Modifying convolutional neural networks enables untrained AI to mimic human brain activity, rivaling trained systems.
Key Details
- 1Johns Hopkins research shows biologically inspired AI architectures can simulate human brain patterns before training.
- 2Convolutional architectures, when tweaked, better align with patterns seen in human and primate brains than other models.
- 3Transformers and fully connected networks showed little change in brain-like activity when similarly modified.
- 4Untrained CNNs matched the brain-similarity of conventional trained AI, which usually require massive datasets.
- 5Findings published in Nature Machine Intelligence highlight the importance of architecture over traditional data-heavy training.
Why It Matters
This research challenges prevailing AI paradigms by emphasizing architectural design over large-scale data training, suggesting a pathway toward more efficient, brain-like AI systems. For radiology and imaging AI, biologically aligned models could improve interpretability, reduce resource requirements, and lead to smarter diagnostic tools.
Source
EurekAlert
Related News
•EurekAlert
AI Model Accurately Predicts Blood Loss Risk in Liposuction
A machine learning model predicts blood loss during high-volume liposuction with 94% accuracy.
•EurekAlert
AI-Driven CT Tool Predicts Cancer Spread in Oropharyngeal Tumors
Researchers have created an AI tool that uses CT imaging to predict the spread risk of oropharyngeal cancer, offering improved treatment stratification.
•EurekAlert
AI Model PRTS Predicts Spatial Transcriptomics From H&E Histology Images
Researchers developed PRTS, a deep learning model that infers single-cell spatial transcriptomics from standard H&E-stained tissue images.