skip to content

How do nervous systems of animals process information and how can this knowledge be transferred to systems of artificial intelligence? The research group of Martin Nawrot investigates principles of neural information processing and its transfer to computational brain models and machine learning algorithms. Particular interest is placed on (1) reinforcement learning and synaptic plasticity in biological and artificial systems, and (2) large scale brain simulations to accommodate multi-stability and attractor dynamics supporting models for sensory-motor integration, motor control, and decision making in primates.

Neuromorphic computing paradigm for data classification according to Schmuker, Pfeil & Nawrot, 2014.

Selected publications

  1. Rapp, H., Nawrot, M. P. (2020). A spiking neural program for sensorimotor control during foraging in flying insects. Proceedings of the National Academy of Sciences, 117(45), 28412-28421
  2. Rapp, H., Nawrot, M. P., Stern, M. (2020). Numerical cognition based on precise counting with a single spiking neuron. Iscience, 23(2), 100852
  3. Haenicke J., Yamagata N., Zwaka H., Nawrot M. P., Menzel R. (2018). Neural Correlates of Odor Learning in the Presynaptic Microglomerular Circuitry in the Honeybee Mushroom Body Calyx. eNeuro, 5(3)
  4. Rost T., Deger M., Nawrot, M. P. (2018). Winnerless competition in clustered balanced networks: inhibitory assemblies do the trick. Biological Cybernetics 112, 81-98
  5. Schmuker M., Pfeil T., Nawrot M. P. (2014). A neuromorphic network for generic multivariate data classification. Proceedings of the National Academy of Sciences 111(6), 2081-2086
  6. Farkhooi F., Froese A., Müller E., Menzel R., Nawrot M. P. (2013). Cellular Adaptation Facilitates Sparse and Reliable Coding in Sensory Pathways. PLoS Computational Biology 9: e1003251
  7. Strube-Bloss M., Nawrot M. P., Menzel R. (2011). Encoding of odor-reward associations in mushroom body output neurons. Journal of Neuroscience 31(8):3129-3140