Growth strategy determines the memory and structural properties of brain networks

Ana P. Millán*, Joaquín J. Torres, Samuel Johnson, J. Marro

*Corresponding author for this work

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Abstract

The interplay between structure and function affects the emerging properties of many natural systems. Here we use an adaptive neural network model that couples activity and topological dynamics and reproduces the experimental temporal profiles of synaptic density observed in the brain. We prove that the existence of a transient period of relatively high synaptic connectivity is critical for the development of the system under noise circumstances, such that the resulting network can recover stored memories. Moreover, we show that intermediate synaptic densities provide optimal developmental paths with minimum energy consumption, and that ultimately it is the transient heterogeneity in the network that determines its evolution. These results could explain why the pruning curves observed in actual brain areas present their characteristic temporal profiles and they also suggest new design strategies to build biologically inspired neural networks with particular information processing capabilities.

Original languageEnglish
Pages (from-to)44-56
Number of pages13
JournalNeural Networks
Volume142
Early online date26 Apr 2021
DOIs
Publication statusPublished - Oct 2021

Bibliographical note

Funding Information:
The authors acknowledge financial support from the Spanish Ministry of Science and Technology, and the Agencia Espa?ola de Investigaci?n (AEI), Spain under grant FIS2017-84256-P (FEDER funds) and from the Consejer?a de Conocimiento, Investigaci?n Universidad, Junta de Andaluc?a and European Regional Development Funds, Spain, Refs. SOMM17/6105/UGR and A-FQM-175-UGR18. APM also acknowledges support from ?Obra Social La Caixa, Spain? (ID 100010434 with code LCF/BQ/ES15/10360004) and from ZonMw, Netherlands and the Dutch Epilepsy Foundation, Netherlands, project number 95105006. SJ acknowledges support from the Alan Turing Institute under EPSRC, United Kingdom Grant No. EP/N510129/1.

Funding Information:
The authors acknowledge financial support from the Spanish Ministry of Science and Technology , and the Agencia Española de Investigación (AEI), Spain under grant FIS2017-84256-P (FEDER funds) and from the Consejería de Conocimiento, Investigación Universidad, Junta de Andalucía and European Regional Development Funds, Spain , Refs. SOMM17/6105/UGR and A-FQM-175-UGR18. APM also acknowledges support from “ Obra Social La Caixa, Spain ” (ID 100010434 with code LCF/BQ/ES15/10360004 ) and from ZonMw, Netherlands and the Dutch Epilepsy Foundation, Netherlands , project number 95105006. SJ acknowledges support from the Alan Turing Institute under EPSRC, United Kingdom Grant No. EP/N510129/1 .

Publisher Copyright:
© 2021 The Author(s)

Keywords

  • Associative memory
  • Brain development
  • Co-evolving neural network
  • Complex networks
  • Temporal networks

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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