Spatial Learning and Spatial Attention

空间学习和空间注意力

• 批准号: RGPIN-2017-04336
• 负责人: Maler, Leonard
• 金额: $ 2.84万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=687438
• 关键词: Spatial; Learning; Attention

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”.

我们如何在新的环境中找到自己的方向？这项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.）在黑暗中使用电子器官放电（EOD）来感知它们的环境。这些鱼利用它们的电感觉来学习食物相对于地标的位置。他们每单位距离的爆炸物处理率（抽样密度，SD）是一种注意力的标志。SD与空间学习有关：当鱼第一次遇到地标或食物时，SD很高，但在学习后减少。SD增加也是由记忆引起的：当预期位置没有食物时，SD增加。为了确定食物相对于地标的位置，鱼必须估计它们之间的距离。我们最近的实验表明，鱼丘脑中的电感觉神经元计算与物体相遇之间的时间间隔：它们的放电频率将发出信号，指示遇到地标和食物之间的时间。我们假设这些神经元的反应，结合对鱼的速度的独立估计，可以用来估计从地标到食物的距离。我们建议用行为实验和记录自由游动的鱼的丘脑来检验这个假设的预测，这些鱼从事与行为研究中相同的导航任务。我们将做行为实验（红外线照明），以确定鱼是否控制其游泳速度，因为它从一个单一的地标导航到食物。我们预测，鱼将游泳缓慢，并以最小的试验到试验的变化，因为它接近预期的食物的位置，同时将增加其SD。我们还预测，鱼的食物位置的估计将不太精确，当它位于远离地标。我们将监测SD，并将其与鱼类定位食物的能力的准确性联系起来，作为其与地标距离的函数。我们将记录从时间间隔编码细胞作为鱼游向食物位于不同距离的地标。我们预测，当鱼控制它的游泳速度，这些细胞将准确地编码地标和食物之间的距离，发射率响应的变异性将随着距离的增加。最后，我们将记录丘脑神经元对侧线输入的反应，并确定它们对鱼的速度编码的准确性。这样我们就可以把鱼的注意力控制速度和神经对速度的准确估计联系起来。给定神经速度估计和时间间隔估计，我们将使用计算模型来预测食物位置的神经估计的精度。通过将模型预测与行为数据进行比较，我们将能够准确地确定脊椎动物如何创建“空间地图”。