Contextual inference in learning and memory

JB Heald, M Lengyel, DM Wolpert - Trends in cognitive sciences, 2023 - cell.com
Context is widely regarded as a major determinant of learning and memory across
numerous domains, including classical and instrumental conditioning, episodic memory …

If deep learning is the answer, what is the question?

A Saxe, S Nelli, C Summerfield - Nature Reviews Neuroscience, 2021 - nature.com
Neuroscience research is undergoing a minor revolution. Recent advances in machine
learning and artificial intelligence research have opened up new ways of thinking about …

Sleep-like unsupervised replay reduces catastrophic forgetting in artificial neural networks

T Tadros, GP Krishnan, R Ramyaa… - Nature …, 2022 - nature.com
Artificial neural networks are known to suffer from catastrophic forgetting: when learning
multiple tasks sequentially, they perform well on the most recent task at the expense of …

Rapid context inference in a thalamocortical model using recurrent neural networks

WL Zheng, Z Wu, A Hummos, GR Yang… - Nature …, 2024 - nature.com
Cognitive flexibility is a fundamental ability that enables humans and animals to exhibit
appropriate behaviors in various contexts. The thalamocortical interactions between the …

Parametric control of flexible timing through low-dimensional neural manifolds

M Beiran, N Meirhaeghe, H Sohn, M Jazayeri, S Ostojic - Neuron, 2023 - cell.com
Biological brains possess an unparalleled ability to adapt behavioral responses to changing
stimuli and environments. How neural processes enable this capacity is a fundamental open …

Thalamic regulation of frontal interactions in human cognitive flexibility

A Hummos, BA Wang, S Drammis… - PLoS Computational …, 2022 - journals.plos.org
Interactions across frontal cortex are critical for cognition. Animal studies suggest a role for
mediodorsal thalamus (MD) in these interactions, but the computations performed and direct …

Action chunking as policy compression

L Lai, AZ Huang, SJ Gershman - 2022 - osf.io
Many skills in our everyday lives are learned by sequencing actions towards a desired goal.
The action sequence can become a chunk when individual actions are grouped together …

Reinforcement learning approaches to hippocampus-dependent flexible spatial navigation

C Tessereau, R O'Dea… - Brain and …, 2021 - journals.sagepub.com
Humans and non-human animals show great flexibility in spatial navigation, including the
ability to return to specific locations based on as few as one single experience. To study …

Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts

JT Colas, JP O'Doherty, ST Grafton - PLOS Computational Biology, 2024 - journals.plos.org
Active reinforcement learning enables dynamic prediction and control, where one should not
only maximize rewards but also minimize costs such as of inference, decisions, actions, and …

Fast rule switching and slow rule updating in a perceptual categorization task

F Bouchacourt, S Tafazoli, MG Mattar, TJ Buschman… - Elife, 2022 - elifesciences.org
To adapt to a changing world, we must be able to switch between rules already learned and,
at other times, learn rules anew. Often we must do both at the same time, switching between …