Orientation preference in L23 V1 pyramidal neurons (Park et al 2019)

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"Pyramidal neurons integrate synaptic inputs from basal and apical dendrites to generate stimulus-specific responses. It has been proposed that feed-forward inputs to basal dendrites drive a neuron’s stimulus preference, while feedback inputs to apical dendrites sharpen selectivity. However, how a neuron’s dendritic domains relate to its functional selectivity has not been demonstrated experimentally. We performed 2-photon dendritic micro-dissection on layer-2/3 pyramidal neurons in mouse primary visual cortex. We found that removing the apical dendritic tuft did not alter orientation-tuning. Furthermore, orientation-tuning curves were remarkably robust to the removal of basal dendrites: ablation of 2 basal dendrites was needed to cause a small shift in orientation preference, without significantly altering tuning width. Computational modeling corroborated our results and put limits on how orientation preferences among basal dendrites differ in order to reproduce the post-ablation data. In conclusion, neuronal orientation-tuning appears remarkably robust to loss of dendritic input."
1 . Park J, Papoutsi A, Ash RT, Marin MA, Poirazi P, Smirnakis SM (2019) Contribution of apical and basal dendrites to orientation encoding in mouse V1 L2/3 pyramidal neurons Nature Communications 10:5372
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L2/3 pyramidal GLU cell;
Channel(s): I L high threshold; I T low threshold; I A; I K,Ca; I M; I K; I Na,t;
Gap Junctions:
Receptor(s): GabaA; NMDA; AMPA;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Vision;
Implementer(s): Papoutsi, Athanasia [athpapoutsi at gmail.com];
Search NeuronDB for information about:  Neocortex L2/3 pyramidal GLU cell; GabaA; AMPA; NMDA; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I K,Ca; Gaba; Glutamate;
TITLE simple AMPA receptors


	Simple model for glutamate AMPA receptors


    Whole-cell recorded postsynaptic currents mediated by AMPA/Kainate
    receptors (Xiang et al., J. Neurophysiol. 71: 2552-2556, 1994) were used
    to estimate the parameters of the present model; the fit was performed
    using a simplex algorithm (see Destexhe et al., J. Computational Neurosci.
    1: 195-230, 1994).


    The simplified model was obtained from a detailed synaptic model that 
    included the release of transmitter in adjacent terminals, its lateral 
    diffusion and uptake, and its binding on postsynaptic receptors (Destexhe
    and Sejnowski, 1995).  Short pulses of transmitter with first-order
    kinetics were found to be the best fast alternative to represent the more
    detailed models.


    The first-order model can be solved analytically, leading to a very fast
    mechanism for simulating synapses, since no differential equation must be
    solved (see references below).


   Destexhe, A., Mainen, Z.F. and Sejnowski, T.J.  An efficient method for
   computing synaptic conductances based on a kinetic model of receptor binding
   Neural Computation 6: 10-14, 1994.  

   Destexhe, A., Mainen, Z.F. and Sejnowski, T.J. Synthesis of models for
   excitable membranes, synaptic transmission and neuromodulation using a 
   common kinetic formalism, Journal of Computational Neuroscience 1: 
   195-230, 1994.


	RANGE R, gmax, g             
	NONSPECIFIC_CURRENT  iglu             : i
	GLOBAL Cdur, Alpha, Beta, Erev, Rinf, Rtau
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(umho) = (micromho)
	(mM) = (milli/liter)

        Cmax	= 1	(mM)		: max transmitter concentration
	Cdur	= 0.3	(ms)		: transmitter duration (rising phase)
	Alpha	= 10	(/ms)		: forward (binding) rate
:	Beta	= 0.31	(/ms)		: backward (unbinding) rate 
	Beta	= 0.4	(/ms)		: backward (unbinding) rate Until March 2010, then changed 0.15
        Erev	= 0	(mV)		:0 reversal potential

	v		(mV)		: postsynaptic voltage
	iglu 		(nA)		: current = g*(v - Erev)     :i
	g 		(umho)		: conductance
	Rinf				: steady state channels open
	Rtau		(ms)		: time constant of channel binding

STATE {Ron Roff}

        Rinf = Cmax*Alpha / (Cmax*Alpha + Beta)
       	Rtau = 1 / ((Alpha * Cmax) + Beta)
	synon = 0

	SOLVE release METHOD cnexp
	g = (Ron + Roff)*1(umho)
	iglu = g*(v - Erev)  :i

DERIVATIVE release {
	Ron' = (synon*Rinf - Ron)/Rtau
	Roff' = -Beta*Roff

: following supports both saturation from single input and
: summation from multiple inputs
: if spike occurs during CDur then new off time is t + CDur
: ie. transmitter concatenates but does not summate
: Note: automatic initialization of all reference args to 0 except first

NET_RECEIVE(weight, on, nspike, r0, t0 (ms)) {
	: flag is an implicit argument of NET_RECEIVE and  normally 0
        if (flag == 0) { : a spike, so turn on if not already in a Cdur pulse
		nspike = nspike + 1
		if (!on) {
			r0 = r0*exp(-Beta*(t - t0))
			t0 = t
			on = 1
			synon = synon + weight
			state_discontinuity(Ron, Ron + r0)
			state_discontinuity(Roff, Roff - r0)
		: come again in Cdur with flag = current value of nspike
		net_send(Cdur, nspike)
	if (flag == nspike) { : if this associated with last spike then turn off
		r0 = weight*Rinf + (r0 - weight*Rinf)*exp(-(t - t0)/Rtau)
		t0 = t
		synon = synon - weight
		state_discontinuity(Ron, Ron - r0)
		state_discontinuity(Roff, Roff + r0)
		on = 0

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