Simulation framework for electrophysiological networks: Effect of syncytial properties on smooth-muscle synaptic potentials
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A building block-based software framework was developed to simulate electrophysiological networks. The synaptic potentials generated during neurotrans-mission were simulated in an existing discrete bidomain model of smooth muscle, using cubic three-dimensional grids of varying sizes. The model is automatically derived and numerically solved and the results of the simulation agree with previous results obtained analytically. An enhanced model was also proposed, incorporating an additional (junctional) capacitance in the network. The correctness of the model was verified, and the effect of the extra capacitance on the synaptic potentials was explored. It was found that, with a junctional capacitance Ci of 1.4×10−10F incorporated, the peak amplitude of the spontaneous excitatory junction potential Vpeak declined by ∼13% at node 0 and by ∼37% at node 3x for a system size of 93. Similar results were obtained for different system sizes. Vpeak also declined as the junctional capacitance Ci was increased. In a system of size 113, a 200-fold increase in Ci induced a 55% reduction at node 0. It is suggested that the type of modular simulation framework developed here may find general applicability for simulations of other physiological systems.
KeywordsComputational neuroscience Synaptic transmission Smooth muscle Syncytium RC network Simulator
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