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Physical principles for scalable neural recording

Overview of attention for article published in Frontiers in Computational Neuroscience, January 2013
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#18 of 1,475)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
1 news outlet
blogs
5 blogs
twitter
93 X users
facebook
10 Facebook pages
googleplus
6 Google+ users
reddit
4 Redditors

Citations

dimensions_citation
227 Dimensions

Readers on

mendeley
747 Mendeley
citeulike
5 CiteULike
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Title
Physical principles for scalable neural recording
Published in
Frontiers in Computational Neuroscience, January 2013
DOI 10.3389/fncom.2013.00137
Pubmed ID
Authors

Adam H. Marblestone, Bradley M. Zamft, Yael G. Maguire, Mikhail G. Shapiro, Thaddeus R. Cybulski, Joshua I. Glaser, Dario Amodei, P. Benjamin Stranges, Reza Kalhor, David A. Dalrymple, Dongjin Seo, Elad Alon, Michel M. Maharbiz, Jose M. Carmena, Jan M. Rabaey, Edward S. Boyden, George M. Church, Konrad P. Kording

Abstract

Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience. Entirely new approaches may be required, motivating an analysis of the fundamental physical constraints on the problem. We outline the physical principles governing brain activity mapping using optical, electrical, magnetic resonance, and molecular modalities of neural recording. Focusing on the mouse brain, we analyze the scalability of each method, concentrating on the limitations imposed by spatiotemporal resolution, energy dissipation, and volume displacement. Based on this analysis, all existing approaches require orders of magnitude improvement in key parameters. Electrical recording is limited by the low multiplexing capacity of electrodes and their lack of intrinsic spatial resolution, optical methods are constrained by the scattering of visible light in brain tissue, magnetic resonance is hindered by the diffusion and relaxation timescales of water protons, and the implementation of molecular recording is complicated by the stochastic kinetics of enzymes. Understanding the physical limits of brain activity mapping may provide insight into opportunities for novel solutions. For example, unconventional methods for delivering electrodes may enable unprecedented numbers of recording sites, embedded optical devices could allow optical detectors to be placed within a few scattering lengths of the measured neurons, and new classes of molecularly engineered sensors might obviate cumbersome hardware architectures. We also study the physics of powering and communicating with microscale devices embedded in brain tissue and find that, while radio-frequency electromagnetic data transmission suffers from a severe power-bandwidth tradeoff, communication via infrared light or ultrasound may allow high data rates due to the possibility of spatial multiplexing. The use of embedded local recording and wireless data transmission would only be viable, however, given major improvements to the power efficiency of microelectronic devices.

X Demographics

X Demographics

The data shown below were collected from the profiles of 93 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 747 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 31 4%
Germany 6 <1%
United Kingdom 3 <1%
Switzerland 2 <1%
France 2 <1%
Portugal 2 <1%
Korea, Republic of 1 <1%
Ireland 1 <1%
Norway 1 <1%
Other 14 2%
Unknown 684 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 234 31%
Researcher 128 17%
Student > Bachelor 73 10%
Student > Master 72 10%
Professor > Associate Professor 38 5%
Other 111 15%
Unknown 91 12%
Readers by discipline Count As %
Engineering 216 29%
Agricultural and Biological Sciences 127 17%
Neuroscience 117 16%
Physics and Astronomy 57 8%
Computer Science 27 4%
Other 91 12%
Unknown 112 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 110. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 April 2024.
All research outputs
#388,223
of 25,728,350 outputs
Outputs from Frontiers in Computational Neuroscience
#18
of 1,475 outputs
Outputs of similar age
#2,596
of 290,910 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#3
of 138 outputs
Altmetric has tracked 25,728,350 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,475 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 290,910 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 138 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.