In-Silico Model of NMDA and Non-NMDA Receptor Activities Using Analog Very-Large-Scale Integrated Circuits
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Computational modeling is a useful analytical tool that (with valid data) may help to explain how complex brain systems work. In respiratory control, various neural network models have been proposed to explore the possible mechanisms of respiratory rhythmogenesis based on intrinsic neuronal properties as well as interactions at the network level1,2. However, the large parameter space that must be tested for such neuromorphic models makes it rather cumbersome and time consuming to simulate even a modest size network with discrete respiratory neurons. For large-scale neural network models involving multiple populations of respiratory neurons, such simulations become computationally prohibitive on general-purpose digital computers.
KeywordsVery Large Scale Integration Respiratory Neuron Hebbian Learning Rule AMPAR Channel Very Large Scale Integration Circuit
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