期刊
ANNUAL REVIEW OF NEUROSCIENCE, VOL 44, 2021
卷 44, 期 -, 页码 275-293出版社
ANNUAL REVIEWS
DOI: 10.1146/annurev-neuro-110220-013050
关键词
neuronal circuit; wiring diagram; zebrafish; neuronal computation; volume electron microscopy
资金
- Novartis Research Foundation
- Swiss National Science Foundation [31003A_135196, 310030B_1528331, 310030A_172925]
- European Research Council under the European Union [742576]
- C.V. Starr Fellowship in Neuroscience from Princeton University
- Swiss National Science Foundation (SNF) [31003A_135196] Funding Source: Swiss National Science Foundation (SNF)
- European Research Council (ERC) [742576] Funding Source: European Research Council (ERC)
Dynamic connectomics in zebrafish can provide insights into the circuit mechanisms underlying higher-order neuronal computations by combining reconstructions of wiring diagrams with measurements of neuronal population activity and behavior.
The dense reconstruction of neuronal wiring diagrams from volumetric electron microscopy data has the potential to generate fundamentally new insights into mechanisms of information processing and storage in neuronal circuits. Zebrafish provide unique opportunities for dynamical connectomics approaches that combine reconstructions of wiring diagrams with measurements of neuronal population activity and behavior. Such approaches have the power to reveal higher-order structure in wiring diagrams that cannot be detected by sparse sampling of connectivity and that is essential for neuronal computations. In the brain stem, recurrently connected neuronal modules were identified that can account for slow, low-dimensional dynamics in an integrator circuit. In the spinal cord, connectivity specifies functional differences between premotor interneurons. In the olfactory bulb, tuning-dependent connectivity implements a whitening transformation that is based on the selective suppression of responses to overrepresented stimulus features. These findings illustrate the potential of dynamical connectomics in zebrafish to analyze the circuit mechanisms underlying higher-order neuronal computations.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据