Development of neural network structure with biological mechanisms

Samuel Johnson; Joaquín Marro; Jorge F. Mejias; Joaquín J. Torres

doi:10.1007/978-3-642-02478-8_29

Development of neural network structure with biological mechanisms

Samuel Johnson^*
, Joaquín Marro
, Jorge F. Mejias
, Joaquín J. Torres

^*Corresponding author for this work

Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We present an evolving neural network model in which synapses appear and disappear stochastically according to bio-inspired probabilities. These are in general nonlinear functions of the local fields felt by neurons-akin to electrical stimulation-and of the global average field-representing total energy consumption. We find that initial degree distributions then evolve towards stationary states which can either be fairly homogeneous or highly heterogeneous, depending on parameters. The critical cases-which can result in scale-free distributions-are shown to correspond, under a mean-field approximation, to nonlinear drift-diffusion equations. We show how appropriate choices of parameters yield good quantitative agreement with published experimental data concerning synaptic densities during brain development (synaptic pruning).

Original language	English
Title of host publication	Bio-Inspired Systems
Subtitle of host publication	Computational and Ambient Intelligence - 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings
Pages	228-235
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-02478-8_29
Publication status	Published - 2009
Event	10th International Work-Conference on Artificial Neural Networks, IWANN 2009 - Salamanca, Spain Duration: 10 Jun 2009 → 12 Jun 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	5517 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Work-Conference on Artificial Neural Networks, IWANN 2009
Country/Territory	Spain
City	Salamanca
Period	10/06/09 → 12/06/09

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Brain development
Neural networks
Synaptic pruning

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-02478-8_29

Cite this

Johnson, S., Marro, J., Mejias, J. F., & Torres, J. J. (2009). Development of neural network structure with biological mechanisms. In Bio-Inspired Systems: Computational and Ambient Intelligence - 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings (PART 1 ed., pp. 228-235). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5517 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-02478-8_29

Johnson, Samuel ; Marro, Joaquín ; Mejias, Jorge F. et al. / Development of neural network structure with biological mechanisms. Bio-Inspired Systems: Computational and Ambient Intelligence - 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings. PART 1. ed. 2009. pp. 228-235 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

@inproceedings{8c9c6d725cbe41a2ac77f5f24845b974,

title = "Development of neural network structure with biological mechanisms",

abstract = "We present an evolving neural network model in which synapses appear and disappear stochastically according to bio-inspired probabilities. These are in general nonlinear functions of the local fields felt by neurons-akin to electrical stimulation-and of the global average field-representing total energy consumption. We find that initial degree distributions then evolve towards stationary states which can either be fairly homogeneous or highly heterogeneous, depending on parameters. The critical cases-which can result in scale-free distributions-are shown to correspond, under a mean-field approximation, to nonlinear drift-diffusion equations. We show how appropriate choices of parameters yield good quantitative agreement with published experimental data concerning synaptic densities during brain development (synaptic pruning).",

keywords = "Brain development, Neural networks, Synaptic pruning",

author = "Samuel Johnson and Joaqu{\'i}n Marro and Mejias, \{Jorge F.\} and Torres, \{Joaqu{\'i}n J.\}",

year = "2009",

doi = "10.1007/978-3-642-02478-8\_29",

language = "English",

isbn = "3642024777",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 1",

pages = "228--235",

booktitle = "Bio-Inspired Systems",

edition = "PART 1",

note = "10th International Work-Conference on Artificial Neural Networks, IWANN 2009 ; Conference date: 10-06-2009 Through 12-06-2009",

}

Johnson, S, Marro, J, Mejias, JF & Torres, JJ 2009, Development of neural network structure with biological mechanisms. in Bio-Inspired Systems: Computational and Ambient Intelligence - 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 5517 LNCS, pp. 228-235, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Salamanca, Spain, 10/06/09. https://doi.org/10.1007/978-3-642-02478-8_29

Development of neural network structure with biological mechanisms. / Johnson, Samuel; Marro, Joaquín; Mejias, Jorge F. et al.
Bio-Inspired Systems: Computational and Ambient Intelligence - 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings. PART 1. ed. 2009. p. 228-235 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5517 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Development of neural network structure with biological mechanisms

AU - Johnson, Samuel

AU - Marro, Joaquín

AU - Mejias, Jorge F.

AU - Torres, Joaquín J.

PY - 2009

Y1 - 2009

N2 - We present an evolving neural network model in which synapses appear and disappear stochastically according to bio-inspired probabilities. These are in general nonlinear functions of the local fields felt by neurons-akin to electrical stimulation-and of the global average field-representing total energy consumption. We find that initial degree distributions then evolve towards stationary states which can either be fairly homogeneous or highly heterogeneous, depending on parameters. The critical cases-which can result in scale-free distributions-are shown to correspond, under a mean-field approximation, to nonlinear drift-diffusion equations. We show how appropriate choices of parameters yield good quantitative agreement with published experimental data concerning synaptic densities during brain development (synaptic pruning).

AB - We present an evolving neural network model in which synapses appear and disappear stochastically according to bio-inspired probabilities. These are in general nonlinear functions of the local fields felt by neurons-akin to electrical stimulation-and of the global average field-representing total energy consumption. We find that initial degree distributions then evolve towards stationary states which can either be fairly homogeneous or highly heterogeneous, depending on parameters. The critical cases-which can result in scale-free distributions-are shown to correspond, under a mean-field approximation, to nonlinear drift-diffusion equations. We show how appropriate choices of parameters yield good quantitative agreement with published experimental data concerning synaptic densities during brain development (synaptic pruning).

KW - Brain development

KW - Neural networks

KW - Synaptic pruning

UR - https://www.scopus.com/pages/publications/68749118721

U2 - 10.1007/978-3-642-02478-8_29

DO - 10.1007/978-3-642-02478-8_29

M3 - Conference contribution

AN - SCOPUS:68749118721

SN - 3642024777

SN - 9783642024771

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 228

EP - 235

BT - Bio-Inspired Systems

T2 - 10th International Work-Conference on Artificial Neural Networks, IWANN 2009

Y2 - 10 June 2009 through 12 June 2009

ER -

Johnson S, Marro J, Mejias JF, Torres JJ. Development of neural network structure with biological mechanisms. In Bio-Inspired Systems: Computational and Ambient Intelligence - 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings. PART 1 ed. 2009. p. 228-235. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-02478-8_29

Development of neural network structure with biological mechanisms

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this