Human L2/3 pyramidal cells with low Cm values (Eyal et al. 2016)

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The advanced cognitive capabilities of the human brain are often attributed to our recently evolved neocortex. However, it is not known whether the basic building blocks of human neocortex, the pyramidal neurons, possess unique biophysical properties that might impact on cortical computations. Here we show that layer 2/3 pyramidal neurons from human temporal cortex (HL2/3 PCs) have a specific membrane capacitance (Cm) of ~0.5 µF/cm2, half of the commonly accepted “universal” value (~1 µF/cm2) for biological membranes. This finding was predicted by fitting in vitro voltage transients to theoretical transients then validated by direct measurement of Cm in nucleated patch experiments. Models of 3D reconstructed HL2/3 PCs demonstrated that such low Cm value significantly enhances both synaptic charge-transfer from dendrites to soma and spike propagation along the axon. This is the first demonstration that human cortical neurons have distinctive membrane properties, suggesting important implications for signal processing in human neocortex.
1 . Eyal G, Verhoog MB, Testa-Silva G, Deitcher Y, Lodder JC, Benavides-Piccione R, Morales J, DeFelipe J, de Kock CP, Mansvelder HD, Segev I (2016) Unique membrane properties and enhanced signal processing in human neocortical neurons. Elife [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Dendrite;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L2/3 pyramidal GLU cell;
Gap Junctions:
Simulation Environment: Python; NEURON;
Model Concept(s): Action Potential Initiation; Parameter Fitting; Membrane Properties;
Implementer(s): Eyal, Guy [guy.eyal at];
Search NeuronDB for information about:  Neocortex L2/3 pyramidal GLU cell;

Potassium channel, Hodgkin-Huxley style kinetics
Kinetic rates based roughly on Sah et al. and Hamill et al. (1991)

Author: Zach Mainen, Salk Institute, 1995,
26 Ago 2002 Modification of original channel to allow 
variable time step and to correct an initialization error.
Done by Michael Hines(michael.hines@yale.e) and 
Ruggero Scorcioni( at EU Advance Course 
in Computational Neuroscience. Obidos, Portugal

20110202 made threadsafe by Ted Carnevale
20120514 fixed singularity in PROCEDURE rates

Special comment:

This mechanism was designed to be run at a single operating 
temperature--37 deg C--which can be specified by the hoc 
assignment statement
celsius = 37
This mechanism is not intended to be used at other temperatures, 
or to investigate the effects of temperature changes.

Zach Mainen created this particular model by adapting conductances 
from lower temperature to run at higher temperature, and found it 
necessary to reduce the temperature sensitivity of spike amplitude 
and time course.  He accomplished this by increasing the net ionic 
conductance through the heuristic of changing the standard HH 
  g = gbar*product_of_gating_variables
  g = tadj*gbar*product_of_gating_variables
  tadj = q10^((celsius - temp)/10)
  temp is the "reference temperature" (at which the gating variable
    time constants were originally determined)
  celsius is the "operating temperature"

Users should note that this is equivalent to changing the channel 
density from gbar at the "reference temperature" temp (the 
temperature at which the at which the gating variable time 
constants were originally determined) to tadj*gbar at the 
"operating temperature" celsius.

	RANGE n, gk, gbar
	RANGE ninf, ntau
	GLOBAL q10, temp, tadj, vmin, vmax

	(mA) = (milliamp)
	(mV) = (millivolt)
	(pS) = (picosiemens)
	(um) = (micron)

	gbar = 5   	(pS/um2)	: 0.03 mho/cm2
	tha  = 25	(mV)		: v 1/2 for inf
	qa   = 9	(mV)		: inf slope		
	Ra   = 0.02	(/ms)		: max act rate
	Rb   = 0.002	(/ms)		: max deact rate	

:	dt		(ms)
	temp = 23	(degC)		: original temp 	
	q10  = 2.3			: temperature sensitivity

	vmin = -120	(mV)
	vmax = 100	(mV)

	v 		(mV)
	celsius		(degC)
	a		(/ms)
	b		(/ms)
	ik 		(mA/cm2)
	gk		(pS/um2)
	ek		(mV)
	ntau (ms)	

STATE { n }

    tadj = q10^((celsius - temp)/(10 (degC))) : make all threads calculate tadj at initialization

	n = ninf

        SOLVE states METHOD cnexp
	gk = tadj*gbar*n
	ik = (1e-4) * gk * (v - ek)

DERIVATIVE  states {   :Computes state variable n 
        trates(v)      :             at the current v and dt.
        n' =  (ninf-n)/ntau

PROCEDURE trates(v (mV)) {  :Computes rate and other constants at current v.
                      :Call once from HOC to initialize inf at resting v.
    TABLE ninf, ntau
    DEPEND celsius, temp, Ra, Rb, tha, qa
    FROM vmin TO vmax WITH 199

	rates(v): not consistently executed from here if usetable_hh == 1

:        tinc = -dt * tadj
:        nexp = 1 - exp(tinc/ntau)

PROCEDURE rates(v (mV)) {  :Computes rate and other constants at current v.
                      :Call once from HOC to initialize inf at resting v.

    : singular when v = tha
:    a = Ra * (v - tha) / (1 - exp(-(v - tha)/qa))
:    a = Ra * qa*((v - tha)/qa) / (1 - exp(-(v - tha)/qa))
:    a = Ra * qa*(-(v - tha)/qa) / (exp(-(v - tha)/qa) - 1)
    a = Ra * qa * efun(-(v - tha)/qa)

    : singular when v = tha
:    b = -Rb * (v - tha) / (1 - exp((v - tha)/qa))
:    b = -Rb * qa*((v - tha)/qa) / (1 - exp((v - tha)/qa))
:    b = Rb * qa*((v - tha)/qa) / (exp((v - tha)/qa) - 1)
    b = Rb * qa * efun((v - tha)/qa)

        tadj = q10^((celsius - temp)/10)
        ntau = 1/tadj/(a+b)
	ninf = a/(a+b)

FUNCTION efun(z) {
	if (fabs(z) < 1e-4) {
		efun = 1 - z/2
		efun = z/(exp(z) - 1)