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 …

Machine learning addresses the question of how to build computers that improve
automatically through experience. It is one of today's most rapidly growing technical fields …

An influential line of thinking in behavioral science, to which the two authors have long
subscribed, is that many of society's most pressing problems can be addressed cheaply and …

The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …

Drug addiction represents a dramatic dysregulation of motivational circuits that is caused by
a combination of exaggerated incentive salience and habit formation, reward deficits and …

The theory of reinforcement learning provides a normative account, deeply rooted in
psychological and neuroscientific perspectives on animal behaviour, of how agents may …

Recent progress in artificial intelligence has renewed interest in building systems that learn
and think like people. Many advances have come from using deep neural networks trained …

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 …

During learning, the brain modifies synapses to improve behaviour. In the cortex, synapses
are embedded within multilayered networks, making it difficult to determine the effect of an …