Jan 2, 2022
2021 in Review: Unsupervised Brain Models
Posted by Kelvin Dafiaghor in categories: neuroscience, robotics/AI
We’re in a golden age of merging AI and neuroscience. No longer tied to conventional publication venues with year-long turnaround times, our field is moving at record speed. As 2021 draws to a close, I wanted to take some time to zoom out and review a recent trend in neuro-AI, the move toward unsupervised learning to explain representations in different brain areasfootnote.
One of the most robust findings in neuro-AI is that artificial neural networks trained to perform ecologically relevant tasks match single neurons and ensemble signals in the brain. The canonical example is the ventral stream, where DNNs trained for object recognition on ImageNet match representations in IT (Khaligh-Razavi & Kriegeskorte, 2014, Yamins et al. 2014). Supervised, task-optimized networks link two important forms of explanation: ecological relevance and accounting for neural activity. They answer the teleological question: what is a brain region for?
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