Connecting neural activity to function is a common aim in neuroscience. How to define and
conceptualize function, however, can vary. Here I focus on grounding this goal in the specific …

The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process
of pharmaceutical research and development, is progressing. By using the ability to process …

Learning of adaptive behaviors requires the refinement of coordinated activity across
multiple brain regions. However, how neural communications develop during learning …

The orbitofrontal cortex (OFC) and ventromedial-prefrontal cortex (vmPFC) play a key role in
decision-making and encode task states in addition to expected value. We review evidence …

Recent studies in reinforcement learning have explored brain-inspired function
approximators and learning algorithms to simulate brain intelligence and adapt to …

The last few decades have seen the stepwise incorporation of multitude of disciplines of
computer sciences, physics, chemistry, mathematics, engineering, and information …

The ability to compose new skills from a preacquired behavior repertoire is a hallmark of
biological intelligence. Although artificial agents extract reusable skills from past experience …

One stream of reinforcement learning research is exploring biologically plausible models
and algorithms to simulate biological intelligence and fit neuromorphic hardware. Among …

In visual decision making, high-level features, such as object categories, have a strong
influence on choice. However, the impact of low-level features on behavior is less …

When rodents learn to navigate in a novel environment, a high density of place fields
emerges at reward locations, fields elongate against the trajectory, and individual fields …