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!
New preprint: "Adaptive coding for dynamic sensory inference" is available on biorXiv
We will be presenting two posters at Cosyne:
"Adaptive Compression of Statistically Homogenous Sensory
Signals" with Josh McDermott
"Statistical Inference under Resource Constraints in
Dynamic Environments" with Ann Hermundstad
New preprint: "Learning mid-level auditory codes from natural sound statistics" is available on arXiv
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.
Preprints and Manuscripts under Review:
Mlynarski W.*, Hermundstad A.M.*, "Adaptive coding for dynamic sensory inference", bioRXiv
Mlynarski W., McDermott J.H., "Learning mid-level auditory codes from natural sound statistics", Neural Computation, 2017 (in press - arXiv prerpint)
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., "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