Growth strategy determines the memory and structural properties of brain networks

Ana P. Millán; Joaquín J. Torres; Samuel Johnson; J. Marro

doi:10.1016/j.neunet.2021.04.027

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

Mathematics

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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 language	English
Pages (from-to)	44-56
Number of pages	13
Journal	Neural Networks
Volume	142
Early online date	26 Apr 2021
DOIs	https://doi.org/10.1016/j.neunet.2021.04.027
Publication status	Published - 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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

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title = "Growth strategy determines the memory and structural properties of brain networks",

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.",

keywords = "Associative memory, Brain development, Co-evolving neural network, Complex networks, Temporal networks",

author = "Mill{\'a}n, {Ana P.} and Torres, {Joaqu{\'i}n J.} and Samuel Johnson and J. Marro",

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 Espa{\~n}ola de Investigaci{\'o}n (AEI), Spain under grant FIS2017-84256-P (FEDER funds) and from the Consejer{\'i}a de Conocimiento, Investigaci{\'o}n Universidad, Junta de Andaluc{\'i}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 . Publisher Copyright: {\textcopyright} 2021 The Author(s)",

year = "2021",

month = oct,

doi = "10.1016/j.neunet.2021.04.027",

language = "English",

volume = "142",

pages = "44--56",

journal = "Neural Networks",

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AU - Millán, Ana P.

AU - Torres, Joaquín J.

AU - Johnson, Samuel

AU - Marro, J.

N1 - 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)

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N2 - 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.

AB - 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.

KW - Associative memory

KW - Brain development

KW - Co-evolving neural network

KW - Complex networks

KW - Temporal networks

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JO - Neural Networks

JF - Neural Networks

ER -

Growth strategy determines the memory and structural properties of brain networks

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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