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 input objects have a given order and timing. This mechanism temporarily silences neurons following their recent spiking activity. Therefore, transitory objects act on different dynamically created feedforward sub-networks. ID-nets are demonstrated to reliably identify 10 handwritten d

SUBMITTER: Hodassman S 

PROVIDER: S-EPMC9523036 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature