ArticleFrontiers in computational neuroscience2014
Spatial information in large-scale neural recordings.
Article in Frontiers in computational neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- OP-GLX: A MATLAB Toolbox for Online Processing and Plotting of Neuropixels Data Acquired with SpikeGLX.bioRxiv : the preprint server for biology · 2026Article
- CHIME: CMOS-HostedFrontiers in neuroscience · 2020Article
- Nucleotide-time alignment for molecular recorders.PLoS computational biology · 2017Article
- Spatial resolution dependence on spectral frequency in human speech cortex electrocorticography.Journal of neural engineering · 2016Article
- High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels.Lab on a chip · 2015Article
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Authors and funding
7 authors.
Funding
Abstract
To record from a given neuron, a recording technology must be able to separate the activity of that neuron from the activity of its neighbors. Here, we develop a Fisher information based framework to determine the conditions under which this is feasible for a given technology. This framework combines measurable point spread functions with measurable noise distributions to produce theoretical bounds on the precision with which a recording technology can localize neural activities. If there is sufficient information to uniquely localize neural activities, then a technology will, from an information theoretic perspective, be able to record from these neurons. We (1) describe this framework, and (2) demonstrate its application in model experiments. This method generalizes to many recording devices that resolve objects in space and should be useful in the design of next-generation scalable neural recording systems.
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Registered trials
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