Reference number : GC-2017-Q1-01918, amendment number : , agreement type : G, recipient legal name : Maler, Leonard (University of Ottawa)|Maler, Leonard (Université d’Ottawa), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : How do we learn to find our way in a new environment? The proposed research will connect spatial learning to attention using an electric fish model and determine the neural bases for such learning. Pulse electric fish (Gymnotus sp.) use electric organ discharges (EOD) to sense their environment in the dark. These fish learn the location of food with respect to landmarks using their electrosense. Their EOD rate per unit distance (sampling density, SD) is a signature of attention. SD is associated with spatial learning: it is high when the fish first encounters landmarks or food but diminishes after learning. SD increases are also induced by memory: it increases when no food is present at its expected location. In order to locate the food with respect to a landmark, the fish has to estimate the distance between them. Our recent experiments have shown that electrosensory neurons in the fish&rsquo;s thalamus compute the time interval between encounters with objects: their firing rate will signal the time between encountering a landmark followed by food. We hypothesize that the response of these neurons, combined with an independent estimate of the fish&rsquo;s speed, could be used to estimate the distance from landmark to food. We propose to test the predictions that flow from this hypothesis with behavioural experiments and by recording from the thalamus of freely swimming fish engaged in the same navigational task examined in the behavioural studies. 
We will do behavioural experiments (infrared lighting) to determine whether the fish controls its swim speed as it navigates from a single landmark to the food. We predict that the fish will swim slowly and with minimal trial-to-trial variability as it approaches the expected food location and will simultaneously increase its SD. We also predict that the fish&rsquo;s estimate of food location will be less precise when it is located further from the landmark. We will monitor SD and connect it to the accuracy of the fish&rsquo;s ability to localize food as a function of its distance from the landmark. 
We will record from the time interval coding cells as the fish swims towards food located at varying distances from the landmark. We predict that, when the fish controls its swim speed, these cells will accurately encode distance between landmark and food; the variability of the firing rate response will increase with distance. Finally, we will record from thalamic neurons responsive to lateral line input and determine how accurately they can encode the fish&rsquo;s speed. We will then be able to connect the fish&rsquo;s attentional control of its speed and connect it to the accuracy of the neural estimate of speed. Given the neural speed estimates and the time interval estimates, we will use a computational model to predict the precision of the neural estimate of food location. By comparing the model predictions to the behavioural data, we will be able to determine exactly how a vertebrate can create a &ldquo;spatial map&rdquo;., program purpose french : How do we learn to find our way in a new environment? The proposed research will connect spatial learning to attention using an electric fish model and determine the neural bases for such learning. Pulse electric fish (Gymnotus sp.) use electric organ discharges (EOD) to sense their environment in the dark. These fish learn the location of food with respect to landmarks using their electrosense. Their EOD rate per unit distance (sampling density, SD) is a signature of attention. SD is associated with spatial learning: it is high when the fish first encounters landmarks or food but diminishes after learning. SD increases are also induced by memory: it increases when no food is present at its expected location. In order to locate the food with respect to a landmark, the fish has to estimate the distance between them. Our recent experiments have shown that electrosensory neurons in the fish&rsquo;s thalamus compute the time interval between encounters with objects: their firing rate will signal the time between encountering a landmark followed by food. We hypothesize that the response of these neurons, combined with an independent estimate of the fish&rsquo;s speed, could be used to estimate the distance from landmark to food. We propose to test the predictions that flow from this hypothesis with behavioural experiments and by recording from the thalamus of freely swimming fish engaged in the same navigational task examined in the behavioural studies. 
We will do behavioural experiments (infrared lighting) to determine whether the fish controls its swim speed as it navigates from a single landmark to the food. We predict that the fish will swim slowly and with minimal trial-to-trial variability as it approaches the expected food location and will simultaneously increase its SD. We also predict that the fish&rsquo;s estimate of food location will be less precise when it is located further from the landmark. We will monitor SD and connect it to the accuracy of the fish&rsquo;s ability to localize food as a function of its distance from the landmark. 
We will record from the time interval coding cells as the fish swims towards food located at varying distances from the landmark. We predict that, when the fish controls its swim speed, these cells will accurately encode distance between landmark and food; the variability of the firing rate response will increase with distance. Finally, we will record from thalamic neurons responsive to lateral line input and determine how accurately they can encode the fish&rsquo;s speed. We will then be able to connect the fish&rsquo;s attentional control of its speed and connect it to the accuracy of the neural estimate of speed. Given the neural speed estimates and the time interval estimates, we will use a computational model to predict the precision of the neural estimate of food location. By comparing the model predictions to the behavioural data, we will be able to determine exactly how a vertebrate can create a &ldquo;spatial map&rdquo;., agreement title english : Spatial Learning and Spatial Attention, agreement title french : Spatial Learning and Spatial Attention, agreement number : RGPIN, agreement value : 195000.0, 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