Endocannabinoid dynamics gate spike-timing dependent depression and potentiation (Cui et al 2016)

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The endocannabinoid (eCB) system is considered involved in synaptic depression. Recent reports have also linked eCBs to synaptic potentiation. However it is not known how eCB signaling may support such bidirectionality. To question the mechanisms of this phenomena in spike-timing dependent plasticity (STDP) at corticostriatal synapses, we combined electrophysiology experiments with biophysical modeling. We demonstrate that STDP is controlled by eCB levels and dynamics: prolonged and moderate levels of eCB lead to eCB-mediated long-term depression (eCB-tLTD) while short and large eCB transients produce eCB-mediated long-term potentiation (eCB-tLTP). Therefore, just like neurotransmitters glutamate or GABA, eCB form a bidirectional system.
1 . Cui Y, Prokin I, Xu H, Delord B, Genet S, Venance L, Berry H (2016) Endocannabinoid dynamics gate spike-timing dependent depression and potentiation. Elife 5:e13185 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse; Channel/Receptor;
Brain Region(s)/Organism:
Cell Type(s): Neostriatum medium spiny direct pathway GABA cell; Neostriatum medium spiny indirect pathway GABA cell; Neostriatum spiny neuron;
Channel(s): I L high threshold; I Calcium; I_SERCA; I Cl, leak; Ca pump;
Gap Junctions:
Receptor(s): AMPA; NMDA; mGluR; Glutamate; IP3;
Simulation Environment: FORTRAN; Python;
Model Concept(s): Ion Channel Kinetics; Coincidence Detection; Parameter Fitting; Synaptic Plasticity; Long-term Synaptic Plasticity; Signaling pathways; STDP; Calcium dynamics; Parameter sensitivity; G-protein coupled; Neuromodulation;
Search NeuronDB for information about:  Neostriatum medium spiny direct pathway GABA cell; Neostriatum medium spiny indirect pathway GABA cell; AMPA; NMDA; mGluR; Glutamate; IP3; I L high threshold; I Calcium; I_SERCA; I Cl, leak; Ca pump;
!    -*- f95 -*-
! (c) 2016 - Ilya Prokin - isprokin@gmail.com - https://sites.google.com/site/ilyaprokin
! INRIA Rhone-Alpes
! STDP model : NMDAR model
module NMDA

    use pars_mod
    use general_math
    use ghk_flux
    implicit none

    type NMDA_tabs_type
    real*8, allocatable :: B(:)
    integer :: n
    real*8 :: xst, xfin, xstep
    end type NMDA_tabs_type

    type(NMDA_tabs_type), save :: NMDA_tabs


    subroutine NMDA_tables_make(xst,xfin,n_x,pars, NMDA_tabs)
        integer, intent(in) :: n_x
        real*8, intent(in) :: xst,xfin
        type(pars_type), intent(in) :: pars
        type(NMDA_tabs_type), intent(out) :: NMDA_tabs
        real*8 :: v(n_x), xstep
        integer :: i
        xstep = (xfin-xst)/n_x
        forall (i=1:n_x)
            v(i) = xst+xstep*(i-1)
            NMDA_tabs%B(i) = 1.0/(1+pars%NMDA%Mg/3.57*exp(-0.062*v(i)))
        end forall
    end subroutine NMDA_tables_make

    subroutine NMDA_tables_clean(NMDA_tabs)
        type(NMDA_tabs_type) :: NMDA_tabs
    end subroutine NMDA_tables_clean

    real*8 function NMDA_B(v)
        real*8, intent(in) :: v
        NMDA_B=lin_interpTable_brds(NMDA_tabs%B, NMDA_tabs%n, (v-NMDA_tabs%xst)/NMDA_tabs%xstep)
    end function NMDA_B

    real*8 function g_NMDA_func(V, o_NMDA)
        implicit none
        real*8 :: o_NMDA,V
        g_NMDA_func =  o_NMDA * NMDA_B(V)
    end function g_NMDA_func

    subroutine do_NMDA(Glu,o_NMDA,pars, d_o_NMDA)
        ! o_NMDA - probability of the open state
        implicit none
        real*8, intent(in) :: Glu,o_NMDA
        type(pars_type), intent(in) :: pars
        real*8, intent(out) :: d_o_NMDA
        d_o_NMDA = pars%NMDA%Alpha*Glu*(1-o_NMDA)-pars%NMDA%Beta*o_NMDA
    end subroutine do_NMDA

end module NMDA

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