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Poster
Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources
Bariscan Bozkurt · Cengiz Pehlevan · Alper Erdogan

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #305
in Poster Session 6 »

Extraction of latent sources of complex stimuli is critical for making sense of the world. While the brain solves this blind source separation (BSS) problem continuously, its algorithms remain unknown. Previous work on biologically-plausible BSS algorithms assumed that observed signals are linear mixtures of statistically independent or uncorrelated sources, limiting the domain of applicability of these algorithms. To overcome this limitation, we propose novel biologically-plausible neural networks for the blind separation of potentially dependent/correlated sources. Differing from previous work, we assume some general geometric, not statistical, conditions on the source vectors allowing separation of potentially dependent/correlated sources. Concretely, we assume that the source vectors are sufficiently scattered in their domains which can be described by certain polytopes. Then, we consider recovery of these sources by the Det-Max criterion, which maximizes the determinant of the output correlation matrix to enforce a similar spread for the source estimates. Starting from this normative principle, and using a weighted similarity matching approach that enables arbitrary linear transformations adaptable by local learning rules, we derive two-layer biologically-plausible neural network algorithms that can separate mixtures into sources coming from a variety of source domains. We demonstrate that our algorithms outperform other biologically-plausible BSS algorithms on correlated source separation problems.

Keywords: blind source separation · Weighted Similarity Matching · Biologically Plausible Neural Networks · Determinant Maximization
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Bariscan Bozkurt (Koc University)

# Education * M.Sc. Student, Electrical-Electronics Engineering, Koc University (2021-) * B.Sc. Electrical-Electronics Engineering, Koc University (2018-2021) * B.A. Mathematics, Koc University (2015-2021) # Research * Koc University Advanced Signal Processing and Communication Group (February 2021-) * Koc University-Is Bank Artificial Intelligence Center (September 2021-) # Business Experience * Machine Learning Engineer (Online), Hospital on Mobile, Silicon Valley (March 2021-September 2021) * Machine Learning Engineering Intern, P.I. Works Inc./Applied Research, Istanbul/TURKEY (July 2020 - November 2020) * VHDL Design - Summer Internship, ASELSAN A.Ş., Ankara/TURKEY (July 2020 - August 2020)

Cengiz Pehlevan (Harvard University)
Alper Erdogan (Koç University)
Alper Erdogan

Alper T. Erdogan (Senior Member, IEEE) was born in Ankara, Turkey, in 1971. He received the B.S. degree from the Middle East Technical University, Ankara, Turkey, in 1993, and the M.S. and Ph.D. degrees from Stanford University, Stanford, CA, USA, in 1995 and 1999, respectively. He was a Principal Research Engineer with the Globespan-Virata Corporation (formerly Excess Bandwidth and Virata Corporations) from September 1999 to November 2001. He joined the Electrical and Electronics Engineering Department, Koc University, Istanbul, Turkey, in January 2002, where he is currently a Professor. His research interests include adaptive signal processing, machine learning, physical layer communications, computational neuroscience, optimization, system theory and control, and information theory. Dr. Erdogan was the recipient of several awards including TUBITAK Career Award (2005), Werner Von Siemens Excellence Award (2007), TUBA GEBIP Outstanding Young Scientist Award (2008), TUBITAK Encouragement Award (2010), and Outstanding Teaching Award (2017). He was an Associate Editor for the IEEE Transactions on Signal Processing, and he was a member of IEEE Signal Processing Theory and Methods Technical Committee.

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