Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/25465
Appears in Collections:Psychology Journal Articles
Peer Review Status: Refereed
Title: Partial information decomposition as a unified approach to the specification of neural goal functions
Authors: Wibral, Michael
Priesemann, Viola
Kay, James W
Lizier, Joseph T
Phillips, William
Keywords: Information theory
Unique information
Shared information
Synergy
Redundancy
Predictive coding
Neural coding
Coherent infomax
Neural goal function
Issue Date: Mar-2017
Citation: Wibral 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.
Abstract: In 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.
DOI Link: http://dx.doi.org/10.1016/j.bandc.2015.09.004
Rights: 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Files in This Item:
File Description SizeFormat 
1-s2.0-S027826261530021X-main.pdf693.07 kBAdobe PDFView/Open



This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.