Sensitivity and specificity of a Bayesian single trial analysis for time varying neural signals
| Autor: | Jeff T. Mohl, Surya T. Tokdar, Jennifer M. Groh, Valeria C. Caruso |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0303 health sciences
business.industry Bayesian probability Single stimulus Pattern recognition Stimulus (physiology) Correct response Article Synthetic data 03 medical and health sciences Neural activity 0302 clinical medicine Categorization Quantitative Biology - Neurons and Cognition FOS: Biological sciences Neurons and Cognition (q-bio.NC) Artificial intelligence Single trial business 030217 neurology & neurosurgery 030304 developmental biology Mathematics |
| Zdroj: | Neuron Behav Data Anal Theory |
| DOI: | 10.1101/690958 |
| Popis: | We recently reported the existence of fluctuations in neural signals that may permit neurons to code multiple simultaneous stimuli sequentially across time. This required deploying a novel statistical approach to permit investigation of neural activity at the scale of individual trials. Here we present tests using synthetic data to assess the sensitivity and specificity of this analysis. We fabricated datasets to match each of several potential response patterns derived from single-stimulus response distributions. In particular, we simulated dual stimulus trial spike counts that reflected fluctuating mixtures of the single stimulus spike counts, stable intermediate averages, single stimulus winner-take-all, or response distributions that were outside the range defined by the single stimulus responses (such as summation or suppression). We then assessed how well the analysis recovered the correct response pattern as a function of the number of simulated trials and the difference between the simulated responses to each "stimulus" alone. We found excellent recovery of the mixture, intermediate, and outside categories (>97% percent correct), and good recovery of the single/winner-take-all category (>90% correct) when the number of trials was >20 and the single-stimulus response rates were 50Hz and 20Hz respectively. Both larger numbers of trials and greater separation between the single stimulus firing rates improved categorization accuracy. These results provide a benchmark, and guidelines for data collection, for use of this method to investigate coding of multiple items at the individual-trial time scale. Accepted for publication in Neurons, Behavior, Data analysis, and Theory |
| Databáze: | OpenAIRE |
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