ArticleNature communications2024
Machine learning-based high-frequency neuronal spike reconstruction from low-frequency and low-sampling-rate recordings.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Artificial intelligence-enabled "inherited noninvasive intracellular recording" for prolonged monitoring of cardiac action potentials.Science advances · 2026Article
- Article
- From microelectrode arrays to all-optical and multimodal neural interfaces: emerging platforms for spatiotemporal interrogation ofFrontiers in synaptic neuroscience · 2025Review
- Advances in brain-computer interface for decoding speech imagery from EEG signals: a systematic review.Cognitive neurodynamics · 2024Review
- Machine learning-based high-frequency neuronal spike reconstruction from low-frequency and low-sampling-rate recordings.Nature communications · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
Recording neuronal activity using multiple electrodes has been widely used to understand the functional mechanisms of the brain. 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. With our ML-based data reduction applicable to existing multichannel recording hardware while achieving neuronal signals of broad bandwidths, we expect to enable more comprehensive analysis and control of brain functions.
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Registered trials
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