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

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Abstract

Decoding 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 µm, 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 (Rd) 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 Rd 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.

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Acknowledgements

This study was supported by TÜBİTAK Grant 117F481 within European Union’s FLAG-ERA JTC 2017 project GRAFIN and Boğaziçi University BAP no: 17XP2 given to Dr. Güçlü. We thank Bige Vardar, Deniz Kılınç, Utku Zeki Ortal for their help with experiments, and to Dr. Sinan Yıldırım for his clear explanations about Bayesian estimation.

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Authors and Affiliations

  1. Department of Neurobiology, Duke University Medical School, Durham, NC, 27710, USA

    Sevgi Öztürk

  2. Biomedical Engineering Department, Çorlu Engineering Faculty, Tekirdağ Namık Kemal University, Tekirdağ, 59860, Turkey

    İsmail Devecioğlu

  3. Institute of Biomedical Engineering, Boğaziçi University, Kandilli Campus, İstanbul, 34684, Turkey

    Burak Güçlü

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  1. Sevgi Öztürk
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Table S1

Prediction of right (R) and left (L) lever responses by the Bayesian model. For each rat, class prediction scores (recall (RE), precision (PR) and F1 score) are calculated based on true positive (TP), false negative (FN), false positive (FP) and true negative (TN) pooled counts. Class averaged scores and accuracies (ACC) are also given. Last three rows report, respectively, subject means, standard deviations, and performance scores for subject-pooled data. (DOCX 16.8 KB)

Table S2

Prediction of correct (C) and incorrect (I) responses by the Bayesian model based on subject-pooled data. Similar to Table S1, class prediction scores are given as recall (RE), precision (PR) and F1 score. Class-averaged scores and accuracies (ACC) are also given. (DOCX 16.9 KB)

Table S3

Psychophysical performances of biological, simulated (R/L classifier model), and augmented (C/I classifier model) subjects. Probabilities of hits (pH), false alarms (pH), non-parametric sensitivity (A’) and bias (B’’) indices are given. The last two rows are subject means and standard deviations. (DOCX 15.7 KB)

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Öztürk, S., Devecioğlu, İ. & Güçlü, B. Bayesian prediction of psychophysical detection responses from spike activity in the rat sensorimotor cortex. J Comput Neurosci 51, 207–222 (2023). https://doi.org/10.1007/s10827-023-00844-0

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