title: Measuring the signal-to-noise ratio of a neuron.
abstract: The signal-to-noise ratio (SNR), a commonly used measure of fidelity in physical systems, is defined as the ratio of the squared amplitude or variance of a signal relative to the variance of the noise. This definition is not appropriate for neural systems in which spiking activity is more accurately represented as point processes. We show that the SNR estimates a ratio of expected prediction errors and extend the standard definition to one appropriate for single neurons by representing neural spiking activity using point process generalized linear models (PP-GLM). We estimate the prediction errors using the residual deviances from the PP-GLM fits. Because the deviance is an approximate χ(2) random variable, we compute a bias-corrected SNR estimate appropriate for single-neuron analysis and use the bootstrap to assess its uncertainty. In the analyses of four systems neuroscience experiments, we show that the SNRs are -10 dB to -3 dB for guinea pig auditory cortex neurons, -18 dB to -7 dB for rat thalamic neurons, -28 dB to -14 dB for monkey hippocampal neurons, and -29 dB to -20 dB for human subthalamic neurons. The new SNR definition makes explicit in the measure commonly used for physical systems the often-quoted observation that single neurons have low SNRs. The neuron's spiking history is frequently a more informative covariate for predicting spiking propensity than the applied stimulus. Our new SNR definition extends to any GLM system in which the factors modulating the response can be expressed as separate components of a likelihood function.
authors.affiliation: Department of Biostatistics and Department of Eye and Vision Science, Faculty of Health and Life Sciences, University of Liverpool, Liverpool L69 3GA, United Kingdom; czanner@liverpool.ac.uk enb@neurostat.mit.edu.
authors.forename: Gabriela
authors.initials: G
authors.lastname: Czanner
authors.affiliation: Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218;
authors.forename: Sridevi V
authors.initials: SV
authors.lastname: Sarma
authors.affiliation: Department of Brain and Cognitive Sciences and.
authors.forename: Demba
authors.initials: D
authors.lastname: Ba
authors.affiliation: Department of Mathematics and Statistics, Boston University, Boston, MA 02215;
authors.forename: Uri T
authors.initials: UT
authors.lastname: Eden
authors.affiliation: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China;
authors.forename: Wei
authors.initials: W
authors.lastname: Wu
authors.affiliation: Departments of Neurosurgery and.
authors.forename: Emad
authors.initials: E
authors.lastname: Eskandar
authors.affiliation: Department of Biomedical Engineering, Institute for Translational Neuroscience, University of Minnesota, Minneapolis, MN 55455;
authors.forename: Hubert H
authors.initials: HH
authors.lastname: Lim
authors.affiliation: Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129; and.
authors.forename: Simona
authors.initials: S
authors.lastname: Temereanca
authors.affiliation: Center for Neural Science, New York University, New York, NY 10003.
authors.forename: Wendy A
authors.initials: WA
authors.lastname: Suzuki
authors.affiliation: Department of Brain and Cognitive Sciences and Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139; Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114; czanner@liverpool.ac.uk enb@neurostat.mit.edu.
authors.forename: Emery N
authors.initials: EN
authors.lastname: Brown
mesh_terms: D000200:Action Potentials; D000818:Animals; D001303:Auditory Cortex / Q000166:cytology; D006168:Guinea Pigs; D016013:Likelihood Functions; D009474:Neurons / Q000502:physiology*; D059629:Signal-To-Noise Ratio
keywords: SNR; neuron; point processes; signal-to-noise ratio; simulation