An artificial neural network for automated behavioral state classification in rats.

ABSTRACT: Accurate behavioral state classification is critical for many research applications. Researchers typically rely upon manual identification of behavioral state through visual inspection of electrophysiological signals, but this approach is time intensive and subject to low inter-rater reliability. To overcome these limitations, a diverse set of algorithmic approaches have been put forth to automate the classification process. Recently, novel machine learning approaches have been detailed that produce rapid and highly accurate classifications. These approaches however, are often computationally expensive, require significant expertise to implement, and/or require proprietary software that limits broader adoption. Here we detail a novel artificial neural network that uses electrophysiological

SUBMITTER: Ellen JG 

PROVIDER: S-EPMC8435206 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature