Spike separation based on symmetries analysis in phase space

The present study introduces an approach for automatic classification of extracellularly recorded action potentials of neurons based on geometrical approach. Neuronal spikes are considered as geometrical objects, namely trajectories in phase space. It is shown that for spikes, generated by the same...

Повний опис

Збережено в:
Бібліографічні деталі
Дата:2003
Автори: Polyarush, A.I., Makarenko, A.S., Tetko, I.V.
Формат: Стаття
Мова:English
Опубліковано: Навчально-науковий комплекс "Інститут прикладного системного аналізу" НТУУ "КПІ" МОН та НАН України 2003
Назва видання:Системні дослідження та інформаційні технології
Теми:
Онлайн доступ:http://dspace.nbuv.gov.ua/handle/123456789/50276
Теги: Додати тег
Немає тегів, Будьте першим, хто поставить тег для цього запису!
Назва журналу:Digital Library of Periodicals of National Academy of Sciences of Ukraine
Цитувати:Spike separation based on symmetries analysis in phase space / A.I. Polyarush, A.S. Makarenko, I.V. Tetko // Систем. дослідж. та інформ. технології. — 2003. — № 2. — С. 124-135. — Бібліогр.: 9 назв. — англ.

Репозитарії

Digital Library of Periodicals of National Academy of Sciences of Ukraine
Опис
Резюме:The present study introduces an approach for automatic classification of extracellularly recorded action potentials of neurons based on geometrical approach. Neuronal spikes are considered as geometrical objects, namely trajectories in phase space. It is shown that for spikes, generated by the same neuron, it is possible to find such a symmetry transformation under which their trajectories are invariant in phase space. On the other hand, the phase trajectories of spikes generated by other neurons change significantly under the action of that transformation. Thus, it is possible to define a special symmetry transformation that only typifies the spikes of the given neuron. The proposed algorithm is explained and an overview of the mathematical background is given. The method was tested on simulated data and showed good results in real experiments.