Topic: lazy learning

Equality reasoning is ubiquitous and purely abstract -- sameness or difference may be evaluated no matter the nature of the underlying objects. As a result, same-different tasks (SD) have been extensively studied as a starting point for understanding abstract reasoning in humans, and comparatively across animal species. With the rise of neural networks (NN) that exhibit a striking apparent proficiency for abstractions, equality reasoning in NNs has also gained interest. Yet despite the extensive study, conclusions about equality reasoning vary widely. Some authors claim that SD can be learned

Integrating task-relevant information into neural representations is essential for both biological and artificial intelligence. However, current experimental limitations in tracking synaptic weight changes make it challenging to understand learning mechanisms in animals. To address this, we propose a framework based on neural manifold geometry to study representation learning directly from neural activity. A neural manifold represents the collection of population responses to the same task condition (e.g., a cat manifold for responses to images of cats) and can be estimated from recorded activ