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dc.contributor.authorHagen, Espen
dc.contributor.authorChambers, Anna
dc.contributor.authorEinevoll, Gaute
dc.contributor.authorPettersen, Klas Henning
dc.contributor.authorEnger, Rune
dc.contributor.authorStasik, Alexander Johannes
dc.date.accessioned2022-01-18T09:42:49Z
dc.date.available2022-01-18T09:42:49Z
dc.date.created2021-01-14T10:18:47Z
dc.date.issued2021
dc.identifier.citationNeuroinformatics. 2021, 19 493-514.
dc.identifier.issn1539-2791
dc.identifier.urihttps://hdl.handle.net/11250/2837853
dc.description.abstractHippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain functions such as memory consolidation and decision making. Understanding their underlying mechanisms in healthy and pathological brain function and behaviour rely on accurate SPW-R detection. In this multidisciplinary study, we propose a novel, self-improving artificial intelligence (AI) detection method in the form of deep Recurrent Neural Networks (RNN) with Long Short-Term memory (LSTM) layers that can learn features of SPW-R events from raw, labeled input data. The approach contrasts conventional routines that typically relies on hand-crafted, heuristic feature extraction and often laborious manual curation. The algorithm is trained using supervised learning on hand-curated data sets with SPW-R events obtained under controlled conditions. The input to the algorithm is the local field potential (LFP), the low-frequency part of extracellularly recorded electric potentials from the CA1 region of the hippocampus. Its output predictions can be interpreted as time-varying probabilities of SPW-R events for the duration of the inputs. A simple thresholding applied to the output probabilities is found to identify times of SPW-R events with high precision. The non-causal, or bidirectional variant of the proposed algorithm demonstrates consistently better accuracy compared to the causal, or unidirectional counterpart. Reference implementations of the algorithm, named ‘RippleNet’, are open source, freely available, and implemented using a common open-source framework for neural networks (tensorflow.keras) and can be easily incorporated into existing data analysis workflows for processing experimental data.
dc.language.isoeng
dc.subjectKunstig intelligens
dc.subjectArtificial intelligence
dc.subjectMaskinlæring
dc.subjectMachine learning
dc.subjectHippocampus
dc.subjectHippocampus
dc.subjectNevrovitenskap
dc.subjectNeuroscience
dc.titleRippleNet: a Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionpublishedVersion
dc.description.versionpublishedVersion
dc.subject.nsiVDP::Simulering, visualisering, signalbehandling, bildeanalyse: 429
dc.subject.nsiVDP::Simulation, visualisation, signal processing, image analysis: 429
dc.source.pagenumber493-514
dc.source.volume19
dc.source.journalNeuroinformatics
dc.identifier.doi10.1007/s12021-020-09496-2
dc.identifier.cristin1871142
dc.relation.projectNorges forskningsråd: 250128
dc.relation.projectNorges forskningsråd: 274328
dc.relation.projectNorges forskningsråd: 300504
dc.relation.projectNorges forskningsråd: 249988
dc.relation.projectNotur/NorStore: NS9021K
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.fulltextoriginal
cristin.qualitycode1


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