Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of
indispensable factors to achieve human-level or super-human AI systems. On the other …

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 …

In this Review, we describe the singular success of attractor neural network models in
describing how the brain maintains persistent activity states for working memory, corrects …

Learning and interpreting the structure of the environment is an innate feature of biological
systems, and is integral to guiding flexible behaviors for evolutionary viability. The concept of …

The medial entorhinal cortex is part of a neural system for map** the position of an
individual within a physical environment. Grid cells, a key component of this system, fire in a …

Hippocampal neurons encode physical variables,,,,,–such as space or auditory frequency in
cognitive maps. In addition, functional magnetic resonance imaging studies in humans have …

Synaptic plasticity, the activity-dependent change in neuronal connection strength, has long
been considered an important component of learning and memory. Computational and …

Learning has been associated with modifications of synaptic and circuit properties, but the
precise changes storing information in mammals have remained largely unclear. We …

The hippocampal-entorhinal system is important for spatial and relational memory tasks. We
formally link these domains, provide a mechanistic understanding of the hippocampal role in …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …