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Data Whitening Improves Sparse Autoencoder Learning
Ashwin Saraswatula, David Klindt
Sparse autoencoders (SAEs) have emerged as a promising approach for learning interpretable features from neural network activations. However, the optimization landscape for SAE tra…
From superposition to sparse codes: interpretable representations in neural networks
David Klindt, Charles O'Neill, Patrik Reizinger +2
Understanding how information is represented in neural networks is a fundamental challenge in both neuroscience and artificial intelligence. Despite their nonlinear architectures,…
Latent computing by biological neural networks: A dynamical systems framework
Fatih Dinc, Marta Blanco-Pozo, David Klindt +8
Although individual neurons and neural populations exhibit the phenomenon of representational drift, perceptual and behavioral outputs of many neural circuits can remain stable acr…