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Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
09:49

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Published on: April 16, 2014

Top-down influences on local networks: basic theory with experimental implications.

Ramesh Srinivasan1, Samuel Thorpe, Paul L Nunez

  • 1Department of Cognitive Sciences, University of California Irvine, CA, USA ; Department of Biomedical Engineering, University of California Irvine, CA, USA ; Institute for Mathematical Behavioral Sciences, University of California Irvine, CA, USA.

Frontiers in Computational Neuroscience
|April 26, 2013
PubMed
Summary
This summary is machine-generated.

The brain's background state significantly influences neuronal responses and cognitive functions like attention. This study models how brain states modulate neural oscillations, impacting information processing and behavior.

Keywords:
ECoGEEGneural mass modelspopulation dynamicstop-down control

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Modeling the Functional Network for Spatial Navigation in the Human Brain
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05:55

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Published on: October 13, 2023

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Neuroscience

Background:

  • Cortical neuron responses are influenced by receptive fields, arousal, attention, and cognitive biases.
  • Brain states dynamically affect neuronal excitability and modulate neural oscillations, such as gamma band activity by theta band rhythms.
  • Understanding top-down influences on neural networks is crucial for interpreting brain activity, especially with electroencephalography (EEG).

Purpose of the Study:

  • To develop a generic computational model of top-down influences on local cortical networks.
  • To investigate how dynamic background brain states affect neuronal population responses and oscillations.
  • To model the modulation of gamma rhythms by theta rhythms observed in human and animal studies.

Main Methods:

  • Utilized a modified Wilson-Cowan population model of excitatory and inhibitory neurons.
  • Incorporated explicit background state variables into the Wilson-Cowan equations for physiological realism.
  • Applied the model to simulate and reproduce cross-frequency interactions between theta and gamma oscillations.

Main Results:

  • The model demonstrated that the population response of neurons is strongly influenced by the background brain state.
  • The developed model successfully reproduced the observed modulation of gamma rhythms by theta rhythms.
  • The findings highlight the significant role of dynamic background states in neural processing.

Conclusions:

  • A dynamic background state is a key factor in top-down modulation of cortical networks.
  • The generic Wilson-Cowan model provides a framework for incorporating these effects into more detailed neural models.
  • This approach is suitable for analyzing EEG data and understanding cognitive influences on neural activity.