Bayesian prediction of psychophysical detection responses from spike activity in the rat sensorimotor cortex

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dc.authoridGuclu, Burak/0000-0002-7757-5764
dc.authoridÖztürk, Sevgi/0000-0002-5148-416X
dc.authoridDevecioglu, İsmail/0000-0003-4119-617X
dc.contributor.authorÖztürk, Sevgi
dc.contributor.authorDevecioğlu, İsmail
dc.contributor.authorGüçlü, Burak
dc.date.accessioned2023-05-06T17:19:37Z
dc.date.available2023-05-06T17:19:37Z
dc.date.issued2023
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümü
dc.description.abstractDecoding of sensorimotor information is essential for brain-computer interfaces (BCIs) as well as in normal functioning organisms. In this study, Bayesian models were developed for the prediction of binary decisions of 10 awake freely-moving male/female rats based on neural activity in a vibrotactile yes/no detection task. The vibrotactile stimuli were 40-Hz sinusoidal displacements (amplitude: 200 mu m, duration: 0.5 s) applied on the glabrous skin. The task was to depress the right lever for stimulus detection and left lever for stimulus-off condition. Spike activity was recorded from 16-channel microwire arrays implanted in the hindlimb representation of primary somatosensory cortex (S1), overlapping also with the associated representation in the primary motor cortex (M1). Single-/multi-unit average spike rate (R-d) within the stimulus analysis window was used as the predictor of the stimulus state and the behavioral response at each trial based on a Bayesian network model. Due to high neural and psychophysical response variability for each rat and also across subjects, mean R-d was not correlated with hit and false alarm rates. Despite the fluctuations in the neural data, the Bayesian model for each rat generated moderately good accuracy (0.60-0.90) and good class prediction scores (recall, precision, F1) and was also tested with subsets of data (e.g. regular vs. fast spike groups). It was generally observed that the models were better for rats with lower psychophysical performance (lower sensitivity index A'). This suggests that Bayesian inference and similar machine learning techniques may be especially helpful during the training phase of BCIs or for rehabilitation with neuroprostheses.
dc.description.sponsorshipTUEBITAK Grant [117F481]; European Union's FLAG-ERA JTC 2017 project GRAFIN; Bogazici University BAP [17XP2]
dc.description.sponsorshipThis study was supported by TUEBITAK Grant 117F481 within European Union's FLAG-ERA JTC 2017 project GRAFIN and Bogazici University BAP no: 17XP2 given to Dr. Gueclue. We thank Bige Vardar, Deniz Kilinc, Utku Zeki Ortal for their help with experiments, and to Dr. Sinan Yildirim for his clear explanations about Bayesian estimation.
dc.identifier.doi10.1007/s10827-023-00844-0
dc.identifier.issn0929-5313
dc.identifier.issn1573-6873
dc.identifier.pmid36696073
dc.identifier.scopus2-s2.0-85146840067
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.1007/s10827-023-00844-0
dc.identifier.urihttps://hdl.handle.net/20.500.11776/11893
dc.identifier.wosWOS:000920595400001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorDevecioğlu, İsmail
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal Of Computational Neuroscience
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectSomatosensory cortex
dc.subjectVibrotactile
dc.subjectSpike activity
dc.subjectPsychophysics
dc.subjectBayesian
dc.subjectSensorimotor
dc.subjectRat
dc.subjectBCI
dc.subjectNeuroprosthesis
dc.subjectNeuronal-Activity
dc.subjectMotor Cortex
dc.subjectRepresentation
dc.subjectDiscrimination
dc.subjectInformation
dc.subjectFrequency
dc.subjectPatterns
dc.subjectBehavior
dc.subjectTouch
dc.titleBayesian prediction of psychophysical detection responses from spike activity in the rat sensorimotor cortex
dc.typeArticle

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