Complex preferences for the integration of neural codes

Christo Panchev, Stefan Wermter

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

1 Citation (Scopus)


This paper presents a complex preference framework of integrating pulsed neural networks into neural/symbolic hybrid approaches. In particular, we introduce an interpretation of neural codes as multidimensional complex neural preferences and preference classes which allow the integration of knowledge from different neural and symbolic models. We define some basic operations on complex preferences and preference classes that allow them to be directly integrated into symbolic models. Furthermore, we show the interpretation of mean firing rate, time-to-first-spike, synchrony and phase codes as complex neural preferences and the interpretation of the operations on preference classes of these codes. The symbolic interpretation and simultaneous processing of mean firing rate and pulse coding schemes in a preferences framework are addressed.
Original languageEnglish
Title of host publicationProceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing
Subtitle of host publicationNew Challenges and Perspectives for the New Millennium
Pages253 - 258
Number of pages6
Publication statusPublished - 2000
Externally publishedYes
EventInternational Joint Conference on Neural Networks (IJCNN) - Como, Italy
Duration: 27 Jul 200027 Jul 2000


ConferenceInternational Joint Conference on Neural Networks (IJCNN)
Abbreviated titleIJCNN 2000


Dive into the research topics of 'Complex preferences for the integration of neural codes'. Together they form a unique fingerprint.

Cite this