LESH performance on data of various sparseness.

<p>Colored numbers show generalizability: accuracy averaged across all test repeat intervals (larger number in <b>bold</b>). To ensure stability of the results, models were independently optimized for 20 times and yielded 20 parameter sets for every combination of sparseness and re...

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Главный автор:	Viktoria Zemliak (19500335) (author)
Другие авторы:	Gordon Pipa (153913) (author), Pascal Nieters (14611325) (author)
Опубликовано:	2025
Предметы:	Neuroscience Biological Sciences not elsewhere classified Information Systems not elsewhere classified sparse coding observed previously encountered inputs network activity using lateral connectivity shaped connectivity structure supports approach outperforms lstm sparse input conditions recurrent connectivity without continual familiarity decoding recurrent connections input stimuli decoding strategy unsupervised spike spike trains spike synchrony spike count naturally encoded dependent plasticity
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LESH performance on data of various sparseness.

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