ArticleJournal of neurophysiology2020
A neural network for online spike classification that improves decoding accuracy.
Article in Journal of neurophysiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Compression as a neural mechanism of mnemonic chunking in prefrontal cortex.Current biology : CB · 2026Article
- Brain-wide arousal signals are segregated from movement planning in the superior colliculus of the macaque.eLife · 2026Article
- Interactions across hemispheres in prefrontal cortex reflect global cognitive processing.Nature communications · 2026Article
- Article
- A TALE OF TWO TAILS: PREFERRED AND ANTI-PREFERRED NATURAL STIMULI IN VISUAL CORTEX.... International Conference on Learning Representations · 2026Article
- Microelectrode arrays cultured with in vitro neural networks for motion control tasks: encoding and decoding progress and advances.Microsystems & nanoengineering · 2025Review
- A software platform for real-time and adaptive neuroscience experiments.Nature communications · 2025Article
- Efficient and reliable spike sorting from neural recordings with UMAP-based unsupervised nonlinear dimensionality reduction.PLoS biology · 2025Article
- Robustness of Working Memory to Prefrontal Cortex Microstimulation.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Interactions across hemispheres in prefrontal cortex reflect global cognitive processing.bioRxiv : the preprint server for biology · 2025Article
- Robustness of working memory to prefrontal cortex microstimulation.bioRxiv : the preprint server for biology · 2025Article
- Brain-computer Interaction in the Smart Era.Current medical science · 2024Review
- Recent Visual Experience Reshapes V4 Neuronal Activity and Improves Perceptual Performance.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Compact deep neural network models of visual cortex.bioRxiv : the preprint server for biology · 2023Article
- Enhancing the decoding accuracy of EEG signals by the introduction of anchored-STFT and adversarial data augmentation method.Scientific reports · 2022Article
- From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings.Frontiers in neuroinformatics · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Separating neural signals from noise can improve brain-computer interface performance and stability. However, most algorithms for separating neural action potentials from noise are not suitable for use in real time and have shown mixed effects on decoding performance. With the goal of removing noise that impedes online decoding, we sought to automate the intuition of human spike-sorters to operate in real time with an easily tunable parameter governing the stringency with which spike waveforms are classified. We trained an artificial neural network with one hidden layer on neural waveforms that were hand-labeled as either spikes or noise. The network output was a likelihood metric for each waveform it classified, and we tuned the network's stringency by varying the minimum likelihood value for a waveform to be considered a spike. Using the network's labels to exclude noise waveforms, we decoded remembered target location during a memory-guided saccade task from electrode arrays implanted in prefrontal cortex of rhesus macaque monkeys. The network classified waveforms in real time, and its classifications were qualitatively similar to those of a human spike-sorter. Compared with decoding with threshold crossings, in most sessions we improved decoding performance by removing waveforms with low spike likelihood values. Furthermore, decoding with our network's classifications became more beneficial as time since array implantation increased. Our classifier serves as a feasible preprocessing step, with little risk of harm, that could be applied to both off-line neural data analyses and online decoding.
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