Timezone: »
Spotlight
Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication
Guy D Isely · Christopher J Hillar · Fritz Sommer
A new algorithm is proposed for a) unsupervised learning of sparse representations from subsampled measurements and b) estimating the parameters required for linearly reconstructing signals from the sparse codes. We verify that the new algorithm performs efficient data compression on par with the recent method of compressive sampling. Further, we demonstrate that the algorithm performs robustly when stacked in several stages or when applied in undercomplete or overcomplete situations. The new algorithm can explain how neural populations in the brain that receive subsampled input through fiber bottlenecks are able to form coherent response properties.
Author Information
Guy D Isely (UC Berkeley)
Christopher J Hillar (Redwood Center for Theoretical Neuroscience)
Fritz Sommer (UC Berkeley)
Related Events (a corresponding poster, oral, or spotlight)
-
2010 Poster: Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication »
Wed. Dec 8th 08:00 -- 08:00 AM Room
More from the Same Authors
-
2022 : Neuromorphic Visual Scene Understanding with Resonator Networks (in brief) »
Alpha Renner · Giacomo Indiveri · Lazar Supic · Andreea Danielescu · Bruno Olshausen · Fritz Sommer · Yulia Sandamirskaya · Edward Frady -
2022 : Learning the Feedback Connections from V1 to LGN via Information Maximization »
Reza Eghbali · Fritz Sommer · Murray Sherman -
2014 Poster: Information-based learning by agents in unbounded state spaces »
Shariq A Mobin · James A Arnemann · Fritz Sommer -
2013 Workshop: High-dimensional Statistical Inference in the Brain »
Alyson Fletcher · Dmitri B Chklovskii · Fritz Sommer · Ian H Stevenson