Neuronal mechanisms of learning-evoked stimulus orthogonalization

在自然界中，动物的生存往往依赖于学会区分具有相似特征的物体。我们最近的工作已经确定，这种学习与初级视觉皮质(V1)神经元群体的活动变化有关。学习导致对刺激偏好较弱的神经元受到对动物有信息的刺激的抑制，而对刺激偏好较强的神经元只会受到轻微的影响。这种学习诱发效应导致对信息性刺激(即稀疏)反应强烈的神经元数量稀少，从而减少了对多个信息性刺激反应的神经元数量。神经元对信息性刺激反应的相似性的减少，可能会允许大脑中从V1接收视觉信息的区域产生不同的刺激特定行为。然而，这种学习效应背后的神经生物学机制以及它如何影响参与决策的下游大脑区域尚不清楚。为了回答这些问题，我们提出了三个相辅相成的研究目标。我们的第一个目标是识别参与刺激反应稀疏的V1神经元类别。我们将结合我们实验室强大的遗传分析技术，对神经元活动进行大规模记录。通过探测数千个记录的神经元的基因表达，我们可以根据它们的表达谱对它们进行分类，从而允许特定神经元类别的活动与刺激稀疏效应的关联。我们的第二个目标是了解V1大脑回路所有层刺激反应稀疏的精细时间动力学。我们之前的研究使用显微镜记录了神经元的活动，这在时间分辨率上是有限的，并且偏向于皮质层浅层。因此，我们将利用最先进的电生理探头，部分由我们的实验室开发，它可以以毫秒的分辨率采样大量神经元的活动，并跨越V1的整个深度。这些实验将让我们了解稀疏效应的时间进程，以及它是否特定于将信息发送到下游大脑区域的皮质层。我们的第三个目标是了解我们在V1中观察到的学习效果与下游大脑区域活动之间的关系，这些区域对决策和发起行为至关重要。我们最近的工作表明，学习导致V1对信息刺激的反应变得更加不同，我们假设这种效应允许下游区域发起不同的行为来响应它们。我们将通过在V1和与决策有关的大脑区域进行成对的同时电生理记录来直接测试这一点，以确定V1稀疏是否与这些下游脑区的活动变化相关。完成这些目标将提供对V1学习诱发效应背后的神经元电路和脑动力学的关键见解，以及它对视觉引导行为的规划和生成的重要性。

In nature, an animal's survival often depends on learning to discriminate between objects with similar features. Our recent work has established that this learning is associated with changes in activity across the population of neurons in primary visual cortex (V1). Learning causes neurons with weak preferences for stimuli to be suppressed by stimuli informative to the animal, while neurons with strong stimulus preferences are only mildly affected. This learning-evoked effect results in a sparse number of neurons responding strongly to informative stimuli (i.e. sparsening), reducing the number of neurons responding to more than one informative stimulus. This reduction in the similarity of the neuronal responses to informative stimuli may allow regions of the brain that receive visual information from V1 to generate different stimulus-specific behaviors. However, the neurobiological mechanisms underlying this learning effect and how it impacts downstream brain regions involved in decision-making are unknown. To answer these questions, we propose three complementary research objectives.Our first objective is to identify classes of V1 neurons involved in stimulus-response sparsening. We will carry out large-scale recordings of neuronal activity combined with our laboratory's powerful genetic analysis technique. By probing the gene expression of thousands of recorded neurons, we can classify them based on their expression profiles, thus allowing for the correlation of activity in specific neuronal classes to stimulus sparsening effects. Our second objective is to understand the fine-scale temporal dynamics of stimulus-response sparsening across all layers of the V1 brain circuit. Our previous study recorded neuronal activity using microscopy, which is limited in temporal resolution and biased to superficial cortical layers. Thus, we will take advantage of state-of-the-art electrophysiological probes, partly developed in our laboratory, that can sample the activity of large populations of neurons with millisecond resolution and across the entire depth of V1. These experiments will allow us to understand the time course of the sparsening effect and whether it is specific to layers of cortex that send information to downstream brain regions. Our third objective is to understand the relationship between the learning effects we observe in V1 and activity in downstream brain regions important for making decisions and initiating behaviors. Our recent work showed that learning causes V1 responses to informative stimuli to become more dissimilar, and we hypothesize that this effect allows downstream regions to initiate different behaviors in response to them. We will directly test this by carrying out paired simultaneous electrophysiological recordings in V1 and brain regions implicated in decision-making, to determine whether V1 sparsening correlates with changes in activity in these downstream brain regions.Completing these objectives will provide crucial insights into the neuronal circuits and brain dynamics underlying learned-evoked effects on V1 and its importance for the planning and generation of visually guided behaviors.