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Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience.
Current Opinion in Neurobiology ( IF 5.7 ) Pub Date : 2018-05-09 , DOI: 10.1016/j.conb.2018.04.007
L Paninski 1 , J P Cunningham 2
Affiliation  

Modern large-scale multineuronal recording methodologies, including multielectrode arrays, calcium imaging, and optogenetic techniques, produce single-neuron resolution data of a magnitude and precision that were the realm of science fiction twenty years ago. The major bottlenecks in systems and circuit neuroscience no longer lie in simply collecting data from large neural populations, but also in understanding this data: developing novel scientific questions, with corresponding analysis techniques and experimental designs to fully harness these new capabilities and meaningfully interrogate these questions. Advances in methods for signal processing, network analysis, dimensionality reduction, and optimal control-developed in lockstep with advances in experimental neurotechnology-promise major breakthroughs in multiple fundamental neuroscience problems. These trends are clear in a broad array of subfields of modern neuroscience; this review focuses on recent advances in methods for analyzing neural time-series data with single-neuronal precision.

中文翻译:

神经数据科学:加速大规模神经科学中的实验分析理论循环。

现代大规模多神经元记录方法,包括多电极阵列,钙成像和光遗传学技术,产生了数量级和精度高的单神经元分辨率数据,这是二十年前科幻小说的领域。系统和电路神经科学的主要瓶颈不再在于简单地从大量神经种群中收集数据,还在于理解这些数据:开发新颖的科学问题,并采用相应的分析技术和实验设计来充分利用这些新功能并有意义地审问这些问题。信号处理,网络分析,降维,实验神经技术的进步与时俱进地开发了最佳控制方法,这有望在多个基本神经科学问题中取得重大突破。在现代神经科学的众多子领域中,这些趋势显而易见。这篇综述着重于以单神经元精度分析神经时间序列数据的方法的最新进展。
更新日期:2018-05-29
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