CA1 pyramidal neuron: as a 2-layer NN and subthreshold synaptic summation (Poirazi et al 2003)

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Accession:20212
We developed a CA1 pyramidal cell model calibrated with a broad spectrum of in vitro data. Using simultaneous dendritic and somatic recordings, and combining results for two different response measures (peak vs. mean EPSP), two different stimulus formats (single shock vs. 50 Hz trains), and two different spatial integration conditions (within vs. between-branch summation), we found the cell's subthreshold responses to paired inputs are best described as a sum of nonlinear subunit responses, where the subunits correspond to different dendritic branches. In addition to suggesting a new type of experiment and providing testable predictions, our model shows how conclusions regarding synaptic arithmetic can be influenced by an array of seemingly innocuous experimental design choices.
References:
1 . Poirazi P, Brannon T, Mel BW (2003) Arithmetic of subthreshold synaptic summation in a model CA1 pyramidal cell. Neuron 37:977-87 [PubMed]
2 . Poirazi P, Brannon T, Mel BW (2003) Pyramidal neuron as two-layer neural network. Neuron 37:989-99 [PubMed]
3 . Poirazi P, Brannon T, Mel BW (2003ab-sup) Online Supplement: About the Model Neuron 37 Online:1-20
4 . Polsky A, Mel BW, Schiller J (2004) Computational subunits in thin dendrites of pyramidal cells. Nat Neurosci 7:621-7 [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:
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I Calcium;
Gap Junctions:
Receptor(s): GabaA; GabaB; NMDA; Glutamate;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Action Potential Initiation; Activity Patterns; Dendritic Action Potentials; Active Dendrites; Influence of Dendritic Geometry; Detailed Neuronal Models; Action Potentials; Depression; Delay;
Implementer(s): Poirazi, Panayiota [poirazi at imbb.forth.gr];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; GabaA; GabaB; NMDA; Glutamate; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I Calcium;
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CA1_multi
mechanism
not-currently-used
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abbott.o
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VClamp.omod *
                            
TITLE CaChan
: Calcium Channel with Goldman- Hodgkin-Katz permeability
: The fraction of open calcium channels has the same kinetics as
:   the HH m process but is slower by taufactor

UNITS {
	(molar) = (1/liter)
}

UNITS {
	(mV) =	(millivolt)
	(mA) =	(milliamp)
	(mM) =	(millimolar)
}


INDEPENDENT {t FROM 0 TO 1 WITH 100 (ms)}

NEURON {
	SUFFIX cachan
	USEION ca READ cai, cao WRITE ica
	RANGE pcabar, ica
}

UNITS {
	:FARADAY = 96520 (coul)
	:R = 8.3134 (joule/degC)
	FARADAY = (faraday) (coulomb)
	R = (k-mole) (joule/degC)
}

PARAMETER {
	taufactor=.5	: Time constant factor relative to standard HH
	celsius=20	(degC)
	v		(mV)
	pcabar=.2e-3	(cm/s)	: Maximum Permeability
	cai = 1e-3	(mM)
	cao = 10	(mM)
}

ASSIGNED { ica		(mA/cm2)}

STATE {	oca }		: fraction of open channels

BREAKPOINT {
	SOLVE castate METHOD euler
	ica = pcabar*oca*oca*ghk(v, cai, cao)
}

DERIVATIVE castate {
	oca' = (oca_ss(v) - oca)/oca_tau(v)
}

INITIAL {
	oca = oca_ss(v)
}

FUNCTION ghk(v(mV), ci(mM), co(mM)) (.001 coul/cm3) {
	LOCAL z, eci, eco
	z = (1e-3)*2*FARADAY*v/(R*(celsius+273.15))
	eco = co*efun(z)
	eci = ci*efun(-z)
	:high cao charge moves inward
	:negative potential charge moves inward
	ghk = (.001)*2*FARADAY*(eci - eco)
}

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

FUNCTION oca_ss(v(mV)) {
	LOCAL a, b
	TABLE FROM -150 TO 150 WITH 200
	
	v = v+65
	a = 1(1/ms)*efun(.1(1/mV)*(25-v))
	b = 4(1/ms)*exp(-v/18(mV))
	oca_ss = a/(a + b)
}

FUNCTION oca_tau(v(mV)) (ms) {
	LOCAL a, b
	TABLE FROM -150 TO 150 WITH 200

	v = v+65
	a = 1(1/ms)*efun(.1(1/mV)*(25-v))
	b = 4(1/ms)*exp(-v/18(mV))
	oca_tau = taufactor/(a + b)
}

COMMENT
This model is related to the passive model in that it also describes
a membrane channel. However it involves two new concepts in that the
channel is ion selective and the conductance of the channel is
described by a state variable.

Since many membrane mechanisms involve specific ions whose concentration
governs a channel current (either directly or via a Nernst potential) and since
the sum of the ionic currents of these mechanisms in turn may govern
the concentration, it is necessary that NEURON be explicitly told which
ionic variables are being used by this model and which are being computed.
This is done by the USEION statement.  This statement uses the indicated
base name for an ion (call it `base') and ensures the existance of
four range variables that can be used by any mechanism that requests them
via the USEION statement. I.e. these variables are shared by the different
mechanisms.  The four variables are the current, ibase; the
equilibrium potential, ebase; the internal concentration, basei; and the
external concentration, baseo. (Note that Ca and ca would be distinct
ion species).  The READ part of the statement lists the subset of these
four variables which are needed as input to the this model's computations.
Any changes to those variables within this mechanism will be lost on exit.
The WRITE part of the statement lists the subset which are computed by
the present mechanism.  If the current is computed, then it's value
on exit will be added to the neuron wide value of ibase and will also
be added to the total membrane current that is used to calculate the
membrane potential.

When this model is `insert'ed, fcurrent() executes all the statements
of the EQUATION block EXCEPT the SOLVE statement. I.e. the states are
NOT integrated in time.  The fadvance() function executes the entire
EQUATION block including the SOLVE statement; thus the states are integrated
over the interval t to t+dt.

Notice that several mechanisms can WRITE to ibase; but it is an error
if several mechanisms (in the same section) WRITE to ebase, baseo, or basei.

This model makes use of several variables known specially to NEURON. They are
celsius, v, and t.  It implicitly makes use of dt.

TABLE refers to a special type of FUNCTION in which the value of the
function is computed by table lookup with linear interpolation of
the table entries.  TABLE's are recomputed automatically whenever a
variable that the table depends on (Through the DEPEND list; not needed
in these tables) is changed.
The TABLE statement indicates the minimum and maximum values of the argument
and the number of table entries.  From NEURON, the function oca_ss_cachan(v)
returns the proper value in the table. When the variable "usetable_cachan"
is set to 0, oca_ss_cachan(v)returns the true function value.
Thus the table error can be easily plotted.
ENDCOMMENT

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