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

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.