Session 1 “Synapses, neurons and networks” will introduce the motivation behind and basic concepts of theoretical and computational modelling of the neurons, neural networks and brain functions. 

Session 2 “Networks: Functions and Dynamics” will focus on complex dynamics, both at single neuron and population level. The goal of this session is to learn about theoretical methods to study the origin of the dynamical state of biological neural networks, and then to discuss how these insights help in mechanistic understanding of essential brain functions such as working memory. The tools to numerically simulate the activity of biological neural networks will also be discussed.

Session 3 “Learning and Plasticity” will introduce synaptic plasticity, the biological basis of learning and memory.  At the most fundamental level, perception, cognition, learning and memory ultimately depend on complex molecular processes. Modelling of these processes requires simulating phenomena that occur on a wide range of spatial and temporal scales. The goal of this session is to inform and teach students about the modelling of synaptic plasticity and learning. Also, data mining approaches relevant for better understanding of synapses will be exemplified. Some EBRAINS resources providing modeling of synaptic plasticity will be used in the tutorials.

Session 4 “Brain function and dysfunction: models and experiments” will be devoted to the computational perspective on brain disease. It is becoming increasingly clear that the classical approach of relating behavioral deficits to the morphology, genetics or chemistry of the brain is highly simplified. The genetic, chemical, morphological changes manifest in behavioural changes via multi-scale interactions between neurons and networks. Therefore, a number of diseases can be understood as diseases of brain dynamics. In addition, psychiatric diseases are better understood in the computational framework of decision making and reinforcement learning. In this session we will provide a pedagogical lecture on how our understanding of brain dynamics provides a bottom-up view of brain dysfunction such as Parkinson’s diseases, while the decision making, and reinforcement learning provides a top-down framework for understanding psychiatric disorders. 

Session 5 “Artificial systems and embodied brains” will discuss technical applications of neuronal computation. We introduce concepts of neurocognitive systems, give an overview of available neurocomputing hardware and software environments, and introduce closed-loop neuronal control systems. The second part of the day is dedicated to hands-on sessions that allow students (remote) access to simulated neurorobotics and neurocomputing environments. We will prepare a virtual environment, where participating teams can develop neuronal models to train a virtual robot in a competitive task (e.g. collecting items in an arena full of obstacles). We will organize a final competition.