MODELING THE NEURAL ACTIVITY OF CAENORHABDITIS ELEGANS THROUGH NEURAL MESSAGE PASSING.
The great complexity of the human connectome motivates the study of a simpler neural network. For that purpose, the Ising Model was applied on experimental data on the synaptic connectivity of Caenorhabditis elegans (C. elegans) in resting-state, assigning a binary variable (representing active or i...
| Publicado en: | Revista Cubana de Física Vol. 40; pp. 39 - 44 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Universidad de La Habana
2023 Special Issue
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=lth&AN=170037223&site=ehost-live header: @attributes: shortDbName: lth uiTerm: 170037223 longDbName: MedicLatina uiTag: AN controlInfo: bkinfo: jinfo: jid: 02539268 UEW jtl: Revista Cubana de Física issn: 02539268 maglogo: N pubinfo: dt: 2023 Special Issue vid: 40 pid: 21208 pub: Universidad de La Habana artinfo: ui: 170037223 ppf: 39 ppct: 5 formats: fmt: @attributes: type: P size: 610KB tig: atl: MODELING THE NEURAL ACTIVITY OF CAENORHABDITIS ELEGANS THROUGH NEURAL MESSAGE PASSING. aug: au: HERNÁNDEZ-HERNÁNDEZ, Y. MACHADO, D. MULET, R. affil: Centro de Sistemas Complejos, Departamento de Física Aplicada. Facultad de Física, Universidad de La Habana, Cuba su: Artificial neural networks Glass transitions Life cycles (Biology) Ising model Biocomplexity System dynamics Caenorhabditis elegans Interneurons Neural circuitry Algorithms sug: subj: Artificial neural networks Glass transitions Life cycles (Biology) Ising model Biocomplexity System dynamics Caenorhabditis elegans Interneurons Neural circuitry Algorithms ab: The great complexity of the human connectome motivates the study of a simpler neural network. For that purpose, the Ising Model was applied on experimental data on the synaptic connectivity of Caenorhabditis elegans (C. elegans) in resting-state, assigning a binary variable (representing active or inactive states) to each neuron in the network. The dynamics of this system is postulated as a message passing network, encoded by the Belief Propagation algorithm (BP) in its criticality region. The inferences of neuronal activity maps were obtained for different times of the nematode's life cycle. We determined the network susceptibilities as a measure of correlations in the system through the Susceptibility Propagation algorithm (SP). Finally, we applied clustering methods to obtain functional clusters and analyze similarities between them and the real functional clusters (sensory, interneurons and motor). All this contributed to the analysis of structure-function relationship in the C. elegans neural network. La gran complejidad del cerebro humano incentiva el estudio de una red neuronal mucho más simple. Para eso, se aplicó el modelo de Ising sobre datos experimentales de la conectividad sinóptica del Caenorhabditis elegans en estado de reposo, asignándole a cada neurona de la red un comportamiento binario (activo o inactivo). La dinámica de este sistema se postula como una red de transmisión de mensajes dada por el algoritmo Belief Propagation (BP) en su región de criticalidad. Se obtuvieron mapas de inferencia de actividad neuronal para diferentes tiempos del ciclo de vida del nematodo. Además, determinamos las susceptibilidades de la red como medida de las correlaciones del sistema a través del algoritmo Susceptibility Propagation (SP). Finalmente, aplicamos métodos de clustering para inferir módulos funcionales en la red y analizar semejanzas entre estos y los módulos funcionales reales (sensoriales, inter-neuronas y motores). Todo esto contribuyó al análisis de la relación estructura-función en la red neuronal del C. elegans. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Revista Cubana de Física is the property of Universidad de La Habana and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Revista Cubana de Física holder: Universidad de La Habana dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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