
Recognition of stimulus received by recurrent neural network
Author(s) -
С. И. Барцев,
G M Markova
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2094/3/032041
Subject(s) - stimulus (psychology) , artificial neural network , pattern recognition (psychology) , computer science , neural activity , artificial intelligence , centroid , speech recognition , time delay neural network , neuroscience , psychology , cognitive psychology
The study is concerned with the comparison of two methods for identification of stimulus received by artificial neural network using neural activity pattern that corresponds to the period of storing information about this stimulus in the working memory. We used simple recurrent neural networks learned to pass the delayed matching-to-sample test. Neural activity was detected at the period of pause between receiving stimuli. The analysis of neural excitation patterns showed that neural networks encoded variables that were relevant for the task during the delayed matching-to-sample test, and their activity patterns were dynamic. The method of centroids allowed identifying the type of the received stimuli with efficiency up to 75% while the method of neural network-based decoder showed 100% efficiency. In addition, this method was applied to determine the minimal set of neurons whose activity was the most significant for stimulus recognition.