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Modulating human brain responses via optimal natural image selection and synthetic image generation
Zijin Gu1, Keith Jamison2, Mert R Sabuncu1,2
1School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA.
Arxiv
|May 3, 2023
Summary
Researchers used artificial intelligence models to generate images that activate specific brain regions. This study reveals how synthetic images can precisely target visual processing areas and highlights individual differences in brain responses.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Cognitive Neuroscience
Background:
- Understanding how the brain processes visual information is a key goal in neuroscience.
- Computational encoding models map visual stimuli to neural responses, aiding in this understanding.
- Generating targeted visual stimuli allows for controlled exploration of brain activity.
Conclusions:
- AI-generated visual stimuli can effectively modulate macro-scale brain regions, demonstrating functional specialization within the human visual system.
- The NeuroGen framework enables data-driven generation of stimuli to probe and potentially control brain activity with high individual specificity.
- Synthetic images show potential for targeted activation, particularly in regions like aTLfaces, offering new avenues for neuroscience research.
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