Emergence of spontaneous assembly activity in developing neural networks without afferent input

Marcus A. Triplett, Lilach Avitan, Geoffrey J. Goodhill*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Spontaneous activity is a fundamental characteristic of the developing nervous system. Intriguingly, it often takes the form of multiple structured assemblies of neurons. Such assemblies can form even in the absence of afferent input, for instance in the zebrafish optic tectum after bilateral enucleation early in life. While the development of neural assemblies based on structured afferent input has been theoretically well-studied, it is less clear how they could arise in systems without afferent input. Here we show that a recurrent network of binary threshold neurons with initially random weights can form neural assemblies based on a simple Hebbian learning rule. Over development the network becomes increasingly modular while being driven by initially unstructured spontaneous activity, leading to the emergence of neural assemblies. Surprisingly, the set of neurons making up each assembly then continues to evolve, despite the number of assemblies remaining roughly constant. In the mature network assembly activity builds over several timesteps before the activation of the full assembly, as recently observed in calcium-imaging experiments. Our results show that Hebbian learning is sufficient to explain the emergence of highly structured patterns of neural activity in the absence of structured input.

Original languageAmerican English
Article numbere1006421
JournalPLoS Computational Biology
Volume14
Issue number9
DOIs
StatePublished - Sep 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018 Triplett et al. http://creativecommons.org/licenses/by/4.0/.

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