Original Research ARTICLE
Local active information storage as a tool to understand distributed neural information processing
- 1MEG Unit, Brain Imaging Center, Goethe University, Frankfurt am Main, Germany
- 2CSIRO Computational Informatics, Marsfield, NSW, Australia
- 3Fakultät für Biologie, Technische Universtät, Darmstadt, Germany
- 4Department of Nonlinear Dynamics, Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
Every act of information processing can in principle be decomposed into the component operations of information storage, transfer, and modification. Yet, while this is easily done for today's digital computers, the application of these concepts to neural information processing was hampered by the lack of proper mathematical definitions of these operations on information. Recently, definitions were given for the dynamics of these information processing operations on a local scale in space and time in a distributed system, and the specific concept of local active information storage was successfully applied to the analysis and optimization of artificial neural systems. However, no attempt to measure the space-time dynamics of local active information storage in neural data has been made to date. Here we measure local active information storage on a local scale in time and space in voltage sensitive dye imaging data from area 18 of the cat. We show that storage reflects neural properties such as stimulus preferences and surprise upon unexpected stimulus change, and in area 18 reflects the abstract concept of an ongoing stimulus despite the locally random nature of this stimulus. We suggest that LAIS will be a useful quantity to test theories of cortical function, such as predictive coding.
Keywords: visual system, neural dynamics, predictive coding, local information dynamics, voltage sensitive dye imaging, distributed computation, complex systems, information storage
Citation: Wibral M, Lizier JT, Vögler S, Priesemann V and Galuske R (2014) Local active information storage as a tool to understand distributed neural information processing. Front. Neuroinform. 8:1. doi: 10.3389/fninf.2014.00001
Received: 09 November 2013; Paper pending published: 02 December 2013;
Accepted: 09 January 2014; Published online: 28 January 2014.
Edited by:Daniele Marinazzo, University of Gent, Belgium
Reviewed by:Demian Battaglia, Max Planck Institute for Dynamics and Self-Organization, Germany
Luca Faes, University of Trento, Italy
Copyright © 2014 Wibral, Lizier, Vögler, Priesemann and Galuske. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Michael Wibral, MEG Unit, Brain Imaging Center, Goethe University, Heinrich-Hoffmann Strasse 10, Frankfurt am Main, D-602528, Germany e-mail: email@example.com