Complex preferences for the integration of neural codes

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