Publicação
Synthetic data for robust identification of typical and atypical serotonergic neurons using convolutional neural networks
| datacite.subject.sdg | 03:Saúde de Qualidade | |
| datacite.subject.sdg | 09:Indústria, Inovação e Infraestruturas | |
| datacite.subject.sdg | 04:Educação de Qualidade | |
| dc.contributor.author | Corradetti, Daniele | |
| dc.contributor.author | Bernardi, Alessandro | |
| dc.contributor.author | Corradetti, Renato | |
| dc.date.accessioned | 2026-05-29T09:22:03Z | |
| dc.date.available | 2026-05-29T09:22:03Z | |
| dc.date.issued | 2024-11-16 | |
| dc.description.abstract | Serotonergic neurons in the raphe nuclei exhibit diverse electrophysiological properties and functional roles, yet conventional identification methods rely on restrictive criteria that likely overlook atypical serotonergic cells. The use of convolutional neural network (CNN) for comprehensive classification of both typical and atypical serotonergic neurons is an interesting one, but the key challenge is often given by the limited experimental data available for training. This study presents a procedure for synthetic data generation that combines smoothed spike waveforms with heterogeneous noise masks from real recordings. This approach expanded the training set while mitigating overfitting of background noise signatures. CNN models trained on the augmented dataset achieved high accuracy (96.2% true positive rate, 88.8% true negative rate) on non-homogeneous test data collected under different experimental conditions than the training, validation and testing data. | eng |
| dc.identifier.doi | 10.1007/978-3-031-73500-4_1 | |
| dc.identifier.isbn | 9783031734991 | |
| dc.identifier.isbn | 9783031735004 | |
| dc.identifier.issn | 0302-9743 | |
| dc.identifier.issn | 1611-3349 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/29054 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Springer | |
| dc.relation.ispartof | Lecture Notes in Computer Science | |
| dc.relation.ispartof | Progress in Artificial Intelligence | |
| dc.rights.uri | N/A | |
| dc.subject | Deep learning Models | |
| dc.subject | Serotonergic neurons | |
| dc.subject | Convolutional neural networks | |
| dc.subject | Synthetic data | |
| dc.subject | Spike recognition | |
| dc.title | Synthetic data for robust identification of typical and atypical serotonergic neurons using convolutional neural networks | eng |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 15 | |
| oaire.citation.startPage | 3 | |
| oaire.citation.title | Lecture Notes in Computer Science | |
| oaire.citation.volume | 14968 | |
| oaire.version | http://purl.org/coar/version/c_b1a7d7d4d402bcce | |
| person.familyName | Corradetti | |
| person.givenName | Daniele | |
| person.identifier.orcid | 0000-0001-8086-0593 | |
| relation.isAuthorOfPublication | eb033bd7-b864-44d6-8c79-e64b25bb2b6e | |
| relation.isAuthorOfPublication.latestForDiscovery | eb033bd7-b864-44d6-8c79-e64b25bb2b6e |