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Entropy 2017, 19(9), 494; doi:10.3390/e19090494

Quantifying Information Modification in Developing Neural Networks via Partial Information Decomposition

1
MEG Unit, Brain Imaging Center, Goethe University, 60528 Frankfurt, Germany
2
Max Planck Institute for Dynamics and Self-Organization, 37077 Göttingen, Germany
3
Complex Systems Research Group and Centre for Complex Systems, Faculty of Engineering & IT, The University of Sydney, Sydney, NSW 2006, Australia
4
CSIRO Data61, Marsfield, NSW 2122, Australia
5
Bernstein Center for Computational Neuroscience, 37077 Göttingen, Germany
*
Author to whom correspondence should be addressed.
Received: 13 July 2017 / Revised: 12 September 2017 / Accepted: 12 September 2017 / Published: 14 September 2017
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Abstract

Information processing performed by any system can be conceptually decomposed into the transfer, storage and modification of information—an idea dating all the way back to the work of Alan Turing. However, formal information theoretic definitions until very recently were only available for information transfer and storage, not for modification. This has changed with the extension of Shannon information theory via the decomposition of the mutual information between inputs to and the output of a process into unique, shared and synergistic contributions from the inputs, called a partial information decomposition (PID). The synergistic contribution in particular has been identified as the basis for a definition of information modification. We here review the requirements for a functional definition of information modification in neuroscience, and apply a recently proposed measure of information modification to investigate the developmental trajectory of information modification in a culture of neurons vitro, using partial information decomposition. We found that modification rose with maturation, but ultimately collapsed when redundant information among neurons took over. This indicates that this particular developing neural system initially developed intricate processing capabilities, but ultimately displayed information processing that was highly similar across neurons, possibly due to a lack of external inputs. We close by pointing out the enormous promise PID and the analysis of information modification hold for the understanding of neural systems. View Full-Text
Keywords: information theory; partial information decomposition; neural computation; neural development; self-organisation information theory; partial information decomposition; neural computation; neural development; self-organisation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Wibral, M.; Finn, C.; Wollstadt, P.; Lizier, J.T.; Priesemann, V. Quantifying Information Modification in Developing Neural Networks via Partial Information Decomposition. Entropy 2017, 19, 494.

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