5 papers
Hierarchical Successor Representation for Robust Transfer
Changmin Yu, Máté Lengyel
The successor representation (SR) provides a powerful framework for decoupling predictive dynamics from rewards, enabling rapid generalisation across reward configurations. However…
When sufficiency is insufficient: the functional information bottleneck for identifying probabilistic neural representations
Ishan Kalburge, Máté Lengyel
The neural basis of probabilistic computations remains elusive, even amidst growing evidence that humans and other animals track their uncertainty. Recent work has proposed that pr…
Setting up for failure: automatic discovery of the neural mechanisms of cognitive errors
Puria Radmard, Paul M. Bays, Máté Lengyel
Discovering the neural mechanisms underpinning cognition is one of the grand challenges of neuroscience. However, previous approaches for building models of RNN dynamics that expla…
Dynamical stability for dense patterns in attractor neural networks
Uri Cohen, Máté Lengyel
Recurrent neural networks are canonical models of biological memory. In these models, memories are represented by distributed patterns of neural activity that are stored in the rec…
A flexible Bayesian non-parametric mixture model reveals multiple dependencies of swap errors in visual working memory
Puria Radmard, Paul M. Bays, Máté Lengyel
Human behavioural data in psychophysics has been used to elucidate the underlying mechanisms of many cognitive processes, such as attention, sensorimotor integration, and perceptua…