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Published on: May 10, 2019
Neural mass model of human multisensory integration
Rosalyn J Moran1, Richard B Reilly
1Department of Electronic & Electrical Engineering, University College Dublin, Ireland. rosalyn.moran@ee.ucd.ie
Summary
A neural mass model simulated multisensory integration, showing it requires reduced top-down feedback delay. This suggests multisensory processing is controlled by executive functions, not automatic.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Cognitive Neuroscience
Background:
- Understanding multisensory integration is crucial for explaining complex cognitive functions.
- Evoked potentials (EPs) provide valuable insights into neural processing dynamics.
- Neural mass models offer a framework for simulating large-scale brain activity.
Purpose of the Study:
- To develop and validate a neural mass model capable of reproducing unisensory and multisensory evoked potentials.
- To investigate the neural mechanisms underlying multisensory integration during a reaction response task.
- To determine if multisensory integration is an automatic process or centrally controlled.
Main Methods:
- A neural mass model of interacting macro-columns was developed, incorporating excitatory and inhibitory feedback connections.
- The model was stimulated to reproduce auditory, visual, and audiovisual evoked potentials recorded from human patients.
- Model outputs were compared to actual evoked potentials, with architectural and parameter adjustments made for fitting.
Main Results:
- Both auditory and visually evoked potentials were best modeled using a top-down processing paradigm.
- Reconstructing multisensory responses from constituent unisensory models required a decrease in top-down feedback delay.
- No significant architectural changes were needed to fit the multisensory response, only parameter adjustments.
Conclusions:
- Multisensory integration, leading to improved reaction behavior, is not an automatic process.
- The findings suggest that multisensory integration is actively controlled by a central executive functioning.
- Neural mass modeling provides a powerful tool for dissecting the computational principles of brain function.
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