Estimating Information Transmission Time Between Prefrontal Cortex and Striatum by Transfer Entropy
Transmission timing is an important dynamic parameter to characterize two coupled systems between which information are exchanged. We used an information-theoretic approach known as transfer entropy to measure the information transmission delay times betw
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Estimating Information Transmission Time Between Prefrontal Cortex and Striatum by Transfer Entropy Kaidi Shao, Xiaochuan Pan and Rubin Wang
Abstract Transmission timing is an important dynamic parameter to characterize two coupled systems between which information are exchanged. We used an information-theoretic approach known as transfer entropy to measure the information transmission delay times between two simulated autoregressive processes with uni- and bi-directional quadric coupling. The estimated results agreed with the delay parameter settings. We also applied this method to estimate the delay time between the prefrontal cortex (PFC) and striatum based on local field potentials recorded simultaneously in the two areas of the monkey. The delay time from PFC to striatum was much shorter than that in the pathway from striatum to PFC, consistent with anatomical structure between the two areas. Our results demonstrated the capacity of transfer entropy in measuring information transfer delays. Keywords Transfer entropy
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Information flow Local field potential
Introduction
The brain network is a complex system composed of interacting subsystems. A fundamental question to understand the brain’s function is that which subsystems are activated and how they exchange information in cognitive operations. To answer for this question, properties of the dynamic information flow in the network, such as its direction, strength, and timing should be understood. Although it is K. Shao X. Pan (&) R. Wang Institute for Cognitive Neurodynamics, East China University of Science and Technology, P.O. Box 410, 130 Meilong Road, Shanghai 200237, People’s Republic of China e-mail: [email protected] K. Shao Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, People’s Republic of China © Springer Science+Business Media Singapore 2016 R. Wang and X. Pan (eds.), Advances in Cognitive Neurodynamics (V), Advances in Cognitive Neurodynamics, DOI 10.1007/978-981-10-0207-6_31
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difficult to observe such information flow directly, neural activities in different neural subsystems can be recorded through electrophysiological experiments, electroencephalography or magnetoencephalography. Theoretically, through analysis of how the recorded signals vary with time, network dynamic can be reconstructed indirectly. In this sense, the problem is how to estimate dynamic characteristics in coupled systems based on observed time series. In this paper, we introduced an approach based on an information-theoretical metric named as transfer entropy to estimate the information transfer delay time between two coupled systems. We tested the effectiveness of this method by estimating both uni-and bidirectional information transfer delay time between two simulated quadric-coupled autoregressive processes. We also applied this method to the measurement of an unknown information transfer delay time between the prefrontal cortex (PFC) and striatum in th
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