Early lock-in of structured and specialised information flows during neural development

  1. David P Shorten  Is a corresponding author
  2. Viola Priesemann
  3. Michael Wibral
  4. Joseph T Lizier
  1. University of Sydney, Australia
  2. Max Planck Institute for Dynamics and Self-Organization, Germany
  3. Georg August University, Germany


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 the spiking data available for developing neural networks. 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, after which there is a substantial temporal correlation in the information flows across recording days. We analyse 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 - either early, mid or late in the bursts. Further, we find that the specialised computational roles occupied by nodes during bursts are regularly locked-in when the information flows are established. Finally, we briefly compare these results to information flows in a model network developing according to an STDP learning rule from a state of independent firing to synchronous bursting. The phenomena of large increases in information flow, early lock-in of information flow spatial structure and computational roles based on burst position were also observed in this model, hinting at the broader generality of these phenomena.

Data availability

This work made use of a publicly available dataset which can be found at: http://neurodatasharing.bme.gatech.edu/development-data/html/index.htmlAnalysis scripts are available at: https://bitbucket.org/dpshorten/cell_cultures

The following previously published data sets were used

Article and author information

Author details

  1. David P Shorten

    Faculty of Engineering, University of Sydney, Sydney, Australia
    For correspondence
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2412-4705
  2. Viola Priesemann

    MPRG Priesemann, Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8905-5873
  3. Michael Wibral

    Campus Institute for Dynamics of Biological Networks, Georg August University, Göttingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  4. Joseph T Lizier

    Faculty of Engineering, University of Sydney, The University of Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9910-8972


Australian Research Council (DE160100630)

  • Joseph T Lizier

University of Sydney (SOAR Fellowship)

  • Joseph T Lizier

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Tatyana O Sharpee, Salk Institute for Biological Studies, United States

Version history

  1. Preprint posted: June 30, 2021 (view preprint)
  2. Received: October 12, 2021
  3. Accepted: March 13, 2022
  4. Accepted Manuscript published: March 14, 2022 (version 1)
  5. Version of Record published: May 3, 2022 (version 2)


© 2022, Shorten et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.


  • 410
    Page views
  • 92
  • 2

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. David P Shorten
  2. Viola Priesemann
  3. Michael Wibral
  4. Joseph T Lizier
Early lock-in of structured and specialised information flows during neural development
eLife 11:e74651.

Share this article


Further reading

    1. Cell Biology
    2. Computational and Systems Biology
    Thomas Grandits, Christoph M Augustin ... Alexander Jung
    Research Article

    Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimental data can become a significant computational burden. To mitigate the computational burden, the present study introduces a neural network (NN) that emulates the AP for given maximum conductances of selected ion channels, pumps, and exchangers. Its applicability in pharmacological studies was tested on synthetic and experimental data. The NN emulator potentially enables massive speed-ups compared to regular simulations and the forward problem (find drugged AP for pharmacological parameters defined as scaling factors of control maximum conductances) on synthetic data could be solved with average root-mean-square errors (RMSE) of 0.47 mV in normal APs and of 14.5 mV in abnormal APs exhibiting early afterdepolarizations (72.5% of the emulated APs were alining with the abnormality, and the substantial majority of the remaining APs demonstrated pronounced proximity). This demonstrates not only very fast and mostly very accurate AP emulations but also the capability of accounting for discontinuities, a major advantage over existing emulation strategies. Furthermore, the inverse problem (find pharmacological parameters for control and drugged APs through optimization) on synthetic data could be solved with high accuracy shown by a maximum RMSE of 0.22 in the estimated pharmacological parameters. However, notable mismatches were observed between pharmacological parameters estimated from experimental data and distributions obtained from the Comprehensive in vitro Proarrhythmia Assay initiative. This reveals larger inaccuracies which can be attributed particularly to the fact that small tissue preparations were studied while the emulator was trained on single cardiomyocyte data. Overall, our study highlights the potential of NN emulators as powerful tool for an increased efficiency in future quantitative systems pharmacology studies.

    1. Computational and Systems Biology
    2. Neuroscience
    Domingos Leite de Castro, Miguel Aroso ... Paulo Aguiar
    Research Article Updated

    Closed-loop neuronal stimulation has a strong therapeutic potential for neurological disorders such as Parkinson’s disease. However, at the moment, standard stimulation protocols rely on continuous open-loop stimulation and the design of adaptive controllers is an active field of research. Delayed feedback control (DFC), a popular method used to control chaotic systems, has been proposed as a closed-loop technique for desynchronisation of neuronal populations but, so far, was only tested in computational studies. We implement DFC for the first time in neuronal populations and access its efficacy in disrupting unwanted neuronal oscillations. To analyse in detail the performance of this activity control algorithm, we used specialised in vitro platforms with high spatiotemporal monitoring/stimulating capabilities. We show that the conventional DFC in fact worsens the neuronal population oscillatory behaviour, which was never reported before. Conversely, we present an improved control algorithm, adaptive DFC (aDFC), which monitors the ongoing oscillation periodicity and self-tunes accordingly. aDFC effectively disrupts collective neuronal oscillations restoring a more physiological state. Overall, these results support aDFC as a better candidate for therapeutic closed-loop brain stimulation.