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

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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.