Traditional approaches to sensory coding use code component-oriented objective functions (COCOFs) to evaluate code quality. Previous COCOFs do not take into account the information-theoretic complexity of the code-generating mapping itself. We do: “Low-complexity coding and decoding” (Lococode) generates so-called lococodes that (1) convey information about the input data, (2) can be computed from the data by a low-complexity mapping (LCM), and (3) can be decoded by a lcm. We implement lococode by training autoassociators with Flat Minimum Search (FMS), a general method for finding low-complexity neural nets. lococode extracts optimal codes for difficult versions of the "bars" benchmark problem. As a preprocessor for a vowel recognition benchmark problem it sets the stage for excellent classification performance.
|Original language||English (US)|
|Title of host publication||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Number of pages||6|
|State||Published - Jan 1 1997|