Niessing, Michael

[["dc.bibliographiccitation.artnumber","jn.00614.2017"],["dc.bibliographiccitation.journal","Journal of Neurophysiology"],["dc.contributor.author","Berger, Michael"],["dc.contributor.author","Calapai, Antonino"],["dc.contributor.author","Stephan, Valeska"],["dc.contributor.author","Niessing, Michael"],["dc.contributor.author","Burchardt, Leonore"],["dc.contributor.author","Gail, Alexander"],["dc.contributor.author","Treue, Stefan"],["dc.date.accessioned","2018-01-17T13:11:54Z"],["dc.date.available","2018-01-17T13:11:54Z"],["dc.date.issued","2017"],["dc.description.abstract","Teaching non-human primates the complex cognitive behavioral tasks that are central to cognitive neuroscience research is an essential and challenging endeavor. It is crucial for the scientific success that the animals learn to interpret the often complex task rules, and reliably and enduringly act accordingly. To achieve consistent behavior and comparable learning histories across animals, it is desirable to standardize training protocols. Automatizing the training can significantly reduce the time invested by the person training the animal. And self-paced training schedules with individualized learning speeds based on automatic updating of task conditions could enhance the animals' motivation and welfare. We developed a training paradigm for across-task unsupervised training (AUT) of successively more complex cognitive tasks to be administered through a stand-alone housing-based system optimized for rhesus monkeys in neuroscience research settings (Calapai et al. 2016). The AUT revealed inter-individual differences in long-term learning progress between animals, helping to characterize learning personalities, and commonalities, helping to identify easier and more difficult learning steps in the training protocol. Our results demonstrate that (1) rhesus monkeys stay engaged with the AUT over months despite access to water and food outside the experimental sessions, but with lower numbers of interaction compared to conventional fluid-controlled training; (2) with unsupervised training across sessions and task levels, rhesus monkeys can learn tasks of sufficient complexity for state-of-the art cognitive neuroscience in their housing environment; (3) AUT learning progress is primarily determined by the number of interactions with the system rather than the mere exposure time."],["dc.identifier.doi","10.1152/jn.00614.2017"],["dc.identifier.pmid","29142094"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/11705"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.relation.eissn","1522-1598"],["dc.title","Standardized automated training of rhesus monkeys for neuroscience research in their housing environment"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]