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

 Download zip file 
Help downloading and running models
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]
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 V1 L2/6 pyramidal intratelencephalic 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 V1 L2/6 pyramidal intratelencephalic GLU cell;
TITLE AMPA and NMDA receptor with presynaptic short-term plasticity 

AMPA and NMDA receptor conductance using a dual-exponential profile
Based on Jahr and Stevens 1990 equations 



        RANGE  tau_r_NMDA, tau_d_NMDA,n_NMDA,gama_NMDA
        RANGE Use
        RANGE i,  i_NMDA,  g_NMDA, e, gmax


    	n_NMDA = 0.28011 (/mM)	
    	gama_NMDA = 0.062 (/mV) 
	   tau_r_NMDA = 0.3   (ms) : dual-exponential conductance profile
        tau_d_NMDA = 43     (ms) : IMPORTANT: tau_r < tau_d
        Use = 1.0   (1)   : Utilization of synaptic efficacy (just initial values! Use, Dep and Fac are overwritten by BlueBuilder assigned values) 

        e = 0     (mV)  : AMPA and NMDA reversal potential
	    mg = 1   (mM)  : initial concentration of mg2+
    	:gmax = .001 (uS) :1nS weight conversion factor (from nS to uS)
    	u0 = 0 :initial value of u, which is the running value of Use

The Verbatim block is needed to generate random nos. from a uniform distribution between 0 and 1 
for comparison with Pr to decide whether to activate the synapse or not



        v (mV)
        i (nA)
	i_NMDA (nA)
	g_NMDA (uS)


	A_NMDA       : NMDA state variable to construct the dual-exponential profile - decays with conductance tau_r_NMDA
    B_NMDA       : NMDA state variable to construct the dual-exponential profile - decays with conductance tau_d_NMDA


    LOCAL  tp_NMDA
	A_NMDA = 0
	B_NMDA = 0
	tp_NMDA = (tau_r_NMDA*tau_d_NMDA)/(tau_d_NMDA-tau_r_NMDA)*log(tau_d_NMDA/tau_r_NMDA) :time to peak of the conductance
	factor_NMDA = -exp(-tp_NMDA/tau_r_NMDA)+exp(-tp_NMDA/tau_d_NMDA) :NMDA Normalization factor - so that when t = tp_NMDA, gsyn = gpeak
    factor_NMDA = 1/factor_NMDA


    SOLVE state METHOD cnexp
	mggate = 1 / (1 + exp(gama_NMDA  * -(v)) * (n_NMDA)) :mggate kinetics - Jahr & Stevens 1990
	g_NMDA = (B_NMDA-A_NMDA) * mggate :compute time varying conductance as the difference of state variables B_NMDA and A_NMDA and mggate kinetics
	i_NMDA = g_NMDA*(v-e) :compute the NMDA driving force based on the time varying conductance, membrane potential, and NMDA reversal
	i =  i_NMDA


	A_NMDA' = -A_NMDA/tau_r_NMDA
    B_NMDA' = -B_NMDA/tau_d_NMDA

NET_RECEIVE (weight, weight_NMDA){
	weight_NMDA = weight

	A_NMDA = A_NMDA + weight_NMDA*factor_NMDA
    B_NMDA = B_NMDA + weight_NMDA*factor_NMDA


Loading data, please wait...