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.
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Gopych, P. (2009). BSDT Multi-valued Coding in Discrete Spaces. In: Corchado, E., Zunino, R., Gastaldo, P., Herrero, Á. (eds) Proceedings of the International Workshop on Computational Intelligence in Security for Information Systems CISIS’08. Advances in Soft Computing, vol 53. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88181-0_33
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DOI: https://doi.org/10.1007/978-3-540-88181-0_33
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