Developmental and cognitive neuroscience
Computational models of brain function play an important role in helping us understand the brain and behaviour. Working with psychologists and biologists, we explore brain function through computational neuroscience models.
- Activity-dependent synaptic and neuronal plasticity
- Bottom-up and top-down processing
- Cortical versus subcortical brain pathways
- Neuronal structure and self-organization (structure, dynamics, and function)
Computational intelligence and machine learning
Computational intelligence embodies a wide range of techniques, from those that are inspired from physical, chemical, social and biological systems. In this theme we investigate in computational environments natural intelligent systems at different levels, ranging from populations and societies, to nervous systems and brains, and to genes, proteins and metabolites.
- Meta-modeling in evolution in dynamic environments, multi-objective optimization
- Multi-objective evolutionary learning
- Semi-supervised learning, graph-based learning, and activelearning
Systems biology and morphogenetic engineering
Natural techniques demonstrate how a variety of biological systems can organise and classify high dimensional and high volume data. By looking at a wider variety of natural systems, we can understand different patterns of intelligence, while benefiting from their application to difficult real-world problems.
- Organising principles in neural evolution (e.g. energy constraint)
- In silico modeling, analysis and synthesis of genetic circuits
- Morphogenetic self-organization of collective systems (e.g., mobile sensors, computing resources), structural design