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dc.contributor.authorWibral, Michaelen_UK
dc.contributor.authorPriesemann, Violaen_UK
dc.contributor.authorKay, James Wen_UK
dc.contributor.authorLizier, Joseph Ten_UK
dc.contributor.authorPhillips, Williamen_UK
dc.description.abstractIn many neural systems anatomical motifs are present repeatedly, but despite their structural similarity they can serve very different tasks. A prime example for such a motif is the canonical microcircuit of six-layered neo-cortex, which is repeated across cortical areas, and is involved in a number of different tasks (e.g. sensory, cognitive, or motor tasks). This observation has spawned interest in finding a common underlying principle, a 'goal function', of information processing implemented in this structure. By definition such a goal function, if universal, cannot be cast in processing-domain specific language (e.g. 'edge filtering', 'working memory'). Thus, to formulate such a principle, we have to use a domain-independent framework. Information theory offers such a framework. However, while the classical framework of information theory focuses on the relation between one input and one output (Shannon's mutual information), we argue that neural information processing crucially depends on the combination of multiple inputs to create the output of a processor. To account for this, we use a very recent extension of Shannon Information theory, called partial information decomposition (PID). PID allows to quantify the information that several inputs provide individually (unique information), redundantly (shared information) or only jointly (synergistic information) about the output. First, we review the framework of PID. Then we apply it to reevaluate and analyze several earlier proposals of information theoretic neural goal functions (predictive coding, infomax and coherent infomax, efficient coding). We find that PID allows to compare these goal functions in a common framework, and also provides a versatile approach to design new goal functions from first principles. Building on this, we design and analyze a novel goal function, called 'coding with synergy', which builds on combining external input and prior knowledge in a synergistic manner. We suggest that this novel goal function may be highly useful in neural information processing. © 2015 The Authors.en_UK
dc.relationWibral M, Priesemann V, Kay JW, Lizier JT & Phillips W (2017) Partial information decomposition as a unified approach to the specification of neural goal functions. Brain and Cognition, 112, pp. 25-38.
dc.rights2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (
dc.subjectInformation theoryen_UK
dc.subjectUnique informationen_UK
dc.subjectShared informationen_UK
dc.subjectPredictive codingen_UK
dc.subjectNeural codingen_UK
dc.subjectCoherent infomaxen_UK
dc.subjectNeural goal functionen_UK
dc.titlePartial information decomposition as a unified approach to the specification of neural goal functionsen_UK
dc.typeJournal Articleen_UK
dc.citation.jtitleBrain and Cognitionen_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.affiliationGoethe University Frankfurten_UK
dc.contributor.affiliationMax Planck Institute for Dynamics and Self-Organizationen_UK
dc.contributor.affiliationUniversity of Glasgowen_UK
dc.contributor.affiliationUniversity of Sydneyen_UK
Appears in Collections:Psychology Journal Articles

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