Estimating a dynamic state to relate neural spiking activity to behavioral signals during cognitive tasks

Xinyi Deng, Rose T. Faghih, Riccardo Barbieri, Angelique C. Paulk, Wael F. Asaad, Emery N. Brown, Darin D. Dougherty, Alik S. Widge, Emad N. Eskandar, Uri T. Eden

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    An important question in neuroscience is understanding the relationship between high-dimensional electrophysiological data and complex, dynamic behavioral data. One general strategy to address this problem is to define a low-dimensional representation of essential cognitive features describing this relationship. Here we describe a general state-space method to model and fit a low-dimensional cognitive state process that allows us to relate behavioral outcomes of various tasks to simultaneously recorded neural activity across multiple brain areas. In particular, we apply this model to data recorded in the lateral prefrontal cortex (PFC) and caudate nucleus of non-human primates as they perform learning and adaptation in a rule-switching task. First, we define a model for a cognitive state process related to learning, and estimate the progression of this learning state through the experiments. Next, we formulate a point process generalized linear model to relate the spiking activity of each PFC and caudate neuron to the stimated learning state. Then, we compute the posterior densities of the cognitive state using a recursive Bayesian decoding algorithm. We demonstrate that accurate decoding of a learning state is possible with a simple point process model of population spiking. Our analyses also allow us to compare decoding accuracy across neural populations in the PFC and caudate nucleus.

    Original languageEnglish (US)
    Title of host publication2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages7808-7813
    Number of pages6
    ISBN (Electronic)9781424492718
    DOIs
    StatePublished - Nov 4 2015
    Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy
    Duration: Aug 25 2015Aug 29 2015

    Publication series

    NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
    Volume2015-November
    ISSN (Print)1557-170X

    Conference

    Conference37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
    Country/TerritoryItaly
    CityMilan
    Period8/25/158/29/15

    ASJC Scopus subject areas

    • Signal Processing
    • Biomedical Engineering
    • Computer Vision and Pattern Recognition
    • Health Informatics

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