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Encoding and Decoding Neuronal Dynamics: Methodological Framework to Uncover the Algorithms of Cognition

Abstract : A central challenge to cognitive neuroscience consists in decomposing complex brain signals into an interpretable sequence of operations-an algorithm-which ultimately accounts for intelligent behaviors. Over the past decades, a variety of analytical tools have been developed to (i) isolate each algorithmic step and (ii) track their ordering from neuronal activity. In the present chapter, we briefly review the main methods to encode and decode temporally-resolved neural recordings, show how these approaches relate to one-another, and summarize their main premises and challenges. Finally we highlight, through a series of recent findings, the increasing role of machine learning both as i) a method to extract convoluted patterns of neural activity, and as ii) an operational framework to formalize the computational bases of cognition. Overall, we discuss how modern analyses of neural time series can identify the algorithmic organization of cognition.
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Contributor : Jean-Rémi King Connect in order to contact the contributor
Submitted on : Tuesday, July 24, 2018 - 3:43:50 PM
Last modification on : Thursday, February 3, 2022 - 3:08:57 AM
Long-term archiving on: : Thursday, October 25, 2018 - 3:36:37 PM


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  • HAL Id : hal-01848442, version 1


Jean-Rémi King, Laura Gwilliams, Chris Holdgraf, Jona Sassenhagen, Alexandre Barachant, et al.. Encoding and Decoding Neuronal Dynamics: Methodological Framework to Uncover the Algorithms of Cognition. 2018. ⟨hal-01848442⟩



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