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 …

Neuromodulatory systems, including the noradrenergic, serotonergic, dopaminergic, and
cholinergic systems, track environmental signals, such as risks, rewards, novelty, effort, and …

Neurofeedback (NFB) is emerging as a promising technique that enables self-regulation of
ongoing brain oscillations. However, despite a rise in empirical evidence attesting to its …

Biological neural networks are systems of extraordinary computational capabilities shaped
by evolution, development, and lifelong learning. The interplay of these elements leads to …

Robotics can play a useful role in the scientific understanding of the sense of self, both
through the construction of embodied models of the self and through the use of robots as …

We continuously face the dilemma of choosing between actions that gather new information
or actions that exploit existing knowledge. This “exploration-exploitation” trade-off depends …

The vast majority of research on sport consumers fails to utilize a theoretical understanding
of motivation to examine behaviour. Self-Determination Theory was used to develop a new …

The purpose of this study is to examine the effect of system quality and information quality on
the User satisfaction of The Accrual-Based Accounting System of Institutions application …

Mature simulation systems for Spiking Neural Networks (SNNs) become more relevant than
ever for understanding the brain and supporting neuromorphic computing. The CARL-sim …

We use a biologically grounded neural network model to investigate the brain mechanisms
underlying individual differences specific to the selection and instantiation of representations …