Atlas Kazemian

The paper shows that distinct neural populations in the human temporal lobe encode speech context from past, future, and surrounding input, with implications for speech comprehension models.

The paper shows that convolutional neural networks with minimal training can produce cortex-aligned visual representations through specific dimensionality manipulations, suggesting architectural constraints are key.