Abstract
New study results on artificial intelligence have been published. According to news reporting out of the Daegu Gyeongbuk Institute of Science and Technology (DGIST) by NewsRx editors, research stated, “Recording neuronal activity using multiple electrodes has been widely used to understand the functional mechanisms of the brain.” The news reporters obtained a quote from the research from Daegu Gyeongbuk Institute of Science and Technology (DGIST): “Increasing the number of electrodes allows us to decode more variety of functionalities. However, handling massive amounts of multichannel electrophysiological data is still challenging due to the limited hardware resources and unavoidable thermal tissue damage. Here, we present machine learning (ML)-based reconstruction of high-frequency neuronal spikes from subsampled low-frequency band signals. Inspired by the equivalence between high-frequency restoration and super-resolution in image processing, we applied a transformer ML model to neuronal data recorded from both in vitro cultures and in vivo male mouse brains. Even with the x8 downsampled datasets, our trained model reasonably estimated high-frequency information of spiking activity, including spike timing, waveform, and network connectivity.”
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