焦点功能:
Linking Experimental and Computational Connectomics
Editorial: Linking experimental and
computational connectomics
Alexander Peyser
1, Sandra Diaz Pier1, Wouter Klijn1,
Abigail Morrison1,2,3, and Jochen Triesch4
1SimLab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum
Jülich GmbH, Jülich, 德国
2Institute of Neuroscience and Medicine, Institute for Advanced Simulation, JARA Institute Brain Structure-Function
Relationships, Forschungszentrum Jülich GmbH, Jülich, 德国
3Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, Bochum, 德国
4Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe University, Frankfurt am Main, 德国
开放访问
杂志
关键词: connectomics, brain structure to function, interdisciplinary neuroscience, multiscale,
high performance computing
抽象的
Large-scale in silico experimentation depends on the generation of connectomes beyond
available anatomical structure. We suggest that linking research across the fields of
experimental connectomics, theoretical neuroscience, and high-performance computing can
enable a new generation of models bridging the gap between biophysical detail and global
function. This Focus Feature on ”Linking Experimental and Computational Connectomics”
aims to bring together some examples from these domains as a step toward the development
of more comprehensive generative models of multiscale connectomes.
An important direction for theoretical and experimental neuroscience is the development
of in silico experimentation through the use of simulation and analysis of large-scale and bi-
ologically realistic neuronal networks (Einevoll et al., 2019) in order to predict the functional
and clinical consequences of brain structure. The connectome, the ensemble of anatomical
connections between neurons (Sporns et al., 2005), is a fundamental player in the production
of brain function. Large-scale connectome data defining circuits internally and linking them
to other circuits and brain regions is essential for modeling biologically realistic networks.
Current computational infrastructure allows us to simulate models with increasingly larger
and more detailed connectomes, as well as to investigate connectivity data extracted from
experiments with increasingly higher precision and complexity, as seen in the Human Brain
项目 (Amunts et al., 2016) and the Brain Initiative (Mott et al., 2018). 然而, anatomical
structures in connectomic datasets are incomplete and contain large uncertainties (Bakker
等人。, 2012), while functional data cannot fully specify unique connectomes—the structure/
function relationship is not uniquely invertible. How can we go from incomplete structural
data to predicted functional behavior to enable in silico experimentation?
One way to address this challenge is the generation of neural connectivity, 例如,
through either simulated individual development (Nowke et al., 2018) or simulated evo-
lutionary processes (Avena-Koenigsberger et al., 2014; Bellec et al., 2018). The scientific in-
frastructure to enable the construction of connectomes at either level may involve generative
connectivity rules, homeostatic and plasticity rules transforming functional networks, detailed
morphological data perturbed to fit behavioral outputs, and the improvement of simulator
引文: Peyser, A。, Diaz Pier, S。, Klijn,
W., Morrison, A。, & Triesch, J. (2019).
Editorial: Linking experimental and
computational connectomics. 网络
神经科学, 3(4), 902–904.
https://doi.org/10.1162/netn_e_00108
DOI:
https://doi.org/10.1162/netn_e_00108
已收到: 20 八月 2019
利益争夺: 作者有
声明不存在竞争利益
存在.
通讯作者:
Alexander Peyser
a.peyser@fz-juelich.de
版权: © 2019
麻省理工学院
在知识共享下发布
归因 4.0 国际的
(抄送 4.0) 执照
麻省理工学院出版社
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Editorial: Linking experimental and computational connectomics
technology to couple all these elements of connectome construction and to enable direct
interaction by scientists using visualizing technology. To catalyze the combination of such
interdisciplinary development, a satellite workshop at the 2017 Bernstein Conference in
Göttingen, 德国, on “Connectivity generation, exploration and visualization for large-
scale neural networks” was organized, spanning generative rules, simulator integration, 六-
实际化, mapping of experimental data to connectome generation and dynamic processes
at multiple timescales.
The presentations covered both technical and scientific questions at multiple organi-
zational levels: How should an abstract representation of connectivity be derived from ex-
perimental data? Which connectivity elements are essential for characterizing different levels
of organization? What is the interplay between changes in connectivity and slow processes
like neuromodulation, neurodegeneration, and neurodevelopment? What are the essential
features of connectivity that impact function, at which scales, and to which extent? A lack
of definite answers to these questions has limited our ability to exploit the extremely com-
plex data acquired through experiments in models of neural networks for simulation and
分析.
Over two days, 11 presentations by workers in the fields of network neuroscience, com-
puter science, high-performance computing and experimental neuroscience helped to eluci-
date some answers to these challenges. Scientists attending from throughout Europe ranged
from master’s students entering the field to renowned senior scientists. In this Focus Feature
on “Linking Experimental and Computational Connectomics,” we present a selection of work
derived from these discussions from both theoretical and experimental points of view.
Hilgetag et al. (2019) give us a panoramic view on our current knowledge of cortical con-
nectivity in the primate brain. With their analysis of fundamental structural parameters in dif-
ferent cortical areas, they demonstrate the existence of an architecture that can explain and
predict the structural organization of the brain across scales.
Whether during development, 学习, adaptation, input integration, 疾病, or recovery
after lesions, the connectivity in the brain is always changing. Lu et al. (2019) explore the
impact of external stimulation on the brain’s structure for therapeutic purposes and provide
initial insight on the inner mechanisms regulating long-term functional recovery by connec-
tivity rewiring.
Hailing from the experimental methods side of the field, Rojas et al. (2019) introduce a
framework based on wavelet transform methods and apply it to the analysis of complex mul-
tiscale dynamic events in in vivo long-term loose patch clamp recordings of the cockroach
circadian clock circuits. This novel toolset will enable the next step in the detection of corre-
lations in multiscale neuronal behaviors and thus advance functional connectomics.
Interdisciplinary venues such as this workshop, training events, and journals in the spirit
of Network Neuroscience promote progress across the divide between experimental, theoreti-
卡尔, and computational connectomic neuroscience. Collaboration across these disciplinary do-
mains is critical toward developing a multiscale understanding of neuronal networks capable of
bridging the gap between local biochemical details on the timescale of milliseconds and global
plasticity and behavior occurring over hours and even the entire life span. The rise of genera-
tive connectomics—unifying experimental, theoretical, and computational approaches—is an
important step toward grappling with such challenges of scale in the face of necessary limits
in experimental constraints, requiring collaboration across neuroscience and beyond.
网络神经科学
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Editorial: Linking experimental and computational connectomics
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网络神经科学
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