Real and Artificial Neural Networks (NEUR30006)

The analysis of real neural networks and the construction of artificial neural networks afford mutually synergistic technologies with broad application within and beyond neuroscience. Artificial neural networks, and other machine learning methods, have found numerous applications in analysis and modelling, and have produced insights into numerous complex phenomena (and generated huge economic value). Such technologies can also be used to gain insights into the biological systems that inspired their creation: we will explore how learning is instantiated in artificial and biological neural networks.

The subject aims to provide foundation skills for those who may wish to peruse neuroscience - or any research or work environment that involves the creation or capture, and analysis, of complex data. Students will gain experience with digital signals and digital signal processing (whether those signals are related to images, molecular data, connectomes, or electrophysiological recordings), and will learn how to conceptualise and implement approaches to modelling data by constructing an artificial neural network using the Python programming language.