Max Planck Institute for Biological Cybernetics
Investigation of information processing in the brain.
We use experimental and theoretical methods as well as computer simulations to investigate the processes that make us perceive, decide, act, and learn. Many of our scientific findings have laid essential foundations in AI research and will continue to shape this discipline in the future. In addition to the three research departments Computational Neuroscience (Director: Peter Dayan), High Field Magnetic Resonance (Max Planck Fellow: Klaus Scheffler) and Sensory & Sensorimotor Systems (Max Planck Fellow: Li Zhaoping), there are several independent research groups.
Director: Dr. Peter Dayan
One of the research focuses of this department is the question of how the brain makes decisions. To this end, it creates various forms of learning with theoretical models, including so-called reinforcement learning. Based on statistical and programming methods, the scientists can simulate learning and decision-making processes on the computer. They are also analyzing how impaired decision-making can lead to disorders such as depression, addiction as well as anxiety, obsessive-compulsive and personality disorders. In this way, they want to combine the psychological and neuronal view of such disorders and thereby learn more about the causes, classification and possible treatment of these diseases.
Lead: Prof. Dr. Zhaoping Li
The Department of Sensory and Sensorimotor Systems, we also call it the Natural Intelligence Lab, became part of the Max Planck Institute for Biological Cybernetics in October 2018. It is led by Prof. Dr. Zhaoping Li. Her research aims to identify and understand how the brain receives and processes sensory stimuli (visual, auditory, tactile, olfactory), and how it subsequently uses this information to control body movements and make cognitive decisions.
Lead: Prof. Dr. Klaus Scheffler
Our goal is to develop and apply novel magnetic resonance techniques at very high field strengths to study the anatomical and functional microstructure of the brain. We want to understand how physiological processes and microstructures affect the measured nuclear magnetic resonance signal and how these magnetic fingerprints can be used to measure thought processes and brain activation. Magnetic resonance imaging is currently the only method that can capture thought processes of the entire human brain with a spatial resolution of 1 millimeter and a temporal resolution of 1 second using ultra-high magnetic fields.
Research Group Leader: Dr. Assaf Breska
Our research combines the domains of time-related knowledge processing, learning, attention, and predictive action with the goal of developing a neurocognitive model of dynamic knowledge processing. We are interested in how perception, cognition, and action are interrelated within a temporal coordinate system and how anticipatory information processing in the brain incorporates the time aspect along with other dimensions.
Research Group Leaders: Jennifer Li, PhD, Drew Robson, PhD
The joint lab of Drew Robson and Jennifer Li is developing new imaging techniques to map the neural activity of free-swimming zebrafish at the larval stage. The goal is to gain a better understanding of the mechanisms responsible for motivation and attention in the brain. The research group investigates the state-dependent regulation of decision-making processes in the brain during natural behaviors such as navigation, foraging, and operant learning. The interplay of neuromodulatory systems and brain states is of particular interest.
Research Group Leader: Dr. Manuel Spitschan
The Max Planck Research Group Translational Sensory and Circadian Neuroscience aims to understand the effects of light on human physiology and behaviour. Light influences our physiology and behaviour. For example, exposure to light at night can suppress the production of the hormone melatonin and shift our endogenous circadian rhythm. Light being a major influence on the human circadian system – and specifically its associated negative effects in the evening and a night – the modification of light exposure is an interesting target for interventions.
Research Group Leader: Dr. Eric Schulz
We are an interdisciplinary research group that develops computational models of human intelligence. Our goal is to build formal theories of how people generalize from little data, explore efficiently, and find approximate solutions to complex problems. We build precise and powerful models of people's cognitive abilities, combining psychology, machine learning, and neuroscience. We use interactive games, large data sets, online and lab experiments to study how people learn and explore.