Spatial Learning and Spatial Attention

我们如何学会在新环境中找到自己的路？这项拟议的研究将使用电鱼模型将空间学习与注意力联系起来，并确定这种学习的神经基础。脉冲电鱼（Gymnotus sp.）利用电器官放电（EOD）在黑暗中感知环境。这些鱼利用它们的电感官根据地标来学习食物的位置。每单位距离的EOD率（采样密度，SD）是注意力的标志。SD与空间学习有关：当鱼第一次遇到地标或食物时，SD值很高，但在学习后会降低。SD的增加也是由记忆引起的：当没有食物出现在预期位置时，SD会增加。为了根据地标定位食物，鱼必须估计它们之间的距离。我们最近的实验表明，鱼的丘脑中的电感觉神经元计算遇到物体之间的时间间隔：它们的放电频率将表明遇到地标和食物之间的时间间隔。我们假设这些神经元的反应，结合对鱼速度的独立估计，可以用来估计从地标到食物的距离。我们建议通过行为实验和记录自由游动的鱼的丘脑来测试从这一假设中得出的预测，这些鱼参与了行为研究中检查的相同的导航任务。

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'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'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'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'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's speed. We will then be able to connect the fish'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 “spatial map”.