The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history.
In more recent times, however, communication and collaboration between the two fields has …

Abstract Systems neuroscience seeks explanations for how the brain implements a wide
variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to …

Quantifying behavior is crucial for many applications in neuroscience. Videography provides
easy methods for the observation and recording of animal behavior in diverse settings, yet …

Map** behavioural actions to neural activity is a fundamental goal of neuroscience. As our
ability to record large neural and behavioural data increases, there is growing interest in …

Reinforcement learning (RL) has shown great success in increasingly complex single-agent
environments and two-player turn-based games. However, the real world contains multiple …

The first comprehensive treatment of active inference, an integrative perspective on brain,
cognition, and behavior used across multiple disciplines. Active inference is a way of …

Motor learning encompasses a wide range of phenomena, ranging from relatively low-level
mechanisms for maintaining calibration of our movements, to making high-level cognitive …

Neuroscientists today can measure activity from more neurons than ever before, and are
facing the challenge of connecting these brain-wide neural recordings to computation and …

Out of equilibrium, a lack of reciprocity is the rule rather than the exception. Non-reciprocity
occurs, for instance, in active matter,,,,–, non-equilibrium systems,–, networks of neurons …

Quantitative behavioral measurements are important for answering questions across
scientific disciplines—from neuroscience to ecology. State-of-the-art deep-learning methods …