Сибирский федеральный университет

Тип публикации: статья из журнала

Год издания: 2022

Идентификатор DOI: 10.1134/S001249662201001X

Ключевые слова: classification of neural activity patterns, delayed match-to-sample test, dynamic coding, neural activity

Аннотация: Abstract: The paper reports the assessment of the possibility to recover information obtained using an artificial neural network via inspecting neural activity patterns. A simple recurrent neural network forms dynamic excitation patterns for storing data on input stimulus in the course of the advanced delayed match to sample test with varying duration of pause between the received stimuli. Information stored in these patterns can be used by the neural network at any moment within the specified interval (three to six clock cycles), whereby it appears possible to detect invariant representation of received stimulus. To identify these representations, the neural network-based decoding method that shows 100% efficiency of received stimuli recognition has been suggested. This method allows for identification the minimum subset of neurons, the excitation pattern of which contains comprehensive information about the stimulus received by the neural network. © 2022, The Author(s).

Журнал: Doklady Biological Sciences

Выпуск журнала: Vol. 502, Is. 1

• Bartsev S.I. (Institute of Biophysics, Siberian Branch, Russian Academy of Sciences, Krasnoyarsk, 660036, Russian Federation, Siberian Federal University, Krasnoyarsk, 660041, Russian Federation)
• Baturina P.M. (Siberian Federal University, Krasnoyarsk, 660041, Russian Federation)
• Markova G.M. (Siberian Federal University, Krasnoyarsk, 660041, Russian Federation)

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