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Knowledge and understanding: Students will learn the fundamentals of the general modelling approach such as the between data model and system model, the importance of measurements, the identification of model parameters in the absence and presence of noise, deconvolution techniques for estimating the model input from the output and its impulse and model validation techniques, modelling for the artificial pancreas. Moreover, they will learn the basics of different approaches to neural modelling, ranging from circuit models of the neuronal membrane to black box approaches used to solve the neural encoding/decoding problem, and the basics of neural networks. Application of knowledge and understanding: At the end of the course, students will be able to represent a set of measurements and study their statistical properties; identify the parameters of a data model in the absence and in the presence of noise; solve a discrete deconvolution problem in the absence and in the presence of noise; use a standardised model to simulate the glucose/insulin cycle in humans and its alterations (diabetes). They will also become familiar with the basics of tools used to build computational models of neural activity from experimental data. Critical and judgement skills: Students will learn how to select the most appropriate modelling approach for a given problem and to evaluate the complexity of the proposed solution. Communication skills. Students will learn to communicate their modelling choices in a multidisciplinary context and to communicate possible design choices for this purpose. Learning skills. Students will develop a mindset geared towards independent learning of advanced concepts not covered in the course.