Comparison of feature-based and Uniform Manifold Approximation and Projection (UMAP)-based spike sorting for neuron classification.
<p><b>(A)</b> High-pass filtering (500 Hz–2 kHz) removes low-frequency noise from extracellular signals. <b>(B)</b> Thresholding the pronounced deflections in the filtered data identifies spikes. <b>(C)</b> Windows around each spike, centered on the trough,...
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| Beste egile batzuk: | , , , , , , , , , , |
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2025
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Etiketa erantsi
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| _version_ | 1849927641762627584 |
|---|---|
| author | Daniel Suárez-Barrera (22676654) |
| author2 | Lucas Bayones (22676657) Norberto Encinas-Rodríguez (22676660) Sergio Parra (22676663) Viktor Monroy (22676666) Sebastián Pujalte (22676669) Bernardo Andrade-Ortega (22676672) Héctor Díaz (22676675) Manuel Alvarez (3468647) Antonio Zainos (22676678) Alessio Franci (143351) Román Rossi-Pool (22676681) |
| author2_role | author author author author author author author author author author author |
| author_facet | Daniel Suárez-Barrera (22676654) Lucas Bayones (22676657) Norberto Encinas-Rodríguez (22676660) Sergio Parra (22676663) Viktor Monroy (22676666) Sebastián Pujalte (22676669) Bernardo Andrade-Ortega (22676672) Héctor Díaz (22676675) Manuel Alvarez (3468647) Antonio Zainos (22676678) Alessio Franci (143351) Román Rossi-Pool (22676681) |
| author_role | author |
| dc.creator.none.fl_str_mv | Daniel Suárez-Barrera (22676654) Lucas Bayones (22676657) Norberto Encinas-Rodríguez (22676660) Sergio Parra (22676663) Viktor Monroy (22676666) Sebastián Pujalte (22676669) Bernardo Andrade-Ortega (22676672) Héctor Díaz (22676675) Manuel Alvarez (3468647) Antonio Zainos (22676678) Alessio Franci (143351) Román Rossi-Pool (22676681) |
| dc.date.none.fl_str_mv | 2025-11-24T18:33:31Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pbio.3003527.g001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Comparison_of_feature-based_and_Uniform_Manifold_Approximation_and_Projection_UMAP_-based_spike_sorting_for_neuron_classification_/30697577 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Cell Biology Molecular Biology Neuroscience Physiology Biotechnology Developmental Biology Science Policy Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified spike sorting pipelines spike sorting pipeline seldom spiking neurons recorded putative neurons correctly sorted neurons clustering algorithms responsible spike sorting makes reliable spike sorting umap drastically increases data analysis techniques drastically improve processed data experimental data universal practice precise explorations neural recordings neural code mathematically grounded fundamentally motivated extracellular electrophysiology enables deeper crucial step computational cost ad hoc |
| dc.title.none.fl_str_mv | Comparison of feature-based and Uniform Manifold Approximation and Projection (UMAP)-based spike sorting for neuron classification. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p><b>(A)</b> High-pass filtering (500 Hz–2 kHz) removes low-frequency noise from extracellular signals. <b>(B)</b> Thresholding the pronounced deflections in the filtered data identifies spikes. <b>(C)</b> Windows around each spike, centered on the trough, are isolated to standardize waveform comparisons. <b>(D)</b> Each spike waveform becomes a point in a high-dimensional space (time vs. voltage). <b>(E)</b> Feature-based sorting relies on linear dimensionality reduction methods (e.g., principal component analysis, wavelets), yet can be thrown off by spikes from different neurons. <b>(F)</b> Clustering in this simplified feature space may misclassify neurons due to these linear constraints. <b>(G)</b> UMAP-based sorting, however, employs a nonlinear approach that preserves both local and global structure. <b>(H)</b> This UMAP projection keeps distinct clusters and neuron-specific features more intact. <b>(I)</b> Clustering in the UMAP-projected space better distinguishes individual neurons (shown as distinct colored clusters), reducing merging errors seen with feature-based methods.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_e6f405969393baa4edb7b3922e8a767b |
