Reference number : GC-2017-Q1-02011, amendment number : , agreement type : G, recipient legal name : Krishnaswamy, Arjun (McGill University)|Krishnaswamy, Arjun (Université McGill), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Complex patterns of synaptic wiring unite neurons together to form circuits whose computations are critical for a wide range of our sensory, motor and cognitive behaviours. Yet, clear links between such computations and underlying circuit patterns are unclear. The goal of my research is to learn how neural circuitry gives rise to computations such as stimulus detection, information storage, and prediction. To meet this goal, we will map the connections of these retinal circuits to understand how they create the retina's primary computation: to detect salient visual features. Approximately 30 types of retinal circuits analyze the visual scene, each tuned to detect a particular feature such as motion, edges, colour, and so on; each circuit ends with a retinal ganglion cell (RGC), which sends feature signals to the brain for further analysis. The current model is that the RGCs are endowed with their feature preference via the connections they receive from particular types of retinal interneurons, called amacrine and bipolar cells. However, clear maps of such circuitry and its links to feature detection are lacking. To learn more, we will devise methods to rapidly map connections among RGCs, ACs, and BCs using recent advances in genetically encoded calcium indicators (gcamp6f) and optogenetics. Armed with this technique, we will map functional connections from genetically defined interneurons to all RGC types in parallel (Objective #1) and obtain valuable information about the patterns of interneuron connectivity. In parallel, we will measure the visual responses of interneurons to understand how their signals could be used to create RGC feature signals (Objective#2). Interneuron responses to a battery of stimuli that include tests for direction, orientation, and contrast-selectivity will be measured with gcamp6f and related to responses from RGCs evoked by the same stimuli; this will allow us to define transformation from photons to features that occurs in interneuron-RGC circuitry. Finally, we seek causal links between interneuron input and RGC feature preference and will employ genetic methods to ablate or silence particular interneuron types while imaging RGC responses (Objective #3). This research will lead to the training of Highly Qualified Personnel, who will leave my lab armed with a combination of cutting-edge life- and neuroscience research methods. This training is a core objective of this proposal and our plan poises my personnel to become leaders in science and innovation in Canada. The training people receive in my lab will make them highly attractive to employers in research, biotech, foundations and beyond. What we learn together will uncover links between circuit-patterns and circuit function in the retina and offer immense potential to generilize these links to higher centers. The reagents, basic insights and tools from this work offer a way to dissect this issue in the brain, program purpose french : Complex patterns of synaptic wiring unite neurons together to form circuits whose computations are critical for a wide range of our sensory, motor and cognitive behaviours. Yet, clear links between such computations and underlying circuit patterns are unclear. The goal of my research is to learn how neural circuitry gives rise to computations such as stimulus detection, information storage, and prediction. To meet this goal, we will map the connections of these retinal circuits to understand how they create the retina's primary computation: to detect salient visual features. Approximately 30 types of retinal circuits analyze the visual scene, each tuned to detect a particular feature such as motion, edges, colour, and so on; each circuit ends with a retinal ganglion cell (RGC), which sends feature signals to the brain for further analysis. The current model is that the RGCs are endowed with their feature preference via the connections they receive from particular types of retinal interneurons, called amacrine and bipolar cells. However, clear maps of such circuitry and its links to feature detection are lacking. To learn more, we will devise methods to rapidly map connections among RGCs, ACs, and BCs using recent advances in genetically encoded calcium indicators (gcamp6f) and optogenetics. Armed with this technique, we will map functional connections from genetically defined interneurons to all RGC types in parallel (Objective #1) and obtain valuable information about the patterns of interneuron connectivity. In parallel, we will measure the visual responses of interneurons to understand how their signals could be used to create RGC feature signals (Objective#2). Interneuron responses to a battery of stimuli that include tests for direction, orientation, and contrast-selectivity will be measured with gcamp6f and related to responses from RGCs evoked by the same stimuli; this will allow us to define transformation from photons to features that occurs in interneuron-RGC circuitry. Finally, we seek causal links between interneuron input and RGC feature preference and will employ genetic methods to ablate or silence particular interneuron types while imaging RGC responses (Objective #3). This research will lead to the training of Highly Qualified Personnel, who will leave my lab armed with a combination of cutting-edge life- and neuroscience research methods. This training is a core objective of this proposal and our plan poises my personnel to become leaders in science and innovation in Canada. The training people receive in my lab will make them highly attractive to employers in research, biotech, foundations and beyond. What we learn together will uncover links between circuit-patterns and circuit function in the retina and offer immense potential to generilize these links to higher centers. The reagents, basic insights and tools from this work offer a way to dissect this issue in the brain, agreement title english : Uncovering the retinal circuits that underlie visual feature detection, agreement title french : Uncovering the retinal circuits that underlie visual feature detection, agreement number : RGPIN, agreement value : 165000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada