Integration of sensory information in layer 2/3 of barrel cortex - a modelling approach

神经元是大脑的基本细胞单元，通过突触连接形成神经网络。神经科学的核心问题之一是，神经网络如何执行特定的任务或“计算”来产生行为，以及学习过程中活动模式是如何存储的。在我们的工作中，我们专注于老鼠的体感皮质(称为桶皮质)，它被认为与老鼠的触觉系统有关，并通过区分不同的纹理和识别物体来帮助啮齿动物感知环境。物体识别需要整合来自多个胡须的感觉信息。然而，单个胡须信息在哪里以及如何整合来代表被触摸的对象尚不清楚。已知，来自单个胡须的信息通过相应桶柱的层4进入皮质。在桶状柱内，胡须特异性活动被传递到更浅的层，如第2/3层。与第4层相比，桶状皮质第2/3层的神经元表现出非常低的刺激诱发放电率和低的胡须选择性。此外，由于不同的桶形柱之间异常的横向连通性，层2/3对于研究时间和空间整合特别感兴趣。作为我的博士项目的一部分，我想将桶形皮质2/3层和4层的双光子钙成像与网络建模相结合，以了解桶形皮质中多个胡须信息的整合。首先，我们正在构建一个解剖学上逼真的、位于第2/3层和第4层桶皮质的硅神经网络模型，以模拟强调多桶整合的信息处理。该模型使用的参数基于现有文献，我们计划将这些理论模拟的结果与执行纹理识别任务的小鼠的第二/3层钙成像实验的实验数据进行比较，从而使我们能够进一步测试和改进该模型。具体地说，双光子钙成像将使我们能够捕捉到大量第2/3层神经元的活动，并提取特定于任务的活动模式。将模型与数据进行匹配后，我们将拥有一个工具来探索网络架构及其关键组件，以及第2/3层可能采用的易于处理的编码方案。此外，该模型将提供关于第2/3层桶皮质的扰动如何影响信息编码的预测，以及我们可以通过实验测试的相应行为。这些结果将导致对大脑皮层回路的结构和大脑皮层编码的性质的新见解。

Neurons are the basic cellular units of the brain and are connected via synapses to form neural networks. One of the central questions in neuroscience is how particular tasks or "computations", are implemented by neural networks to generate behavior and how patterns of activity are stored during learning. In our work, we focus on mouse somatosensory cortex (known as barrel cortex), which is known to be associated to the mouse vibrissae system and helps rodents to sense the environment by discriminating between different textures and identifying objects. Object identification requires the integration of sensory information from multiple whiskers. However, where and how single whisker information is integrated to represent the palpated object is unclear. It is known that information from a single whisker enters cortex via layer 4 of the corresponding barrel column. Within the barrel column, whisker-specific activity is transmitted to more superficial layers like the layer 2/3. In contrast to layer 4, neurons in layer 2/3 of barrel cortex exhibit very low stimulus-evoked firing rates and low whisker selectivity. In addition, layer 2/3 is of particular interest for investigating temporal and spatial integration due to the aberrant lateral connectivity between the different barrel column. As part of my PhD project I want to combine two-photon calcium imaging of layer 2/3 and layer 4 of barrel cortex and network modelling to understand the integration of multi-whisker information in barrel cortex. To begin with, we are building an anatomically-realistic, in silico neuronal network model of layer 2/3 and layer 4 barrel cortex to simulate information processing with an emphasis on multi-barrel integration. The parameters used for the model are based on the available literature and we plan to compare results from these theoretical simulations with experimental data from calcium imaging experiments in layer 2/3 of mice performing a texture discrimination task, allowing us to test and refine the model further. Specifically, two-photon calcium imaging will allow us to capture the activity of a large population of layer 2/3 neurons and extract task-specific activity patterns. After fitting the model to the data we will have a tool to explore the network architecture and its critical components, as well as tractable coding schemes that layer 2/3 could be operating in. Furthermore, the model will provide predictions with respect to how perturbations of layer 2/3 barrel cortex could affect information coding and corresponding behaviours that we can test experimentally. These results will lead to new insights into the configuration of cortical circuits and the nature of cortical codes.