We review the central tenets and neuroanatomical basis of the global neuronal workspace
(GNW) hypothesis, which attempts to account for the main scientific observations regarding …

Studies in humans and non-human primates have provided evidence for storage of working
memory contents in multiple regions ranging from sensory to parietal and prefrontal cortex …

A critical challenge in neuromorphic computing is to present computationally efficient
algorithms of learning. When implementing gradient-based learning, error information must …

Neuroscience has focused on the detailed implementation of computation, studying neural
codes, dynamics and circuits. In machine learning, however, artificial neural networks tend …

Most elementary behaviors such as moving the arm to grasp an object or walking into the
next room to explore a museum evolve on the time scale of seconds; in contrast, neuronal …

Humans and many other animals have an enormous capacity to learn about sensory stimuli
and to master new skills. However, many of the mechanisms that enable us to learn remain …

Highlights•Learning in hierarchical neural networks requires credit assignment.•Credit
assignment is difficult if regular inputs mix with credit signals.•Dendritic mechanisms provide …

From the conception of Baddeley's visuospatial sketchpad, visual working memory and
visual attention have been closely linked concepts. An attractive model has advocated unity …

Training deep neural networks with the error backpropagation algorithm is considered
implausible from a biological perspective. Numerous recent publications suggest elaborate …

Reinforcement learning and working memory are two core processes of human cognition
and are often considered cognitively, neuroscientifically, and algorithmically distinct. Here …