Theories of attention and learning have hypothesized a central role for high-frequency bursting
in cognitive functions, but experimental reports of burst-mediated representations in vivo …

Neuroscience research has made immense progress over the last decade, but our
understanding of the brain remains fragmented and piecemeal: the dream of probing an arbitrary …

In real life, success is often contingent upon multiple critical steps that are distant in time from
each other and from the final reward. These critical steps are challenging to identify with …

Understanding the mechanisms behind decisions taken by large foundation models in
sequential decision making tasks is critical to ensuring that such systems operate transparently …

Neurons in the primary cortex carry sensory- and behavior-related information, but it remains
an open question how this information emerges and intersects together during learning. …

Reinforcement Learning (RL) has emerged as the standard paradigm for sequential
decision-making and a framework for general intelligence. At its core, the RL problem is one of trial-…

The brain’s prowess in learning and adapting remains an enigma, particularly in its approach
to the’temporal credit assignment’problem. How do neural circuits determine which …

In the largest survey of its kind, 2,778 researchers who had published in top-tier artificial
intelligence (AI) venues gave predictions on the pace of AI progress and the nature and impacts …

To make reinforcement learning more sample efficient, we need better credit assignment
methods that measure an action’s influence on future rewards. Building upon Hindsight Credit …

Temporal difference (TD) learning is often used to update the estimate of the value function
which is used by RL agents to extract useful policies. In this paper, we focus on value …