BSDT multi-valued coding in discrete spaces

Abstract

Recent binary signal detection theory (BSDT) employs a 'replacing' binary noise (RBN). In this paper it has been demonstrated that RBN generates some related N-dimensional discrete vector spaces, transforming to each other under different network synchrony conditions and serving 2-, 3-, and 4-valued neurons. These transformations explain optimal BSDT coding/decoding rules and provide a common mathematical framework, for some competing types of signal coding in neurosciences. Results demonstrate insufficiency of almost ubiquitous binary codes and, in complex cases, the need of multi-valued ones. © 2009 Springer-Verlag Berlin Heidelberg.