Top-down control of cortical visual sensory information processing in spatial navigation

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Behavioural adaptation to the environment is vital for survival and relies on the ability to selectively integrate relevant sensory information. This ability depends on the potential of neuronal networks to change through experience; for example, by learning the association of a specific sensory stimulus with a reward. Recent studies have demonstrated that sensory processing in the primary visual cortex (V1) is modulated by axonal feedback projections from higher-order cortical areas. These projections convey predictive signals encoding task-relevant features of the environment. Feedback modulation arises from experience and is thought to gate sensory information according to task demands. For example, it was shown that feedback from the higher-order anterior cingulate cortex (ACC) to V1 conveys information on spatial expectations and self-motion which is important for spatial navigation. It remains largely elusive how feedback is integrated into V1 local circuits and how it alters sensory processing. Further, it is unknown how this contextual feedback circuit is established during learning. A key structure thought to be involved in this process are the basal ganglia: They receive input from both V1 and ACC through the dorsomedial striatum (DMS) and encode action-outcome contingencies.The aim of this proposal is to determine how the integration of visual input with self-motion related input is established in V1 to encode spatial information that is crucial for navigation. Specifically, how is contextual information from ACC integrated with visual information in V1? Does this process depend on neuronal projections from V1 to DMS during the learning of a rewarded navigation task?I will use state-of-the-art two-photon Ca2+ imaging and electrophysiological recordings in combination with optogenetic and chemogenetic approaches to record and modulate neuronal activity in a projection-specific manner.The project will be organized around two aims:1) To characterize the functional inputs from ACC to V1 during and after learning a rewarded visually guided task.2) To determine the causal contribution of V1 projections to DMS and ACC to learning and performance of a rewarded visually guided task.By revealing the inputs, the local circuits computation and the output of V1 neurons to DMS and ACC during learning, I will be able to propose a unifying model that captures how behavioural training changes the neocortex to improve the encoding of behaviourally relevant visual objects.