Intrinsic determinants of flexible prefrontal ensembles

点击翻译按钮获取中文摘要

The prefrontal cortex (PFC) is thought to be engaged in diverse cognitive tasks such as working memory, decision making, response inhibition, category learning, time estimation, and many more. There is consensus that prefrontal cortex neurons exhibit mixed selectivity, i.e., a single neuron’s activity generally contributes to several of such tasks. It is therefore likely that such distributed coding could be based on the dynamical organization of prefrontal ensembles across tasks. To test this core hypothesis 1 of the Research Unit across species and developmental stages, we propose to develop and extend required data analytical tools that allow both to identify recurring population patterns (ensembles) and to relate them to task parameters. For the ensemble detection, we follow established approaches from our own lab and recent developments in the literature. For connecting patterns to behavioral parameters, we adopt recent developments (so called adversarial attacks) from deep learning research, allowing us to identify classification boundaries in high dimensional feature spaces. The methods developed and validated in this project will then be applied in collaboration with the experimental laboratories of this Research Unit a) to identify neuronal ensembles that are most informative about certain task, b) to explore to which extent these ensembles are already intrinsically present as pre-structured activity patterns before engagement in tasks, c) to explore how the representations change across development and species, and d) to study, in line with the core hypotheses 2 and 3, which neurons contribute to the ensembles and, eventually, whether those ensembles are input or output defined.