Less than 10% of synapses in sensory areas of cerebral cortex are from feedforward input; the rest stem from lateral and feedback connections. Much experimental and theoretical research into sensory processing, however, focuses on the feedforward pathway despite the significant impact feedback connections have on activity. What role do feedback connections play, and how do they carry them out? This project combines computational modeling with in vivo imaging to explore the function and organization of feedback connections in the mouse visual system. Specifically, it proposes training neural network models with feedback connections on different challenging visual tasks, each inspired by a hypothesized role of feedback (such as prediction or localization). Once these models are trained, their response to images can be compared to experimental data collected from multiple visual areas. These models also provide an ideal experimental setting wherein the anatomy of the learned feedback connections can be fully characterized. These characterizations can inform hypotheses about what feedback features should be present in the biology. To explore if these features are present, in vivo imaging will be used to characterize the information carried by different feedback pathways in the mouse visual system. The impact of feedback on neurons in V1 will also be determined by comparing activity under normal circumstances to when these feedback pathways are silenced. In summary, this work will use modeling to relate properties of feedback connections to their function and test for these properties experimentally. It will also develop an approach that can be applied to many neural pathways.
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