In this review, we compare methods for temporal sequence learning (TSL) across the
disciplines machine-control, classical conditioning, neuronal models for TSL as well as …

We describe work in progress with the aim of constructing a computational model of
emotional learning and processing inspired by neurophysiological findings. The main brain …

How can we make better sense of animal behavior by using what we know about the brain?
This is the first book that attempts to answer this important question by applying neural …

Reinforcement learning (RL) is learning by interacting with an environment. An RL agent
learns from the consequences of its actions, rather than from being explicitly taught and it …

Animals successfully forage within new environments by learning, simulating and adapting
to their surroundings. The functions behind such goal-oriented behavior can be …

We describe work in progress with the aim of constructing a computational model of
emotional learning and processing inspired by neurophysiological findings. The main areas …

Adaptive behavior requires the sensing of salient behavioral consequences which can act to
modulate changes in neural connections linking sensory and motor structures. In previous …

Learning is an important talent for understanding the nature and accordingly controlling
behavioral characteristics. Behavioral learning theories are one of the popular learning …

It is a well-known fact that Hebbian learning is inherently unstable because of its self-
amplifying terms: the more a synapse grows, the stronger the postsynaptic activity, and …

In this thesis we present our work, where we developed artificial societies of intelligent
agents, in order to understand and simulate adaptive behaviour and social processes. We …