Inserting a TopK sparse autoencoder into the POYO+ neural decoder preserves performance while yielding latent units selective for orientation, temporal frequency, and genetic background, and ablating them causally impairs the corresponding decoding.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nature Machine Intelligence, 1(5), pp.206–215
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Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data
Inserting a TopK sparse autoencoder into the POYO+ neural decoder preserves performance while yielding latent units selective for orientation, temporal frequency, and genetic background, and ablating them causally impairs the corresponding decoding.