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1 |
Refinement of cerebellar network organization by extracellular signaling during development
پالایش سازمان شبکه مخچه با سیگنالینگ خارج سلولی در حین توسعه-2020 The cerebellum forms regular neural network structures consisting of a few major
types of neurons, such as Purkinje cells, granule cells, and molecular layer
interneurons, and receives two major inputs from climbing fibers and mossy fibers. Its
regular structures consist of three well-defined layers, with each type of neuron
designated to a specific location and forming specific synaptic connections. During the
first few weeks of postnatal development in rodents, the cerebellum goes through
dynamic changes via proliferation, migration, differentiation, synaptogenesis, and
maturation, to create such a network structure. The development of this organized
network structure presumably relies on the communication between developing
elements in the network, including not only individual neurons, but also their dendrites,
axons, and synapses. Therefore, it is reasonable that extracellular signaling via
synaptic transmission, secreted molecules, and cell adhesion molecules, plays
important roles in cerebellar network development. Although it is not yet clear as to
how overall cerebellar development is orchestrated, there is indeed accumulating lines
of evidence that extracellular signaling acts toward the development of individual
elements in the cerebellar networks. In this article, we introduce what we have learned
from many studies regarding the extracellular signaling required for cerebellar network
development, including our recent study suggesting the importance of unbiased
synaptic inputs from parallel fibers Keywords : synaptic inputs | extracellular signaling | Purkinje cells | cerebellar granule cells | molecular layer interneurons | climbing fibers |
مقاله انگلیسی |
2 |
Constructing multilayered neural networks with sparse, data-driven connectivity using biologically-inspired, complementary, homeostatic mechanisms
ساخت شبکه های عصبی چند لایه با اتصال پراکنده و داده محور با استفاده از مکانیسم های هوموستاتیک بیولوژیکی مکمل الهام گرفته -2020 The immense complexity of the brain requires that it be built and controlled by intrinsic, self-regulating
mechanisms. One such mechanism, the formation of new connections via synaptogenesis, plays a
central role in neuronal connectivity and, ultimately, performance. Adaptive synaptogenesis networks
combine synaptogenesis, associative synaptic modification, and synaptic shedding to construct sparse
networks. Here, inspired by neuroscientific observations, novel aspects of brain development are incorporated
into adaptive synaptogenesis. The extensions include: (i) multiple layers, (ii) neuron survival
and death based on information transmission, and (iii) bigrade growth factor signaling to control the
onset of synaptogenesis in succeeding layers and to control neuron survival and death in preceding
layers. Also guiding this research is the assumption that brains must achieve a compromise between
good performance and low energy expenditures. Simulations of the network model demonstrate the
parametric and functional control of both performance and energy expenditures, where performance is
measured in terms of information loss and classification errors, and energy expenditures are assumed
to be a monotonically increasing function of the number of neurons. Major insights from this study
include (a) the key role a neural layer between two other layers has in controlling synaptogenesis
and neuron elimination, (b) the performance and energy-savings benefits of delaying the onset of
synaptogenesis in a succeeding layer, and (c) how the elimination of neurons in a preceding layer
provides energy savings, code compression, and can be accomplished without significantly degrading
information transfer or classification performance. Keywords: Synaptogenesis | Apoptosis | Brain development | Energy efficient | Unsupervised learning | Neural network |
مقاله انگلیسی |