Quantifying Information Modification in Developing Neural Networks via Partial Information Decomposition

Michael Wibral,Joseph Lizier,Viola Priesemann,Conor Finn,Patricia Wollstadt

doi:10.3390/e19090494

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.

Highlights

Shannon’s quantitative description of information and its transmission through a communication channel via the entropy and the channel capacity, respectively, has drawn considerable interest from the field of neuroscience from the very beginning
From the viewpoint of partial information decomposition, we hypothesized that stages (i) and (ii) should be characterized by a high fraction of unique information from a neuron’s own history because neurons that do not yet receive sufficient input to trigger their firing can only have unique mutual information with their own history
We here applied partial information decomposition (PID) to neural spike recordings with the objective to compute a measure of information modification, and, for the first time, to assess its face validity given what is already known about information processing in developing neural cultures

Summary

Introduction

Shannon’s quantitative description of information and its transmission through a communication channel via the entropy and the channel capacity, respectively, has drawn considerable interest from the field of neuroscience from the very beginning. Entropy 2017, 19, 494 incoming streams into output information that is not available from any of these streams in isolation This becomes immediately clear when looking at the meshed structure of nervous systems, where multiple communication streams converge on single neurons, and where neural output signals are sent in a divergent manner to many different receiving neurons. This structure differs dramatically from a structure solely focused on the reliable transmission of information where many parallel, but non-interacting streams would suffice. The distributed computation in neural systems may heavily rely on information modification [1]

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Conclusion