Neural Networks are among the hottest topics in machine learning and their applications in a wide range of fields have contributed to major scientific advances. MATLAB’s Neural Networks Toolbox offers a rich selection of methods that allow for implementing a variety of network architectures and concepts. While we will review the functionality of the toolbox, we also want to understand basic concepts underlying the implementation of neural networks. For this session, we will implement one of the early neural network models – the Hopfield network (Hopfield; 1984) – from scratch. Until today, the model is widely used for memory modeling in Neurosciences. We will take associative memory modeling as an example for our implementation and discuss applications in biomedical research (Weber, Maia, Kutz; 2016).