Matthew Farrell

The paper develops a theoretical model of Hebbian plasticity that predicts sequence replay speed and explains how tutor signals influence memory formation and retrieval.

This study shows how recurrent neural networks balance compression and expansion of activity dimensionality during learning to solve discrimination tasks effectively.

The paper uses autoencoder networks to show that regularization can resolve ambiguity in connectivity weights, allowing latent variable extraction through nonlinear dimensionality reduction.

This study investigates how predictive learning in neural networks generates low-dimensional latent representations of sensory inputs by analyzing network dynamics and intrinsic dimensionality.