The Computational Neurophysics lab at the University of Bremen headed by Dr. Udo Ernst offers at the earliest date possible: Postdoc / PhD student in Computational Neuroscience for 3 years. In this project we want to study organization and optimization of flexible information processing in neural networks, with specific focus on the visual system. You will use network modelling, numerical simulation, and mathematical analysis to investigate fundamental aspects of flexible computation such as task-dependent coordination of multiple brain areas for efficient information processing, as well as the emergence of flexible circuits originating from learning schemes which simultaneously optimize for function and flexibility. These studies will be complemented by biophysically realistic modelling and data analysis in collaboration with experimental work. Here we will investigate selective attention as a central aspect of flexibility in the visual system, involving task-dependent coordination of multiple visual areas.

Flexible computation is a hallmark of intelligent behavior. Yet, little is known about how neural networks contextually reconfigure for different computations. Humans are able to perform a new task without extensive training, presumably through the composition of elementary processes that were previously learned. Cognitive scientists have long hypothesized the possibility of a compositional neural code, where complex neural computations are made up of constituent components; however, the neural substrate underlying this structure remains elusive in biological and artificial neural networks. Here we identified an algorithmic neural substrate for compositional computation through the study of multitasking artificial recurrent neural networks. Dynamical systems analyses of networks revealed learned computational strategies that mirrored the modular subtask structure of the task-set used for training. Dynamical motifs such as attractors, decision boundaries and rotations were reused across different task computations. For example, tasks that required memory of a continuous circular variable repurposed the same ring attractor. We show that dynamical motifs are implemented by clusters of units and are reused across different contexts, allowing for flexibility and generalization of previously learned computation. Lesioning these clusters resulted in modular effects on network performance: a lesion that destroyed one dynamical motif only minimally perturbed the structure of other dynamical motifs. Finally, modular dynamical motifs could be reconfigured for fast transfer learning. After slow initial learning of dynamical motifs, a subsequent faster stage of learning reconfigured motifs to perform novel tasks. This work contributes to a more fundamental understanding of compositional computation underlying flexible general intelligence in neural systems. We present a conceptual framework that establishes dynamical motifs as a fundamental unit of computation, intermediate between the neuron and the network. As more whole brain imaging studies record neural activity from multiple specialized systems simultaneously, the framework of dynamical motifs will guide questions about specialization and generalization across brain regions.