Attractor networks are a popular computational construct used to model different brain
systems. These networks allow elegant computations that are thought to represent a number …

We review the use of neural field models for modelling the brain at the large scales
necessary for interpreting EEG, fMRI, MEG and optical imaging data. Albeit a framework that …

Prefrontal persistent activity during the delay of spatial working memory tasks is thought to
maintain spatial location in memory. A'bump attractor'computational model can account for …

Toward the long-standing dream of artificial intelligence, two successful solution paths have
been paved: 1) neuromorphic computing and 2) deep learning. Recently, they tend to …

Neural representations of head direction (HD) have been discovered in many species.
Theoretical work has proposed that the dynamics associated with these representations are …

Recent theoretical models suggest that deciding about actions and executing them are not
implemented by completely distinct neural mechanisms but are instead two modes of an …

Elucidating the intricate neural mechanisms underlying brain functions requires integrative
brain dynamics modeling. To facilitate this process, it is crucial to develop a general-purpose …

Neural noise limits the fidelity of representations in the brain. This limitation has been
extensively analyzed for sensory coding. However, in short-term memory and integrator …

Continuous attractor models of working-memory store continuous-valued information in
continuous state-spaces, but are sensitive to noise processes that degrade memory …

Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only
their content, but also their temporal structure. The phenomenon of replay, in the …