The course Modelling Human Brain Processes is designed to explore the organisation of synaptic connectivity as the foundation of neural computation and learning. Each central nervous system neuron has the processing power of a small computer, receiving input from thousands of other neurons and then deciding which information to pass on. Neurons in neural networks communicate with other neurons to allow us to see, think, remember, smell, and express ourselves. Every action, from simple to complex, involves thousands of neurons in various neural networks. Computational neuroscience enables scientists to gain insight into the brain's functionality. Throughout the course, we will present and discuss these laws, which could enable us to repair damaged brains, create new artificial types of intelligence, and ultimately provide a fascinating window into how neuronal networks function to produce conscious thinking. The course will begin with a brief overview of basic knowledge about the electrophysiological functioning of neurons and will progress to a more detailed overview of various neural network models. Perceptrons, biological and artificial associative networks, and dynamical theories of recurrent networks, including amplifiers, attractors, hybrid computation, back-propagation rule, and Hebbian learning, will be introduced along with models of perception, motor control, memory, and neural development. Computational neuroscience combines theoretical physics, advanced mathematics, and cutting-edge computer technology to create powerful models of working neural networks that help scientists understand how the brain accomplishes feats like memory storage, sensory information processing, and conscious awareness, which may lead to novel treatments for a variety of neurological and psychiatric disorders. Other research has focused on cognitive behaviour, explaining why and how neural networks can make us prefer certain choices over others, and how this can be applied to areas as diverse as economic theory or addiction psychology. The course requires a basic understanding of cell biology, neuroscience, and systems theory. After successfully completing the course, the student will understand which components of a modelled neuron correspond to physiological parameters such as membrane potential and firing rate, as well as which learning principles can be used to change synaptic strength between neurons, i.e., how this plasticity leads to physiological modification of neural network properties. The student will be able to address which cognitive tasks, such as pattern recognition, classification of inputs into different categories, and reward-dependent learning processes, can be performed by elementary networks, as well as discuss the brain's ability to represent information in different modes, associated with either conscious or non-conscious processing, and to make decisions.