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2009 | OriginalPaper | Buchkapitel

Synchronized Rhythmic Signals Effectively Influence Ongoing Cortical Activity for Decision-Making: A Study of the Biological Plausible Neural Network Model

verfasst von : Hiroaki Wagatsuma, Yoko Yamaguchi

Erschienen in: Advances in Neuro-Information Processing

Verlag: Springer Berlin Heidelberg

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The brain is capable of parallel processing of different types of information in different brain regions. However, for higher cognitive functions such as decision-making, such regionally distributed information must interact together in the proper timing. The prefrontal cortex, the region which carries out decision-making, needs to be modulated by external signals to represent the current behavioral context in the hippocampus (HP). The question remains as to how the firing activity of the cortical neural network can be modulated by external signals in corporation with the ongoing activity. We hypothesized that rhythmic signals that attempt to synchronize the cortical ongoing activity minimize the disturbance and effectively enhance the activities of selective neurons. We investigated the level of the modulation by using a mutually connected neural network that consists of a neuron model with excitatory and refractory periods. The results demonstrated that cortical ongoing activities are weakly modulated by random external signals, while synchronized rhythmic signals, given as the pseudo HP signals, selectively enhance cortical activities. This suggests that the cortical ongoing activity is effectively influenced by the synchronized signals, which carry information in the proper timing of excitation. The investigation of neural synchronization dynamics is important to understanding how the brain realizes parallel processing in different sub-regions and to update immediately the internal representation even if the previous internal processing is ongoing.

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Metadaten
Titel
Synchronized Rhythmic Signals Effectively Influence Ongoing Cortical Activity for Decision-Making: A Study of the Biological Plausible Neural Network Model
verfasst von
Hiroaki Wagatsuma
Yoko Yamaguchi
Copyright-Jahr
2009
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-02490-0_105