Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Aynaz Taheri"'
Autor:
Daniel A. Llano, Chihua Ma, Umberto Di Fabrizio, Aynaz Taheri, Kevin A. Stebbings, Georgiy Yudintsev, Gang Xiao, Robert V. Kenyon, Tanya Y. Berger-Wolf
Publikováno v:
Network Neuroscience, Vol 5, Iss 2, Pp 569-590 (2021)
AbstractNetwork analysis of large-scale neuroimaging data is a particularly challenging computational problem. Here, we adapt a novel analytical tool, the community dynamic inference method (CommDy), for brain imaging data from young and aged mice. C
Externí odkaz:
https://doaj.org/article/2edca82b330149eea595b897b44e83d7
Publikováno v:
Applied Network Science, Vol 4, Iss 1, Pp 1-26 (2019)
Abstract We propose sequence-to-sequence architectures for graph representation learning in both supervised and unsupervised regimes. Our methods use recurrent neural networks to encode and decode information from graph-structured data. Recurrent neu
Externí odkaz:
https://doaj.org/article/47e448b5bc5d4952bf23bba30c359eb3
Autor:
Ashwini G, Naik, Robert V, Kenyon, Aynaz, Taheri, Tanya Y, BergerWolf, Baher A, Ibrahim, Yoshitaka, Shinagawa, Daniel A, Llano
Publikováno v:
Journal of Neuroscience Research. 101:217-231
Understanding functional correlations between the activities of neuron populations is vital for the analysis of neuronal networks. Analyzing large-scale neuroimaging data obtained from hundreds of neurons simultaneously poses significant visualizatio
Autor:
Gang Xiao, Tanya Y. Berger-Wolf, Georgiy Yudintsev, Daniel A. Llano, Umberto Di Fabrizio, Chihua Ma, Aynaz Taheri, Kevin A. Stebbings, Robert V. Kenyon
Publikováno v:
Network Neuroscience, Vol 5, Iss 2, Pp 569-590 (2021)
Network Neuroscience
Network Neuroscience
Network analysis of large-scale neuroimaging data is a particularly challenging computational problem. Here, we adapt a novel analytical tool, the community dynamic inference method (CommDy), for brain imaging data from young and aged mice. CommDy, w
Autor:
Daniel A. Llano, Robert V. Kenyon, Baher A. Ibrahim, Tanya Y. Berger-Wolf, Aynaz Taheri, Ashwini G. Naik
BackgroundUnderstanding functional correlations between the activities of neuron populations is vital for the analysis of neuronal networks. Analyzing large-scale neuroimaging data obtained from hundreds of neurons simultaneously poses significant vi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9cebdf1afd6c34dadaaf14ffb77aee2a
https://doi.org/10.1101/2020.12.03.410761
https://doi.org/10.1101/2020.12.03.410761
Autor:
Aynaz Taheri
Representing and comparing graphs is a central problem in many fields. We propose sequence- to-sequence architectures for graph representation learning in both static and dynamic settings. Our methods use recurrent neural networks to encode and decod
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b56bb3be4cc7b55c58a1f898e9733c04
Autor:
Tanya Y. Berger-Wolf, Aynaz Taheri
Publikováno v:
ASONAM
In recent years, substantial effort has been devoted to learning to represent the static graphs and their substructures. A few studies explored utilizing temporal information available in a dynamic setting in order to address the node representation
Publikováno v:
WWW (Companion Volume)
Graph representation learning for static graphs is a well studied topic. Recently, a few studies have focused on learning temporal information in addition to the topology of a graph. Most of these studies have relied on learning to represent nodes an
Autor:
Caitlin A Murphy, Baher A. Ibrahim, Tanya Y. Berger-Wolf, Georgiy Yudintsev, Robert V. Kenyon, Daniel A. Llano, Aynaz Taheri, Matthew I. Banks, Guido Muscioni
Since the discovery of the receptive field, scientists have tracked receptive field structure to gain insights about mechanisms of sensory processing. At the level of the thalamus and cortex, this linear filter approach has been challenged by finding
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b84ba16072a1bbe9e9872579dd2679f
Propagation of cortical activity via open-loop intrathalamic architectures: a computational analysis
Propagation of signals across the cerebral cortex is a core component of many cognitive processes and is generally thought to be mediated by direct intracortical connectivity. The thalamus, by contrast, is considered to be devoid of internal connecti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b42453230a3c424cdd947ccafe7a4391
https://doi.org/10.1101/574178
https://doi.org/10.1101/574178