Wiktor Młynarski

Wiktor Młynarski

Ludwig Maximilian University of Munich, Germany

Wiktor Młynarski


Wiktor Młynarski is a professor of computational neuroscience at the Ludwig Maximilian University of Munich (LMU). After his undergraduate studies in applied computer science at the Jagiellonian University he pursued a PhD at the Max-Planck Institute for Mathematics in the Sciences. Following a postdoc at the Massachusetts Institute of Technology (MIT) and a fellow position at the Institute of Science and Technology Austria (ISTA), he joined the LMU in June 2023.

In his work Wiktor explores theoretical principles of neural computation, with a particular focus on sensory systems. His group studies how the brain infers properties of the environment, represents sensory stimuli and adapts to the constantly changing circumtstances. To do so, the group relies on tools from information theory, statistics and computer science. Theories at the center of the groups work are substrate-independent and as such can be used to seek connections with different domains of biology to identify shared principles of information processing.

Description of the general focus of the symposium "Computational approaches to understand brain complexity"

Talk: "Probabilistic simplicity in the study of the brain"

The tremendous, almost impenetrable complexity of the nervous system is not just one, but a huge collection of mysteries we are all trying to solve. To develop understanding of neurobiological phenomena we need to seek simplicity. In the talk I will discuss one such way to search for simplicity and understanding - through building normative theories of neural computation. Normative theories attempt to identify goals and principles that may be shared by multiple, seemingly different neural systems. I will specifically focus on sensory systems which need
to achieve a delicate balance between external and internal influences in order to accurately represent relevant information. Dynamic adjustments of the sensory code to these influences have been traditionally categorized depending on their origin and studied separately. Sensory adaptation is a response of a neuron to exogenous changes in stimulus statistics, while internal modulation adjusts
sensory representations to changes in the endogenous states of the brain such as behavioral goals, attention or uncertainty. I will present a theoretical framework which provides a unifying perspective on how sensory codes adapt to such changes regardless of their origin. Starting from the same set of basic principles grounded in information theory and Bayesian inference, our framework generates candidate normative explanations of the diversity of adaptive responses in the early visual system as well as the attentional modulation of neural populations in
the primary visual cortex. I will conclude by presenting an experimental finding of spatio-temporal patterns of neural activity which dominate sensory responses in a brain region that has been thought to be predominantly a sensory relay - the superficial superior colliculus. These findings emphasize the need for new theories which will be required to understand the computational principles of dynamic sensory processing and to further tame the overwhelming complexity of the brain.

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