ArticleJournal of neuroscience methods2012
How many neurons can we see with current spike sorting algorithms?
Article in Journal of neuroscience methods, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Modeling and simulation of neocortical micro- and mesocircuitry (Part II, Physiology and experimentation).eLife · 2026Article
- A Study of Deep Clustering in Spike Sorting.Neuroinformatics · 2025Article
- Non-Linear Modeling and Precision Analysis Approach for Implantable Multi-Channel Neural Recording Systems.Micromachines · 2025Article
- A Study of Non-Linear Manifold Feature Extraction in Spike Sorting.Neuroinformatics · 2025Article
- Advancing Spike Sorting Through Gradient-Based Preprocessing and Nonlinear Reduction With Agglomerative Clustering.Brain and behavior · 2025Article
- Unraveling the neural code: analysis of large-scale two-photon microscopy data.Microscopy (Oxford, England) · 2025Review
- Microelectrode Implantation in Human Insula: Technical Challenges and Recording Insights.Brain sciences · 2025Article
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- Comparative analysis of spike-sorters in large-scale brainstem recordings.bioRxiv : the preprint server for biology · 2024Article
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- Comparison of head direction cell firing characteristics across thalamo-parahippocampal circuitry.Hippocampus · 2024Article
- 68-channel neural signal processing system-on-chip with integrated feature extraction, compression, and hardware accelerators for neuroprosthetics in 22 nm FDSOI.Frontiers in neuroscience · 2024Article
- Selective peripheral nerve recording using simulated human median nerve activity and convolutional neural networks.Biomedical engineering online · 2023Article
- A Fast and Effective Spike Sorting Method Based on Multi-Frequency Composite Waveform Shapes.Brain sciences · 2023Article
- Post-explant profiling of subcellular-scale carbon fiber intracortical electrodes and surrounding neurons enables modeling of recorded electrophysiology.Journal of neural engineering · 2023Article
- Partially binarized neural networks for efficient spike sorting.Biomedical engineering letters · 2023Article
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- Improved space breakdown method - A robust clustering technique for spike sorting.Frontiers in computational neuroscience · 2023Article
- From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings.Frontiers in neuroinformatics · 2022Review
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Authors and funding
4 authors.
Funding
Abstract
Recent studies highlighted the disagreement between the typical number of neurons observed with extracellular recordings and the ones to be expected based on anatomical and physiological considerations. This disagreement has been mainly attributed to the presence of sparsely firing neurons. However, it is also possible that this is due to limitations of the spike sorting algorithms used to process the data. To address this issue, we used realistic simulations of extracellular recordings and found a relatively poor spike sorting performance for simulations containing a large number of neurons. In fact, the number of correctly identified neurons for single-channel recordings showed an asymptotic behavior saturating at about 8-10 units, when up to 20 units were present in the data. This performance was significantly poorer for neurons with low firing rates, as these units were twice more likely to be missed than the ones with high firing rates in simulations containing many neurons. These results uncover one of the main reasons for the relatively low number of neurons found in extracellular recording and also stress the importance of further developments of spike sorting algorithms.
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