Measuring the signal-to-noise ratio of a neuron
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1059
- Type
- D - Journal article
- DOI
-
10.1073/pnas.1505545112
- Title of journal
- PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Article number
- -
- First page
- 7141
- Volume
- 112
- Issue
- 23
- ISSN
- 0027-8424
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
9
- Research group(s)
-
-
- Citation count
- 18
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Through research funded by Liverpool Hospital (salary to Czanner), US National Institutes of Health and a Pioneer Award (to Brown), this work, for the first time, proposes SNR in biological systems based on Kullback-Leibler divergence. Most researchers use sum-of-squares SNR that assumes normal distribution and independence on confounders. In collaboration with Harvard and MIT, this work brought a principled theoretical approach to derive SNR for non-normal data, linking it to key concepts of statistics and information theory, validation in simulated data and real neural data from four experiments. This led to an MIT-funded, invited talk at the ENB symposium 2017.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
