Growth rules for the repair of Asynchronous Irregular neuronal networks after peripheral lesions.

PLoS Comput Biol

UH Biocomputation Research Group, Centre for Computer Science and Informatics Research, University of Hertfordshire, Hatfield United Kingdom.

Published: June 2021

AI Article Synopsis

  • The brain uses homeostatic mechanisms like structural plasticity to restore neuronal activity after disruptions, such as peripheral lesions, by changing how neurons connect.
  • Research indicates that neuronal activity influences the growth and retraction of neurites, and a new spiking network model was developed to mimic how networks repair themselves after such disruptions, specifically focusing on how activity-dependent growth affects both excitatory and inhibitory connections.
  • The study finds that contrasting growth rules for excitatory and inhibitory elements are crucial for re-establishing activity and forming new synapses, ultimately leading to the restoration of the network's activity to its original levels.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8195387PMC
http://dx.doi.org/10.1371/journal.pcbi.1008996DOI Listing

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