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Growth rules for the repair of Asynchronous Irregular neuronal networks after peripheral lesions.
Sinha, Ankur; Metzner, Christoph; Davey, Neil; Adams, Roderick; Schmuker, Michael; Steuber, Volker.
Afiliação
  • Sinha A; UH Biocomputation Research Group, Centre for Computer Science and Informatics Research, University of Hertfordshire, Hatfield United Kingdom.
  • Metzner C; UH Biocomputation Research Group, Centre for Computer Science and Informatics Research, University of Hertfordshire, Hatfield United Kingdom.
  • Davey N; Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany.
  • Adams R; UH Biocomputation Research Group, Centre for Computer Science and Informatics Research, University of Hertfordshire, Hatfield United Kingdom.
  • Schmuker M; UH Biocomputation Research Group, Centre for Computer Science and Informatics Research, University of Hertfordshire, Hatfield United Kingdom.
  • Steuber V; UH Biocomputation Research Group, Centre for Computer Science and Informatics Research, University of Hertfordshire, Hatfield United Kingdom.
PLoS Comput Biol ; 17(6): e1008996, 2021 06.
Article em En | MEDLINE | ID: mdl-34061830
ABSTRACT
Several homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology that underlie modifications of synapses in these networks, the underlying mechanisms that drive these changes are yet to be explained. Evidence suggests that neuronal activity modulates neurite morphology and may stimulate neurites to selective sprout or retract to restore network activity levels. We developed a new spiking network model of peripheral lesioning and accurately reproduced the characteristics of network repair after deafferentation that are reported in experiments to study the activity dependent growth regimes of neurites. To ensure that our simulations closely resemble the behaviour of networks in the brain, we model deafferentation in a biologically realistic balanced network model that exhibits low frequency Asynchronous Irregular (AI) activity as observed in cerebral cortex. Our simulation results indicate that the re-establishment of activity in neurons both within and outside the deprived region, the Lesion Projection Zone (LPZ), requires opposite activity dependent growth rules for excitatory and inhibitory post-synaptic elements. Analysis of these growth regimes indicates that they also contribute to the maintenance of activity levels in individual neurons. Furthermore, in our model, the directional formation of synapses that is observed in experiments requires that pre-synaptic excitatory and inhibitory elements also follow opposite growth rules. Lastly, we observe that our proposed structural plasticity growth rules and the inhibitory synaptic plasticity mechanism that also balances our AI network both contribute to the restoration of the network to pre-deafferentation stable activity levels.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Córtex Cerebral / Modelos Neurológicos / Rede Nervosa Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Córtex Cerebral / Modelos Neurológicos / Rede Nervosa Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Ano de publicação: 2021 Tipo de documento: Article