FOFC · Function and Organization of Feedback Connections

Function and Organization of Feedback Connections

The brain has many recurrent loops---situations where neurons send connections back to populations of neurons that they got input from. In fact, 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 recurrent connections have on activity. What role do these recurrent 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 recurrent connections in the mouse visual system. Specifically, it proposes training neural network models with recurrent connections on different challenging visual tasks inspired by a hypothesized role of feedback. 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 connections can be fully characterized. These characterizations can inform hypotheses about what 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 pathways in the mouse visual system. The impact of recurrence on neurons in V1 will also be determined by comparing activity under normal circumstances to when these recurrent 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 pathway. Understanding the role of recurrent processing will inform how the brain processes sensory information over time. Studying this process using computational models also makes it easy to transfer our understanding of the brain to artificial intelligence.

Data: CORDIS, © European Union