Intrinsic determinants of flexible prefrontal ensembles

灵活的前额叶整体的内在决定因素

• 批准号: 465003688
• 负责人: Professor Dr. Christian Leibold
• 金额: --
• 依托单位: Division of Biological Sciences
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/465003688?language=en
• 关键词: Intrinsic; determinants; flexible; prefrontal; ensembles

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.

前额叶皮层（PFC）被认为参与了各种认知任务，如工作记忆，决策，反应抑制，类别学习，时间估计等等。人们一致认为，前额叶皮层神经元表现出混合选择性，即，单个神经元的活动通常有助于完成几个这样的任务。因此，这种分布式编码很可能是基于任务中前额叶集合的动态组织。为了测试这一核心假设1的研究单位跨物种和发展阶段，我们建议开发和扩展所需的数据分析工具，既可以识别经常性的人口模式（合奏），并将它们与任务参数。对于合奏检测，我们遵循我们自己的实验室和文献中的最新发展建立的方法。为了将模式与行为参数联系起来，我们采用了深度学习研究的最新发展（所谓的对抗性攻击），使我们能够在高维特征空间中识别分类边界。本项目中开发和验证的方法将与本研究单位的实验室合作应用，a）识别对某些任务信息量最大的神经元集合，B）探索这些集合在多大程度上已经内在地存在于参与任务之前的预结构化活动模式中，c）探索表征如何在发育和物种之间变化，以及d）根据核心假设2和3，研究哪些神经元对系综有贡献，并且最终研究这些系综是输入定义的还是输出定义的。