    a = -.001/taukm * vtrap((vm+30),-9)
    b =.001/taukm * vtrap((vm+30),9) 
    tau_km = 1/(a+b)
	km_inf = addnoise(a*tau_km)

	m_exp = 1 - exp(-dt/tau_m)
	h_exp = 1 - exp(-dt/tau_h)
	n_exp = 1 - exp(-dt/tau_n)
	km_exp= 1 - exp(-dt/tau_km)	

	
   UNITSON
}

FUNCTION vtrap(x,y) {  :Traps for 0 in denominator of rate eqns.
    if (fabs(exp(x/y) - 1) < 1e-6) {
        vtrap = y*(1 - x/y/2)
    }else{
        vtrap = x/(exp(x/y) - 1)
    }
}
FUNCTION addnoise(input){
	addnoise=input
	if(NF>0){
		addnoise=input*normrand(1,NF*input*(1-input))
	}
	if(addnoise<0){addnoise=0}
	if(addnoise>1){addnoise=1}
	

}


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