Journal of Computational Neuroscience

, Volume 36, Issue 3, pp 321–337

On parsing the neural code in the prefrontal cortex of primates using principal dynamic modes

Authors

    • Department of Biomedical Engineering and the Biomedical Simulations Resource (BMSR)University of Southern California
  • D. C. Shin
    • Department of Biomedical Engineering and the Biomedical Simulations Resource (BMSR)University of Southern California
  • D. Song
    • Department of Biomedical Engineering, Program in Neuroscience, Center for Neural EngineeringUniversity of Southern California
  • R. E. Hampson
    • Department of Physiology and PharmacologyWake Forest University Health Sciences
  • S. A. Deadwyler
    • Department of Physiology and PharmacologyWake Forest University Health Sciences
  • T. W. Berger
    • Department of Biomedical Engineering, Program in Neuroscience, Center for Neural EngineeringUniversity of Southern California
Article

DOI: 10.1007/s10827-013-0475-3

Cite this article as:
Marmarelis, V.Z., Shin, D.C., Song, D. et al. J Comput Neurosci (2014) 36: 321. doi:10.1007/s10827-013-0475-3

Abstract

Nonlinear modeling of multi-input multi-output (MIMO) neuronal systems using Principal Dynamic Modes (PDMs) provides a novel method for analyzing the functional connectivity between neuronal groups. This paper presents the PDM-based modeling methodology and initial results from actual multi-unit recordings in the prefrontal cortex of non-human primates. We used the PDMs to analyze the dynamic transformations of spike train activity from Layer 2 (input) to Layer 5 (output) of the prefrontal cortex in primates performing a Delayed-Match-to-Sample task. The PDM-based models reduce the complexity of representing large-scale neural MIMO systems that involve large numbers of neurons, and also offer the prospect of improved biological/physiological interpretation of the obtained models. PDM analysis of neuronal connectivity in this system revealed “input–output channels of communication” corresponding to specific bands of neural rhythms that quantify the relative importance of these frequency-specific PDMs across a variety of different tasks. We found that behavioral performance during the Delayed-Match-to-Sample task (correct vs. incorrect outcome) was associated with differential activation of frequency-specific PDMs in the prefrontal cortex.

Keyword

Multi-input/multi-output (MIMO) neuronal systemsPrefrontal cortexDynamic nonlinear modelingPrincipal dynamic modesModeling of neural systemsNeural codingVolterra-Wiener modeling

Copyright information

© Springer Science+Business Media New York 2013