Hybrid preference machines based on inspiration from neuroscience

Stefan Wermter, Christo Panchev

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


In the past, a variety of computational problems have been tackled with different connectionist network approaches. However, very little research has been done on a framework which connects neuroscience-inspired models with connectionist models and higher level symbolic processing. In this paper, we outline a preference machine framework which focuses on a hybrid integration of various neural and symbolic techniques in order to address how we may process higher level concepts based on concepts from neuroscience. It is a first hybrid framework which allows a link between spiking neural networks, connectionist preference machines and symbolic finite state machines. Furthermore, we present an example experiment on interpreting a neuroscience-inspired network by using preferences which may be connected to connectionist or symbolic interpretations.
Original languageEnglish
Pages (from-to)255-270
Number of pages16
JournalCognitive Science Research
Issue number2
Early online date31 May 2002
Publication statusPublished - Jun 2002
Externally publishedYes


  • Hybrid systems
  • Neural preferences
  • Preference machines
  • Neural networks of spiking neurons


Dive into the research topics of 'Hybrid preference machines based on inspiration from neuroscience'. Together they form a unique fingerprint.

Cite this