Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We
propose that to accelerate progress in AI, we must invest in fundamental research in …

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 …

Biological organisms learn from interactions with their environment throughout their lifetime.
For artificial systems to successfully act and adapt in the real world, it is desirable to similarly …

Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …

This paper provides a comprehensive review of the current status, advancements, and future
prospects of humanoid robots, highlighting their significance in driving the evolution of next …

The emergence of powerful artificial intelligence (AI) is defining new research directions in
neuroscience. To date, this research has focused largely on deep neural networks trained …

Learning to combine control at the level of joint torques with longer-term goal-directed
behavior is a long-standing challenge for physically embodied artificial agents. Intelligent …

Deep learning techniques have found applications across diverse fields, enhancing the
efficiency and effectiveness of decision-making processes. The integration of these …

Motor neurons are the final common pathway through which the brain controls movement of
the body, forming the basic elements from which all movement is composed. Yet how a …

Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We
propose that to accelerate progress in AI, we must invest in fundamental research in …