Retinal waves in adaptive rewiring networks orchestrate convergence and divergence in the visual system.

Evolving networks Retinal ganglion cells Synaptic remodeling

Journal

Network neuroscience (Cambridge, Mass.)
ISSN: 2472-1751
Titre abrégé: Netw Neurosci
Pays: United States
ID NLM: 101719149

Informations de publication

Date de publication:
2024
Historique:
received: 16 10 2023
accepted: 26 02 2024
medline: 2 10 2024
pubmed: 2 10 2024
entrez: 2 10 2024
Statut: epublish

Résumé

Spontaneous retinal wave activity shaping the visual system is a complex neurodevelopmental phenomenon. Retinal ganglion cells are the hubs through which activity diverges throughout the visual system. We consider how these divergent hubs emerge, using an adaptively rewiring neural network model. Adaptive rewiring models show in a principled way how brains could achieve their complex topologies. Modular small-world structures with rich-club effects and circuits of convergent-divergent units emerge as networks evolve, driven by their own spontaneous activity. Arbitrary nodes of an initially random model network were designated as retinal ganglion cells. They were intermittently exposed to the retinal waveform, as the network evolved through adaptive rewiring. A significant proportion of these nodes developed into divergent hubs within the characteristic complex network architecture. The proportion depends parametrically on the wave incidence rate. Higher rates increase the likelihood of hub formation, while increasing the potential of ganglion cell death. In addition, direct neighbors of designated ganglion cells differentiate like amacrine cells. The divergence observed in ganglion cells resulted in enhanced convergence downstream, suggesting that retinal waves control the formation of convergence in the lateral geniculate nuclei. We conclude that retinal waves stochastically control the distribution of converging and diverging activity in evolving complex networks. Retinal waves consist of spontaneous neural activity that propagates across the retina during neural development. We simulate the intermittent spread of retinal waveforms originating from a designated node in an adaptively rewiring neural network model. Adaptive rewiring models simulate, in a highly abstracted manner, how brains may achieve their complex topologies during development. This way, we aim to uncover basic principles of neural maturation in the visual system. Namely, we seek to shed light onto how retinal waves might be responsible for the differentiation of immature neurons into specific cell types (e.g., retinal ganglion cells, amacrine cells) and how these waves shape the connectivity structure in the visual system.

Autres résumés

Type: plain-language-summary (eng)
Retinal waves consist of spontaneous neural activity that propagates across the retina during neural development. We simulate the intermittent spread of retinal waveforms originating from a designated node in an adaptively rewiring neural network model. Adaptive rewiring models simulate, in a highly abstracted manner, how brains may achieve their complex topologies during development. This way, we aim to uncover basic principles of neural maturation in the visual system. Namely, we seek to shed light onto how retinal waves might be responsible for the differentiation of immature neurons into specific cell types (e.g., retinal ganglion cells, amacrine cells) and how these waves shape the connectivity structure in the visual system.

Identifiants

pubmed: 39355440
doi: 10.1162/netn_a_00370
pii: netn_a_00370
pmc: PMC11340993
doi:

Types de publication

Journal Article

Langues

eng

Pagination

653-672

Informations de copyright

© 2024 Massachusetts Institute of Technology.

Déclaration de conflit d'intérêts

Competing Interests: The authors have declared that no competing interests exist.

Auteurs

Raúl Luna (R)

Department of Psychobiology and Methodology in Behavioural Sciences, Faculty of Psychology, Universidad Complutense de Madrid, Madrid, Spain.
Institute of Optics, Spanish National Research Council (CSIC), Madrid, Spain.
KU Leuven, Brain and Cognition, Leuven, Belgium.

Jia Li (J)

KU Leuven, Brain and Cognition, Leuven, Belgium.

Roman Bauer (R)

NICE Research Group, Computer Science Research Centre, University of Surrey, Guildford, UK.

Cees van Leeuwen (C)

KU Leuven, Brain and Cognition, Leuven, Belgium.
RPTU Kaiserslautern, Cognitive Science, Kaiserslautern, Germany.

Classifications MeSH