摘要：AbstractIn this study a nonlinear mathematical model of plasticity in the brain is reduced using the Proper Orthogonal Decomposition and Discrete Empirical Interpolation Method. Such methods are remarkably useful for connecting reduced small scale models via the inputs and outputs to form optimally performing large scale models. Novel results were obtained as mathematical model order reduction has not been applied in neuroscience without linearization of the mathematical model and never to the model presented here. The reduced order model consumes considerably less computational resources than the original while maintaining a low root mean square error between the original and reduced model.
