In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

This paper reviews recent developments under the free energy principle that introduce a
normative perspective on classical economic (utilitarian) decision-making based on (active) …

Successful behaviour depends on the right balance between maximising reward and
soliciting information about the world. Here, we show how different types of information-gain …

Dopamine plays a key role in learning; however, its exact function in decision making and
choice remains unclear. Recently, we proposed a generic model based on active (Bayesian) …

Curious information-seeking is known to be a key driver for learning, but characterizing this
important psychological phenomenon remains a challenge. In this article, we argue that …

This paper addresses the general problem of reinforcement learning (RL) in partially
observable environments. In 2013, our large RL recurrent neural networks (RNNs) learned …

Any adaptive organism faces the choice between taking actions with known benefits
(exploitation), and sampling new actions to check for other, more valuable opportunities …

What drives our curiosity remains an elusive and hotly debated issue, with multiple
hypotheses proposed but a cohesive account yet to be established. This review discusses …

Reverse-engineering the brain involves adopting and testing a hierarchy of working
hypotheses regarding the computational problems that it solves, the representations and …

This paper surveys value systems for developmental cognitive robotics. A value system
permits a biological brain to increase the likelihood of neural responses to selected external …