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RISS 인기검색어
- 검색키워드
- 저자 : Jinseop S. Kim (검색결과 2 건)
- 무료
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- 유료
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Digital Museum of Retinal Ganglion Cells with Dense Anatomy and Physiology
Bae, J. Alexander,Mu, Shang,Kim, Jinseop S.,Turner, Nicholas L.,Tartavull, Ignacio,Kemnitz, Nico,Jordan, Chris S.,Norton, Alex D.,Silversmith, William M.,Prentki, Rachel,Sorek, Marissa,David, Celia,Jo Elsevier 2018 Cell Vol.173 No.5
<P><B>Summary</B></P> <P>When 3D electron microscopy and calcium imaging are used to investigate the structure and function of neural circuits, the resulting datasets pose new challenges of visualization and interpretation. Here, we present a new kind of digital resource that encompasses almost 400 ganglion cells from a single patch of mouse retina. An online “museum” provides a 3D interactive view of each cell’s anatomy, as well as graphs of its visual responses. The resource reveals two aspects of the retina’s inner plexiform layer: an arbor segregation principle governing structure along the light axis and a density conservation principle governing structure in the tangential plane. Structure is related to visual function; ganglion cells with arbors near the layer of ganglion cell somas are more sustained in their visual responses on average. Our methods are potentially applicable to dense maps of neuronal anatomy and physiology in other parts of the nervous system.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A digital “museum” provides dense anatomy and physiology of retinal ganglion cells </LI> <LI> The inner plexiform layer divides into four sublaminae defined by anatomical criteria </LI> <LI> The aggregate neurite density of a ganglion cell type is approximately uniform </LI> <LI> Inner marginal ganglion cells exhibit significantly more sustained visual responses </LI> </UL> </P> <P><B>Graphical Abstract</B></P> <P>[DISPLAY OMISSION]</P>
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Caenorhabditis elegans Connectomes of both Sexes as Image Classifiers
박창주,Jinseop S. Kim 한국뇌신경과학회 2023 Experimental Neurobiology Vol.32 No.2
Connectome, the complete wiring diagram of the nervous system of an organism, is the biological substrate of the mind. While biological neuralnetworks are crucial to the understanding of neural computation mechanisms, recent artificial neural networks (ANNs) have been developedindependently from the study of real neural networks. Computational scientists are searching for various ANN architectures to improve machinelearning since the architectures are associated with the accuracy of ANNs. A recent study used the hermaphrodite Caenorhabditis elegans (C. elegans) connectome for image classification tasks, where the edge directions were changed to construct a directed acyclic graph (DAG). In this study,we used the whole-animal connectomes of C. elegans hermaphrodite and male to construct a DAG that preserves the chief information flow in theconnectomes and trained them for image classification of MNIST and fashion-MNIST datasets. The connectome-inspired neural networks exhibitedover 99.5% and 92.6% of accuracy for MNIST and fashion-MNIST datasets, respectively, which increased from the previous study. Together,we conclude that realistic biological neural networks provide the basis of a plausible ANN architecture. This study suggests that biological networkscan provide new inspiration to improve artificial intelligences (AIs).
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