Prof. Dr. rer. nat. Martin Nawrot
Martin Nawrot studied physics, political sciences and history at the University of Freiburg and the University of Kent at Canterbury, UK. During his PhD he obtained experimental and theoretical expertise in basic neuroscience research and received his degree from the University of Freiburg in 2003. After two postdoc positions at the Heidelberg Academy of Sciences and Humanities and the Freie Universität Berlin he became a junior research group leader at the Bernstein Center for Computational Neuroscience Berlin in 2007. From 2008 to 2015 he was Junior Professor for Theoretical Neuroscience at the Freie Universität Berlin. In 2015 he was appointed as Full Professor for Animal Physiology at the Institute for Zoology at the University of Cologne.
In an interdisciplinary team we combine theoretical and experimental approaches to investigate information processing in nervous systems of different animal models. Our goal is to formulate valid theories and testable model hypotheses.
1. Neural Coding: Reliable and efficient information processing in the noisy brain
The nervous systems of animals employ highly efficient strategies to process sensory information, to form behavioral decisions, and to control their motor actions. Insects have limited neuronal resources and are thus particularly interesting to study fundamentals of efficient neural information processing. Specifically we study adaptive and sparse coding in the olfactory pathway of insects using experimental approaches and functional neural network models. In mammals we study the mechanisms and function of cortical variability during sensory and motor representations.
2. Learning and Memory: Behavioral and neural plasticity in insects
Plasticity in the nervous system is of vital importance for all animals. Insects show a wide repertoire of behavior and fundamental learning abilities. We are interested in uncovering the mechanisms that underlie learning, memory formation and decision-making. With our collaborators we design behavioral and electrophysiological experiments in insects. Based on our experimental analyses we parameterize mathematical and computational neuronal network models that mimic nervous system function and behavior of an individual animal to control autonomous robots.
3. Cortical Motor Control: Planning and execution of voluntary movements in primates
The mammalian neocortex performs high-level control of voluntary movements. We are specifically interested in the coordination of motor cortical with proprioceptive sensory areas. While our movements are precise and accurate, cortical activity shows a high variability across movement repetitions. We study the sources, temporal dynamics and functional role of cortical variability. In our approach we analyze in vivo recordings from monkeys and humans during preparation and execution of movements and employ large scale simulations of cortical attractor networks.
4. Neuromorphic Computing: brain like computation with in silico neural networks
We make use of neuromorphic hardware—electronic versions of neurons and synapses on a microchip—to implement spiking neural networks inspired by the sensory processing architecture of the nervous system of insects. Specifically we are interested in using neuromorphic computing for the control of autonomous robots and for the real-time decoding of spiking neurons in the living brain.
A neuromorphic network for generic multivariate data classification.
Schmuker M, Pfeil T, Nawrot MP. Proc Natl Acad Sci USA 2014; 111:2081-6
Rapid learning dynamics in individual honeybees during classical conditioning.
Pamir E, Szyszka P, Scheiner R, Nawrot MP Front Behav Neurosci 2014; 8:313
Cellular Adaptation Facilitates Sparse and Reliable Coding in Sensory Pathways.
Farkhooi F, Froese A, Müller E, Menzel R, Nawrot MP PLoS Comp Biol 2013; 9:e1003251
Local interneurons and projection neurons in the antennal lobe from a spiking point of view.
Meyer A, Galizia G, Nawrot MP. J Neurophys 2013; 110:2465-2474
Natural image sequences constrain dynamic receptive fields and imply a sparse code.
Häusler C, Susemihl A, Nawrot MP. Brain Research 2013; 1536:53-67
Encoding of odor-reward associations in mushroom body output neurons
Strube-Bloss M*, Nawrot MP*, Menzel R. J Neurosci 2011; 31:3129-40.
Adaptation reduces variability of the neural population code.
Farkhooi F, Müller E, Nawrot MP. Physical Review E 2011; 83:050905
Beyond the cortical column: abundance and physiology of horizontal connections imply a strong role for inputs from the surround.
Boucsein C*, Nawrot MP*, Schnepel P, Aertsen A. Frontiers in Neuroscience 2011; 5:32
Dynamic encoding of movement direction in motor cortical neurons.
Rickert J, Riehle A, Aertsen A, Rotter S, Nawrot MP. J Neurosci 2009; 29:13870-13882
Rapid odor processing in the honeybee antennal lobe network.
Krofczik S, Menzel R, Nawrot MP. Frontiers in Computational Neuroscience 2009; 2:9