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

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Accession:187605
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.
Reference:
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;
Gene(s):
Transmitter(s):
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;
Implementer(s):
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;
# (c) 2016 - Ilya Prokin - isprokin@gmail.com - https://sites.google.com/site/ilyaprokin
# INRIA Rhone-Alpes
# STDP model : helper functions

from copy import deepcopy
import numpy as np
import solve_py

def pmap(f, xs):
    from multiprocessing import cpu_count, Pool
    p = Pool(cpu_count())
    Y = p.map(f, xs)
    p.close()
    p.join()
    return Y


def interpolate_and_gsmooth(x, y, xnew, sdx):
    # x,y,xnew, sdx - in units of x/xnew
    from scipy.ndimage.filters import gaussian_filter
    ynew = np.interp(xnew, x, y)
    ys = gaussian_filter(ynew, sdx/(xnew[1]-xnew[0]))
    return ys


def nesteddict_to_lists_r(d, keys=None):
    from collections import OrderedDict
    """converts nested dict to the list of keys and the list of values
    keys - is the selecton of top level keys to be considered
    (all top keys taken if not set)
    """
    lv, lk = [], []
    if keys is None:
        keys = d.keys()
    for k in keys:
        if type(d[k]) in [dict, OrderedDict]:
            lvx, lkx = nesteddict_to_lists_r(d[k])
            lv += lvx
            lk += [k+', '+lkx1 for lkx1 in lkx]
        else:
            lv.append(d[k])
            lk.append(k)
    return lv, lk


def find_y0(y0, lv, lk, t_end=200, t_step=1e-3):
    lv0 = deepcopy(lv)
    for ind in [lk.index('stimulation, pre_on'), lk.index('stimulation, post_on'), lk.index('stimulation, num_stim')]:
        lv0[ind] = 0
    t = np.arange(0, t_end, t_step)
    y, ISTATE = solve_py.lsoda_calc(y0, lv0, t)
    return y[:,-1] # that's it!


def find_for_cnd(lv, lk, y0, t, cnd):
    lv1 = deepcopy(lv)
    lv1 = np.array(lv1)
    lv1[map(lambda k: lk.index(k), cnd.keys())] = cnd.values()
    y, ISTATE = solve_py.lsoda_calc(find_y0(y0, lv1, lk), lv1, t)
    return y, lv1


def fpre_fpost_y_4F(y, sifpre,
                    fpost_func=lambda CaMKact_t: CaMKact_t[-1]*3.5/164.6+1):
    y = y[:, [0, -1]]
    CaMKact_t = solve_py.extras.camkiipho_4f(y[:13, :])
    fpost = fpost_func(CaMKact_t)
    fpre = y[sifpre, -1]
    return fpre, fpost


def find1point(t, y0, sifpre, parvals0, idx_dt, dt,
               fpre_fpost_y_4F=fpre_fpost_y_4F):
    parvals1 = deepcopy(parvals0)
    parvals1[idx_dt] = dt
    y, ISTATE = solve_py.lsoda_calc(y0, parvals1, t)
    if ISTATE > 0:  # no errors
        return fpre_fpost_y_4F(y, sifpre)
    else:
        return np.nan, np.nan


def find_STDP(t, y0, sifpre, parvals0, idx_dt, dts,
              parallel=True,
              fpre_fpost_y_4F=fpre_fpost_y_4F):
    from functools import partial
    result = dict()
    if parallel:
        umap = pmap
        parvals1 = parvals0
    else:
        umap = map
        parvals1 = deepcopy(parvals0)
    X = umap(partial(find1point,
                     t, y0, sifpre, parvals1, idx_dt,
                     fpre_fpost_y_4F=fpre_fpost_y_4F),
             dts)
    X = map(lambda *a: list(a), *X)
    result['fpre'], result['fpost'] = np.array(X[0]), np.array(X[1])
    result['ftot'] = result['fpost'] * result['fpre']
    return result


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