Signal detection in extracellular neural ensemble recordings using higher criticism Article Swipe
Farzad Fathizadeh
,
Ekaterina Mitricheva
,
Rie Kimura
,
NK Logothetis
,
HR Noori
·
YOU?
·
· 2019
· Open Access
·
YOU?
·
· 2019
· Open Access
·
Information processing in the brain is conducted by a concerted action of multiple neural populations. Gaining insights in the organization and dynamics of such populations can best be studied with broadband intracranial recordings of so-called extracellular field potential, reflecting neuronal spiking as well as mesoscopic activities, such as waves, oscillations, intrinsic large deflections, and multiunit spiking activity. Such signals are critical for our understanding of how neuronal ensembles encode sensory information and how such information is integrated in the large networks underlying cognition. The aforementioned principles are now well accepted, yet the efficacy of extracting information out of the complex neural data, and their employment for improving our understanding of neural networks, critically depends on the mathematical processing steps ranging from simple detection of action potentials in noisy traces - to fitting advanced mathematical models to distinct patterns of the neural signal potentially underlying intra-processing of information, e.g. interneuronal interactions. Here, we present a robust strategy for detecting signals in broadband and noisy time series such as spikes, sharp waves and multi-unit activity data that is solely based on the intrinsic statistical distribution of the recorded data. By using so-called higher criticism - a second-level significance testing procedure comparing the fraction of observed significances to an expected fraction under the global null - we are able to detect small signals in correlated noisy time-series without prior filtering, denoising or data regression. Results demonstrate the efficiency and reliability of the method and versatility over a wide range of experimental conditions and suggest the appropriateness of higher criticism to characterize neuronal dynamics without prior manipulation of the data.
Related Topics
Concepts
Computer science
Artificial neural network
Artificial intelligence
Local field potential
Neural ensemble
Noise (video)
Signal processing
ENCODE
Information processing
Neural coding
Pattern recognition (psychology)
Machine learning
Neuroscience
Chemistry
Biology
Biochemistry
Image (mathematics)
Gene
Radar
Telecommunications
Metadata
- Type
- article
- Language
- en
- Landing Page
- http://hdl.handle.net/21.11116/0000-0003-C431-8
- http://hdl.handle.net/21.11116/0000-0003-C431-8
- OA Status
- green
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2959553343
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W2959553343Canonical identifier for this work in OpenAlex
- Title
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Signal detection in extracellular neural ensemble recordings using higher criticismWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-05-01Full publication date if available
- Authors
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Farzad Fathizadeh, Ekaterina Mitricheva, Rie Kimura, NK Logothetis, HR NooriList of authors in order
- Landing page
-
https://hdl.handle.net/21.11116/0000-0003-C431-8Publisher landing page
- PDF URL
-
https://hdl.handle.net/21.11116/0000-0003-C431-8Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://hdl.handle.net/21.11116/0000-0003-C431-8Direct OA link when available
- Concepts
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Computer science, Artificial neural network, Artificial intelligence, Local field potential, Neural ensemble, Noise (video), Signal processing, ENCODE, Information processing, Neural coding, Pattern recognition (psychology), Machine learning, Neuroscience, Chemistry, Biology, Biochemistry, Image (mathematics), Gene, Radar, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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20Other works algorithmically related by OpenAlex
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