Prof. Dr. Wiktor Młynarski

Keywords:
computational neuroscience, theoretical neuroscience, sensory systems

Research methods:
Our group relies predominanly on concepts from information theory, statistics and probabilistic machine learning - rigorous frameworks to study how efficient computations could be performed by neural systems. These frameworks are normative and substrate-independent - they can be applied to study the brain at different scales - from synapses, through individual neurons to behavior. Our research will be therefore fundamentally collaborative, and we will work closely with diverse experimental and theory-oriented groups.

Brief research description:
An important property separating living systems from inorganic matter is the ability to build and maintain sophisticated internal models of the world. The brain is a prominent example of a system employing such a strategy - it extracts regularities present in the environment to control behavior of the organism. Since the natural world is never static, sensory neurons must dynamically adapt to constant changes over multiple timescales - from evolution to blink of an eye. Our group will study theoretical principles of such adaptative neural computations. We will explore the statistical structure of natural environments, develop optimal processing strategies which might be approximated by biological systems and confront theoretical predictions with reality in close collaborations with experimental groups. We hope that this approach will bring us towards identifying general rules which govern information processing in biological systems.

Selected publications:
1. Mlynarski W* and Hermundstad A* "Adaptive coding for dynamic sensory inference", eLife 2018, https://doi.org/10.7554/eLife.32055

2. Mlynarski W*, Hledik M*, Sokolowski T and Tkacik G "Statistical analysis and optimality of neural systems", Neuron 2021, https://doi.org/10.1016/j.neuron.2021.01.020

3. Mlynarski W* and Hermundstad A* "Efficient and adaptive sensory codes", Nature Neuroscience 2021, https://doi.org/10.1038/s41593-021-00846-0

4. Gupta D*, Mlynarski W*, Sumser A*, Symonova O, Svaton J and Joesch M "Panoramic visual statistics shape retina-wide organization of receptive fields", Nature Neuroscience 2023 (in press), https://doi.org/10.1101/2022.01.11.475815

5. Mlynarski W "The Opponent Channel Population Code of Sound Location Is an Efficient Representation of Natural Binaural Sounds", Plos Computational Biology 2015, https://doi.org/10.1371/journal.pcbi.1004294