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.
A unified, scalable framework for neural population decoding.Advances in Neural Information Processing Systems, 36:44937–44956, 2023
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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.