| identifier_str_mv | 10.1371/journal.pbio.3003527.g001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30697577 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Comparison of feature-based and Uniform Manifold Approximation and Projection (UMAP)-based spike sorting for neuron classification.Daniel Suárez-Barrera (22676654)Lucas Bayones (22676657)Norberto Encinas-Rodríguez (22676660)Sergio Parra (22676663)Viktor Monroy (22676666)Sebastián Pujalte (22676669)Bernardo Andrade-Ortega (22676672)Héctor Díaz (22676675)Manuel Alvarez (3468647)Antonio Zainos (22676678)Alessio Franci (143351)Román Rossi-Pool (22676681)Cell BiologyMolecular BiologyNeurosciencePhysiologyBiotechnologyDevelopmental BiologyScience PolicySpace ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedspike sorting pipelinesspike sorting pipelineseldom spiking neuronsrecorded putative neuronscorrectly sorted neuronsclustering algorithms responsiblespike sorting makesreliable spike sortingumap drastically increasesdata analysis techniquesdrastically improveprocessed dataexperimental datauniversal practiceprecise explorationsneural recordingsneural codemathematically groundedfundamentally motivatedextracellular electrophysiologyenables deepercrucial stepcomputational costad hoc<p><b>(A)</b> High-pass filtering (500 Hz–2 kHz) removes low-frequency noise from extracellular signals. <b>(B)</b> Thresholding the pronounced deflections in the filtered data identifies spikes. <b>(C)</b> Windows around each spike, centered on the trough, are isolated to standardize waveform comparisons. <b>(D)</b> Each spike waveform becomes a point in a high-dimensional space (time vs. voltage). <b>(E)</b> Feature-based sorting relies on linear dimensionality reduction methods (e.g., principal component analysis, wavelets), yet can be thrown off by spikes from different neurons. <b>(F)</b> Clustering in this simplified feature space may misclassify neurons due to these linear constraints. <b>(G)</b> UMAP-based sorting, however, employs a nonlinear approach that preserves both local and global structure. <b>(H)</b> This UMAP projection keeps distinct clusters and neuron-specific features more intact. <b>(I)</b> Clustering in the UMAP-projected space better distinguishes individual neurons (shown as distinct colored clusters), reducing merging errors seen with feature-based methods.</p>2025-11-24T18:33:31ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pbio.3003527.g001https://figshare.com/articles/figure/Comparison_of_feature-based_and_Uniform_Manifold_Approximation_and_Projection_UMAP_-based_spike_sorting_for_neuron_classification_/30697577CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306975772025-11-24T18:33:31Z |
| spellingShingle | Comparison of feature-based and Uniform Manifold Approximation and Projection (UMAP)-based spike sorting for neuron classification. Daniel Suárez-Barrera (22676654) Cell Biology Molecular Biology Neuroscience Physiology Biotechnology Developmental Biology Science Policy Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified spike sorting pipelines spike sorting pipeline seldom spiking neurons recorded putative neurons correctly sorted neurons clustering algorithms responsible spike sorting makes reliable spike sorting umap drastically increases data analysis techniques drastically improve processed data experimental data universal practice precise explorations neural recordings neural code mathematically grounded fundamentally motivated extracellular electrophysiology enables deeper crucial step computational cost ad hoc |
| status_str | publishedVersion |
| title | Comparison of feature-based and Uniform Manifold Approximation and Projection (UMAP)-based spike sorting for neuron classification. |
| title_full | Comparison of feature-based and Uniform Manifold Approximation and Projection (UMAP)-based spike sorting for neuron classification. |
| title_fullStr | Comparison of feature-based and Uniform Manifold Approximation and Projection (UMAP)-based spike sorting for neuron classification. |
| title_full_unstemmed | Comparison of feature-based and Uniform Manifold Approximation and Projection (UMAP)-based spike sorting for neuron classification. |
| title_short | Comparison of feature-based and Uniform Manifold Approximation and Projection (UMAP)-based spike sorting for neuron classification. |
| title_sort | Comparison of feature-based and Uniform Manifold Approximation and Projection (UMAP)-based spike sorting for neuron classification. |
| topic | Cell Biology Molecular Biology Neuroscience Physiology Biotechnology Developmental Biology Science Policy Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified spike sorting pipelines spike sorting pipeline seldom spiking neurons recorded putative neurons correctly sorted neurons clustering algorithms responsible spike sorting makes reliable spike sorting umap drastically increases data analysis techniques drastically improve processed data experimental data universal practice precise explorations neural recordings neural code mathematically grounded fundamentally motivated extracellular electrophysiology enables deeper crucial step computational cost ad hoc |