I am a postdoctoral researcher working in the Computational Audition Lab at Brain and Cognitive Sciences Department at MIT. I am also a member of the Center for Brains Minds and Machines. Thanks for visiting my webpage!
I'll be presenting at the "Predictive Coding Workshop" during the BCCN 2018 in Berlin on 25th Sept 2018
Our paper: "Adaptive coding for dynamic sensory inference" is now out in eLife
We have two posters at Cosyne 2018 in Denver:
"Co-occurrence statistics of natural sound features
predict perceptual grouping" with Josh McDermott
"Sensory codes for optimizing tradeoffs between task
performance, adaptation speed and resource use"
with Ann Hermundstad
Our paper: "Learning mid-level auditory codes from natural sound statistics" is out in Neural Computation
I'm organizing the Natural Scene Statistics and Sensory Representations workshop at the Bernstein Conference in Berlin on 21st Sept 2016
Education and working experience:
2015 - to date, Postdoctoral Associate, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, USA
2011 - 2015, PhD in Computer Science, Max-Planck Institute for Mathematics in the Sciences, Leipzig, Germany
2009 - 2011, Research Assistant in Bioinformatics, Department of Molecular Neuropharmacology, Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
2005 - 2010 MSc in Computer Science, Jagiellonian University, Kraków, Poland
An important property separating living systems from inorganic matter is the ability to build and maintain internal models of the world. In order to achieve that, organisms extract regularities present in environments in which they evolved and developed.
The brain seems to be a prominent example of a system employing such a strategy. It has been demonstrated that numerous properties of perception and sensation can be explained as an adaptation to the natural environment.
In my work I follow these lines of thought. In particular, I study the auditory system through the lens of stimulus statistics by constructing statistical models of natural sounds and perceptual mechanisms. I hope that this approach will bring us towards identifying general principles which govern information processing in biological systems.
Mlynarski W., McDermott J.H., "Learning mid-level auditory codes from natural sound statistics", Neural Computation, 2018 (link)
Mlynarski W. "The opponent channel population code of sound location is an efficient representation of natural stereo sounds", PLOS Computational Biology, 2015 (link)
Mlynarski W., Jost J. "Statistics of natural binaural sounds", PLOS One, 2014 (link)
Mlynarski W, "Efficient coding of spectrotemporal binaural sounds leads to emergence of the auditory space representation", Frontiers in Computational Neuroscience, 2014 (link)
Mlynarski W., Freigang C., Bennemann J., Stoehr M. and Ruebsamen R.. "Position of acoustic stimulus modulates visual alpha activity", NeuroReport, 2014 (link)
Korostynski M., Piechota M., Dzbek J., Mlynarski W., Szklarczyk K., Ziolkowska B. and Przewlocki R. "Novel drug-regulated transcriptional networks in brain reveal pharmacological properties of psychotropic drugs", BMC Genomics, 2013 (link)
Conference Papers and Technical Reports:
Mlynarski W., McDermott J. H. "Natural sound statistics predict auditory grouping principles", CCN 2018, Philadelphia, USA, Proceedings
Mlynarski W., "Sparse, complex-valued representations of natural sounds learned with phase and amplitude continuity priors", arXiv:1312.4695 [cs.LG]
Mlynarski W., "Learning binaural spectrogram features for azimuthal speaker localization", Interspeech 2013, Lyon, France
Email: mlynar (at) mit (dot) edu
wiktor.mlynarski (at) gmail (dot) com
Office Phone: 617 324 7270
Mailing Address: 77 Massachusetts Avenue, 46-4078, Cambridge, MA 02139
Physical Address: 43 Vassar Street, Office 46-4078