Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/135069
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Type: Journal article
Title: Early lock-in of structured and specialised information flows during neural development
Author: Shorten, D.P.
Priesemann, V.
Wibral, M.
Lizier, J.T.
Citation: eLife, 2022; 11:1-42
Publisher: eLife Sciences Publications, Ltd
Issue Date: 2022
ISSN: 2050-084X
2050-084X
Statement of
Responsibility: 
David P Shorten, Viola Priesemann, Michael Wibral, Joseph T Lizier
Abstract: The brains of many organisms are capable of complicated distributed computation underpinned by a highly advanced information processing capacity. Although substantial progress has been made towards characterising the information flow component of this capacity in mature brains, there is a distinct lack of work characterising its emergence during neural development. This lack of progress has been largely driven by the lack of effective estimators of information processing operations for spiking data. Here, we leverage recent advances in this estimation task in order to quantify the changes in transfer entropy during development. We do so by studying the changes in the intrinsic dynamics of the spontaneous activity of developing dissociated neural cell cultures. We find that the quantity of information flowing across these networks undergoes a dramatic increase across development. Moreover, the spatial structure of these flows exhibits a tendency to lock-in at the point when they arise. We also characterise the flow of information during the crucial periods of population bursts. We find that, during these bursts, nodes tend to undertake specialised computational roles as either transmitters, mediators, or receivers of information, with these roles tending to align with their average spike ordering. Further, we find that these roles are regularly locked-in when the information flows are established. Finally, we compare these results to information flows in a model network developing according to a spike-timing- dependent plasticity learning rule. Similar temporal patterns in the development of information flows were observed in these networks, hinting at the broader generality of these phenomena.
Keywords: Neurons
Action Potentials
Neuronal Plasticity
Models, Neurological
Neural Networks, Computer
Rights: © Copyright Shorten et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
DOI: 10.7554/eLife.74651
Grant ID: http://purl.org/au-research/grants/arc/DE160100630
Published version: http://dx.doi.org/10.7554/elife.74651
Appears in Collections:Molecular and Biomedical Science publications

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