Topic: high-density multielectrode array

The modern world is increasingly dependent upon artificial intelligence and machine learning. Applications range from decision-making in areas such as health and finance to autonomous vehicle control. However, current deep-learning machines and neural network algorithms have important limitations, namely ineffective learning rules, long training, and high power consumption (1). In the present project, we aim to address these limitations by using the human nervous system itself as a model, which can process external information in a power-efficient way. To accomplish this objective, we utilize

DYT1 is an autosomal dominant form of dystonia, linked to a deletion (ΔGAG) in the TOR1A gene. The basal ganglia have been found to play a crucial role in the development of dystonia in the TOR1AΔGAG murine model. Experimental evidence has also indicated that the cerebellar cortex is likely to play a pivotal role in the pathophysiology of this disorder. We investigated cerebellar cortex activity and synaptic plasticity by performing extracellular recordings in acute parasagittal cerebellar slices with a high-density multielectrode array (HD-MEA) that allows to simultaneously record the activit