BrainGain: Modulation of abnormal brain activity by neurostimulation The stimulated nucleus is part of an extensive network involving many brain structures and consisting of several closed loops. Stimulation directly affects neuronal elements within the vicinity of the stimulus site, but
NEUROCAP: natural artificial limbs The goal of this project is to develop a highly selective neuroprosthesis for artificial hand control in amputees. The prosthesis is a neural endcap device. The problem with previous devices is that neural regeneration is hampered by fasciculation of regenerating axons
Memory The brain consists of billions of neurons that are as an ensemble capable of memorizing. It is widely assumed that memories are encoded in the connections between neurons. Thus, new experiences induce connectivity changes in the network, that encode for that memory. However, it remains one of the mysteries of the brain how older memories are protected when new ones are formed.
Network Excitability Characteristic of spontaneous activity in the sleeping neocortex are synchronous poly-neuronal bursts. These play an important role in the homeostatic regulation of network excitability. Long-term suppression of these bursts in the cortex leads to hyper excitability what is usually obseverd in the form of epileptic activity patterns. In contrast to the sleep state, the neocortex receives cholinergic input from other nuclei during the more active States (awake or REM sleep), during these states the bursts disappear.
Cognomics: Comparing neuronal circuits in vitro and in vivo. Our goal in Cognomics (a UT spearhead project financed by the university board, 1.8 MEuro, mediated by BMTI-MIRA/MESA+, main partners BSS (EWI), BIOS (EWI), BME (CTW)) is to understand dynamic features of cognitive processes,
NEURoVERS-it . Neuro-Cognitive Science and Information Technology Virtual University Neurovers-IT is a Marie Curie "Research Training Network", funded by the European Union's 6th Framework Program. The theme of the project is the potential of " in vitro neuronal networks" as a novel class of "computational device". Such devices, we believe, could provide capabilities that are difficult or impossible to implement in current silicon technology. For example, they could learn, they could adapt to the environment they inhabit, they could repair themselves. In some ways, at least they would be similar to the complex networks of neurons we find in human and animal brains.