Vezha Boboeva

From sequences to schemas: low-rank recurrent dynamics underlie abstract relational representations

This paper shows that RNNs learn abstract relational representations through low-rank recurrent dynamics, which support temporal abstraction and generalization.

The hippocampus enables abstract structure learning without reward

This paper shows that the hippocampus is essential for mice to learn abstract statistical structures from sensory input without reward.

From recency to central tendency biases in working memory: a unifying network model

This paper presents a network model that unifies recency and central tendency biases in working memory by showing how volatile memory content leads to contraction bias in rats and humans.

Free recall scaling laws and short-term memory effects in a latching attractor network

This paper develops a neural network model that reproduces free recall phenomena, including power laws and serial position effects, by incorporating firing rate adaptation, global inhibition, and heteroassociative learning.

Latching dynamics as a basis for short-term recall

The paper models short-term memory using latching dynamics, showing interference limits capacity and sublinear scaling in memory tasks.

In Poetry, if Meter has to Help Memory, it Takes its Time

The paper tests if poetic meter aids memory by manipulating meter components in Italian poetry and finding mixed effects on memory and reaction times.

The capacity for correlated semantic memories in the cortex

The paper explores how semantic memory is represented in the cortex and contrasts it with the simpler structure of the hippocampus for episodic memory.