Reconstructing neuro-dynamical principles of prefrontal cortical computations across cognitive tasks and species

重建跨认知任务和物种的前额皮质计算的神经动力学原理

• 批准号: 465072828
• 负责人: Professor Dr. Daniel Durstewitz
• 金额: --
• 依托单位: Klinik für Psychiatrie und Psychotherapie
• 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/465072828?language=en
• 关键词: Reconstructing; neuro; dynamical; principles; prefrontal

The present consortium offers a unique and unprecedented opportunity to unravel neuro-dynamical and computational principles and mechanisms of higher cognitive functions across species, developmental stages, and cognitive domains. This project will exploit this opportunity and will attempt to uncover general computational principles through analyses of experimental data acquired by the other project partners from a unifying dynamical systems perspective, and within a common statistical machine learning framework. We will use deep generative recurrent neural networks (RNN) to approximate the dynamical systems underlying the experimental data. While RNNs in general have recently become a popular tool in neuroscience to study dynamical mechanisms, our methods go one step further and infer RNN parameters directly from the observed physiological and behavioral time series in a statistical, maximum-likelihood or Bayesian approach, yielding quantitative predictions. In collaboration with the experimental partners, we will deploy these methods established in our group to study the neural attractor dynamics of decision making, neuro-dynamical mechanisms underlying interval timing, species-specific neuro-computational mechanisms of working memory, and how these change with developmental stage. We will also address the long-standing question of whether similar or distinct neuro-computational principles underlie PFC-dependent performance on different cognitive tasks. In each of these instances, we propose a set of specific hypotheses about the underlying neuro-dynamical mechanisms that can be tested using our approach. Moreover, we will also generate specific (quantitative) novel predictions that will be fed back to the experimental partners. Furthermore, our RNNs are interpretable both from a dynamical systems perspective and in the sense that they enable to relate neural trajectories in state space to spatio-temporal activity patterns in the data. Hence, RNN trajectories map onto cell assemblies as studied in TP9, thus tightly linking these two theoretical approaches. Finally, we will integrate all findings into a common computational framework and theory of the neuro-dynamical principles underlying prefrontal flexibility.

目前的联盟提供了一个独特的和前所未有的机会，解开神经动力学和计算的原则和机制，更高的认知功能，跨物种，发展阶段和认知领域。该项目将利用这一机会，并试图通过分析其他项目合作伙伴从统一的动力系统角度获得的实验数据，并在一个共同的统计机器学习框架内，揭示一般的计算原理。我们将使用深度生成递归神经网络（RNN）来近似实验数据背后的动力系统。虽然RNN最近已经成为神经科学中研究动力学机制的流行工具，但我们的方法更进一步，直接从观察到的生理和行为时间序列中以统计，最大似然或贝叶斯方法推断RNN参数，产生定量预测。在与实验伙伴的合作中，我们将部署这些方法建立在我们的小组，以研究决策的神经吸引动力学，神经动力学机制的间隔时间，工作记忆的物种特异性神经计算机制，以及这些如何随着发展阶段的变化。我们还将解决一个长期存在的问题，即在不同的认知任务中，PFC依赖的表现是否基于相似或不同的神经计算原理。在这些情况下，我们提出了一套具体的假设，可以使用我们的方法进行测试的潜在的神经动力学机制。此外，我们还将产生具体的（定量的）新预测，这些预测将反馈给实验伙伴。此外，我们的RNN从动态系统的角度来看是可解释的，并且在某种意义上，它们能够将状态空间中的神经轨迹与数据中的时空活动模式相关联。因此，RNN轨迹映射到TP9中研究的细胞集合体上，从而将这两种理论方法紧密联系起来。最后，我们将把所有的发现整合到一个共同的计算框架和理论的神经动力学原则的基础前额叶的灵活性。