The biological function of self-organized criticality is much less understood than the physical mechanisms behind this phenomenon. Critical dynamics seems beneficial to living beings and it is known to bring about optimal computational capabilities, optimal transmission and storage of information, and sensitivity to sensory stimuli. We are interested in the developmental aspects of motor behavior in animals which we study in biomorphic autonomous robots.

This project divided into two subgroups: 'Aging effects in selective attention' and 'Dynamic adaptation in decoding temporal information' that both develop computational models to explain effects in cognitive psychology in a more detailed way than usual psychological theories can. These models base on knowledge about biological processes and insights via abstract modeling in computational neuroscience yielding quantitative model predictions that can directly be tested in psychological experiments.

Neural networks display characteristics of critical dynamics in the neural activities as theoretically predicted. The power-law statistics for the size of avalanches of neural activity was confirmed in real neurons, where the critical behavior is re-approached even after a substantial perturbation of the parameters of the system. These findings provide evidence for the presence of self-organized criticality (SOC). We study neural network models that exhibit power-law statistics with realistic synaptic mechanisms, such as synaptic depression.