Emergent Generalization by Representation Learning in Artificial Neural Networks
Dimensionality reduction has proven powerful for identifying neural manifolds, which are low-dimensional structures underlying high-dimensional neural activity. These low-dimensional representations have improved the interpretability of population-level coding. Yet whether such low-dimensional representations are biologically relevant and confer functional advantages in learning systems, or merely reflect neuron-level activity, remains contested in neuroscience. We show that an explicit information bottleneck forcing a recurrent neural network to learn a low-dimensional representation is necessary for rotational and out-of-distribution generalisation in a time-series prediction task. Usin...
| Authors | Hardik Rajpal, Dan Goodman |
|---|---|
| Primary category | q-bio.NC |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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