Explaining dopamine through prediction errors and beyond
The most influential account of phasic dopamine holds that it reports reward prediction
errors (RPEs). The RPE-based interpretation of dopamine signaling is, in its original form …
errors (RPEs). The RPE-based interpretation of dopamine signaling is, in its original form …
Spontaneous behaviour is structured by reinforcement without explicit reward
Spontaneous animal behaviour is built from action modules that are concatenated by the
brain into sequences,. However, the neural mechanisms that guide the composition of …
brain into sequences,. However, the neural mechanisms that guide the composition of …
A feature-specific prediction error model explains dopaminergic heterogeneity
The hypothesis that midbrain dopamine (DA) neurons broadcast a reward prediction error
(RPE) is among the great successes of computational neuroscience. However, recent …
(RPE) is among the great successes of computational neuroscience. However, recent …
Action prediction error: a value-free dopaminergic teaching signal that drives stable learning
Animals' choice behavior is characterized by two main tendencies: taking actions that led to
rewards and repeating past actions. Theory suggests these strategies may be reinforced by …
rewards and repeating past actions. Theory suggests these strategies may be reinforced by …
The role of motor cortex in motor sequence execution depends on demands for flexibility
The role of the motor cortex in executing motor sequences is widely debated, with studies
supporting disparate views. Here we probe the degree to which the motor cortex's …
supporting disparate views. Here we probe the degree to which the motor cortex's …
Predictive auxiliary objectives in deep RL mimic learning in the brain
C Fang, KL Stachenfeld - arXiv preprint arXiv:2310.06089, 2023 - arxiv.org
The ability to predict upcoming events has been hypothesized to comprise a key aspect of
natural and machine cognition. This is supported by trends in deep reinforcement learning …
natural and machine cognition. This is supported by trends in deep reinforcement learning …
[HTML][HTML] Motor cortex is required for flexible but not automatic motor sequences
How motor cortex contributes to motor sequence execution is much debated, with studies
supporting disparate views. Here we probe the degree to which motor cortex's engagement …
supporting disparate views. Here we probe the degree to which motor cortex's engagement …
Dynamics of striatal action selection and reinforcement learning
Spiny projection neurons (SPNs) in dorsal striatum are often proposed as a locus of
reinforcement learning in the basal ganglia. Here, we identify and resolve a fundamental …
reinforcement learning in the basal ganglia. Here, we identify and resolve a fundamental …
[PDF][PDF] Reinforcement learning with dopamine: a convergence of natural and artificial intelligence
P Masset, SJ Gershman - The Handbook of Dopamine. Elsevier, 2024 - gershmanlab.com
Reinforcement learning is the problem of predicting and maximizing long-term reward.
Computer scientists recognized that this problem could be solved by updating predictions …
Computer scientists recognized that this problem could be solved by updating predictions …
A computational principle of habit formation
KJ Lakshminarasimhan - bioRxiv, 2024 - biorxiv.org
Actions are influenced by multiple decision-making systems--including a goal-directed
system that favors rewarded actions and a habit system that repeats past actions--but …
system that favors rewarded actions and a habit system that repeats past actions--but …