ReviewCurrent opinion in neurobiology2019
Continuing progress of spike sorting in the era of big data.
Review in Current opinion in neurobiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Efficient and reproducible pipelines for spike sorting large-scale electrophysiology data.eLife · 2026Article
- A semi-automated MEA spike sorting method for high-throughput assessment of cultured neurons.Stem cell reports · 2026Article
- Advanced Brain-on-a-Chip for Wetware Computing: A Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Article
- AECuration: automated event curation for spike sorting.Journal of neural engineering · 2025Article
- PseudoSorter: A self-supervised spike sorting approach applied to reveal Tau-induced reductions in neuronal activity.Science advances · 2025Article
- Revealing single-neuron and network-activity interaction by combining high-density microelectrode array and optogenetics.Nature communications · 2024Article
- Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signaling in a Model of Niemann-Pick Disease Type C.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- A fully automatic multichannel neural spike sorting algorithm with spike reduction and positional feature.Journal of neural engineering · 2024Article
- Multi-day neuron tracking in high-density electrophysiology recordings using earth mover's distance.eLife · 2024Article
- Multi-day Neuron Tracking in High Density Electrophysiology Recordings using EMD.bioRxiv : the preprint server for biology · 2024Article
- The technology, opportunities, and challenges of Synthetic Biological Intelligence.Biotechnology advances · 2023Review
- Article
- Post-explant profiling of subcellular-scale carbon fiber intracortical electrodes and surrounding neurons enables modeling of recorded electrophysiology.Journal of neural engineering · 2023Article
- From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings.Frontiers in neuroinformatics · 2022Review
- Review
- Evaluation and resolution of many challenges of neural spike sorting: a new sorter.Journal of neurophysiology · 2021Article
- Ultrastructural Analysis of Neuroimplant-Parenchyma Interfaces Uncover Remarkable Neuroregeneration Along-With Barriers That Limit the Implant Electrophysiological Functions.Frontiers in neuroscience · 2021Article
- SHYBRID: A Graphical Tool for Generating Hybrid Ground-Truth Spiking Data for Evaluating Spike Sorting Performance.Neuroinformatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Engineering efforts are currently attempting to build devices capable of collecting neural activity from one million neurons in the brain. Part of this effort focuses on developing dense multiple-electrode arrays, which require post-processing via 'spike sorting' to extract neural spike trains from the raw signal. Gathering information at this scale will facilitate fascinating science, but these dreams are only realizable if the spike sorting procedure and data pipeline are computationally scalable, at or superior to hand processing, and scientifically reproducible. These challenges are all being amplified as the data scale continues to increase. In this review, recent efforts to attack these challenges are discussed, which have primarily focused on increasing accuracy and reliability while being computationally scalable. These goals are addressed by adding additional stages to the data processing pipeline and using divide-and-conquer algorithmic approaches. These recent developments should prove useful to most research groups regardless of data scale, not just for cutting-edge devices.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.