2022
DOI: 10.1038/s41598-022-20337-x
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Brain inspired neuronal silencing mechanism to enable reliable sequence identification

Abstract: Real-time sequence identification is a core use-case of artificial neural networks (ANNs), ranging from recognizing temporal events to identifying verification codes. Existing methods apply recurrent neural networks, which suffer from training difficulties; however, performing this function without feedback loops remains a challenge. Here, we present an experimental neuronal long-term plasticity mechanism for high-precision feedforward sequence identification networks (ID-nets) without feedback loops, wherein … Show more

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Cited by 2 publications
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“…4). Contrary to common knowledge, shallow feedforward brain-inspired architectures are not inferior, and they do not represent, as thought, an additional biological limitation 28 . They can achieve low error rates such as DL algorithms, even with significantly low computational complexity for complex classification tasks (Fig.…”
Section: Discussionmentioning
confidence: 88%
“…4). Contrary to common knowledge, shallow feedforward brain-inspired architectures are not inferior, and they do not represent, as thought, an additional biological limitation 28 . They can achieve low error rates such as DL algorithms, even with significantly low computational complexity for complex classification tasks (Fig.…”
Section: Discussionmentioning
confidence: 88%