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A Mixed-Filter Algorithm for Dynamically Tracking Learning from Multiple Behavioral and Neurophysiological Measures

Author(s)
Coleman, Todd Prentice; Yanike, Marianna; Suzuki, Wendy A.; Brown, Emery Neal
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Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
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Abstract
Learning is a dynamic process generally defined as a change in behavior as a result of experience. Behavioral performance is commonly measured with continuous variables (reaction times) as well as binary variables (correct/incorrect task execution). When neural activity is recorded at the same time as behavioral measures, an important question is the extent to which neural correlates can be associated with the changes in behavior. Recent work has combined subsets of the three aforementioned modalities to understand learning. In this work, we develop an analysis of learning within a state-space framework of simultaneously recorded continuous and binary performance measures along with neural spiking activity modeled as a point process. This chapter illustrates our approach in the analysis of a simulated learning experiment, and an actual learning experiment, in which a monkey rapidly learns new associations within a single session.
Date issued
2011-09
URI
https://hdl.handle.net/1721.1/126803
Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Journal
The dynamic brain : an exploration of neuronal variability and its functional significance
Publisher
Oxford University Press
Citation
Coleman, Todd P. et al. “A Mixed-Filter Algorithm for Dynamically Tracking Learning from Multiple Behavioral and Neurophysiological Measures.” The dynamic brain : an exploration of neuronal variability and its functional significance. Edited by Mingzhou Ding, Dennis L. Glanzman. Oxford University Press, 2011 © 2011 The Author(s)
Version: Author's final manuscript
ISBN
9780195393798

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