RESUMO
Ionotropic NMDA and AMPA glutamate receptors (iGluRs) play important roles in synaptic function under physiological and pathological conditions. iGluRs sub-synaptic localization and subunit composition are dynamically regulated by activity-dependent insertion and internalization. However, understanding the impact on synaptic transmission of changes in composition and localization of iGluRs is difficult to address experimentally. To address this question, we developed a detailed computational model of glutamatergic synapses, including spine and dendritic compartments, elementary models of subtypes of NMDA and AMPA receptors, glial glutamate transporters, intracellular calcium and a calcium-dependent signaling cascade underlying the development of long-term potentiation (LTP). These synapses were distributed on a neuron model and numerical simulations were performed to assess the impact of changes in composition and localization (synaptic vs extrasynaptic) of iGluRs on synaptic transmission and plasticity following various patterns of presynaptic stimulation. In addition, the effects of various pharmacological compounds targeting NMDARs or AMPARs were determined. Our results showed that changes in NMDAR localization have a greater impact on synaptic plasticity than changes in AMPARs. Moreover, the results suggest that modulators of AMPA and NMDA receptors have differential effects on restoring synaptic plasticity under different experimental situations mimicking various human diseases.
RESUMO
Ionotropic NMDA and AMPA glutamate receptors (iGluRs) play important roles in synaptic function under physiological and pathological conditions. iGluRs sub-synaptic localization and subunit composition are dynamically regulated by activity-dependent insertion and internalization. However, understanding the impact on synaptic transmission of changes in composition and localization of iGluRs is difficult to address experimentally. To address this question, we developed a detailed computational model of glutamatergic synapses, including spine and dendritic compartments, elementary models of subtypes of NMDA and AMPA receptors, glial glutamate transporters, intracellular calcium and a calcium-dependent signaling cascade underlying the development of long-term potentiation (LTP). These synapses were distributed on a neuron model and numerical simulations were performed to assess the impact of changes in composition and localization (synaptic vs extrasynaptic) of iGluRs on synaptic transmission and plasticity following various patterns of presynaptic stimulation. In addition, the effects of various pharmacological compounds targeting NMDARs or AMPARs were determined. Our results showed that changes in NMDAR localization have a greater impact on synaptic plasticity than changes in AMPARs. Moreover, the results suggest that modulators of AMPA and NMDA receptors have differential effects on restoring synaptic plasticity under different experimental situations mimicking various human diseases.
RESUMO
BACKGROUND: The resolution of a model describing the electrical activity of neural tissue and its propagation within this tissue is highly consuming in term of computing time and requires strong computing power to achieve good results. NEW METHOD: In this study, we present a method to solve a model describing the electrical propagation in neuronal tissue, using parareal algorithm, coupling with parallelization space using CUDA in graphical processing unit (GPU). RESULTS: We applied the method of resolution to different dimensions of the geometry of our model (1-D, 2-D and 3-D). The GPU results are compared with simulations from a multi-core processor cluster, using message-passing interface (MPI), where the spatial scale was parallelized in order to reach a comparable calculation time than that of the presented method using GPU. A gain of a factor 100 in term of computational time between sequential results and those obtained using the GPU has been obtained, in the case of 3-D geometry. Given the structure of the GPU, this factor increases according to the fineness of the geometry used in the computation. COMPARISON WITH EXISTING METHOD(S): To the best of our knowledge, it is the first time such a method is used, even in the case of neuroscience. CONCLUSION: Parallelization time coupled with GPU parallelization space allows for drastically reducing computational time with a fine resolution of the model describing the propagation of the electrical signal in a neuronal tissue.
