ArticleFrontiers in neuroscience2019
Neural Field Theory of Corticothalamic Attention With Control System Analysis.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Unified theory of alpha, mu, and tau rhythms via eigenmodes of brain activity.Frontiers in computational neuroscience · 2024Article
- Neural field theory of adaptive effects on auditory evoked responses and mismatch negativity in multifrequency stimulus sequences.Frontiers in human neuroscience · 2023Article
- Modal-Polar Representation of Evoked Response Potentials in Multiple Arousal States.Frontiers in human neuroscience · 2021Article
- Determination of Dynamic Brain Connectivity via Spectral Analysis.Frontiers in human neuroscience · 2021Article
- Neural Field Theory of Evoked Response Sequences and Mismatch Negativity With Adaptation.Frontiers in human neuroscience · 2021Article
- Neural Field Theory of Evoked Response Potentials With Attentional Gain Dynamics.Frontiers in human neuroscience · 2020Article
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Authors and funding
2 authors.
Funding
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Abstract
Neural field theory is used to analyze attention by extending an existing model of the large-scale activity in the corticothalamic system to incorporate local feedbacks that modulate the gains of neural connectivity as part of the response to incoming stimuli. Treatment of both activity changes and connectivity changes as part of a generalized response enables generalized linear transfer functions of the combined response to be derived. These are then analyzed and interpreted via control theory in terms of stimulus-driven changes in system resonances that were recently shown to implement data filtering and prediction of the inputs. Using simple visual stimuli as a test case, it is shown that the gain response can implement attention by evaluating two main features of the stimuli: the magnitude and the rate of change, by increasing the weight placed on the rate of change in response to sudden changes, while reducing the contribution of stimuli value in tandem. These changes of filter parameters are shown to improve the prediction of the upcoming stimuli based on its recent time course. This outcome is analogous to controller-parameter tuning for performance enhancement in engineering control theory.
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