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位，通过突触连接形成神经网络。神经科学的核心问题之一是特定的任务或“计算”如何由神经网络实现以产生行为，以及在学习过程中如何存储活动模式。在我们的工作中，我们专注于小鼠躯体感觉皮层（称为桶皮层），已知它与小鼠触须系统相关，并帮助啮齿动物通过区分不同的纹理和识别物体来感知环境。物体识别需要整合来自多个触须的感觉信息。然而，在哪里以及如何整合单须信息来表示触诊对象还不清楚。已知来自单个须状物的信息经由相应的桶柱的层4进入皮质。在桶柱内，晶须比活性被传递到更表面的层，如2/3层。与第4层相比，桶状皮层第2/3层的神经元表现出非常低的刺激诱发的放电率和低晶须选择性。此外，层2/3是特别感兴趣的调查时间和空间的整合，由于不同的桶列之间的异常横向连接。作为我的博士项目的一部分，我想结合联合收割机的桶皮层的第2/3层和第4层的双光子钙成像和网络建模，以了解桶皮层的多须信息的整合。开始，我们正在建立一个解剖学上的现实，在硅片神经元网络模型的第2/3层和第4层桶皮层模拟信息处理，重点是多桶整合。用于模型的参数是基于现有的文献，我们计划将这些理论模拟的结果与执行纹理辨别任务的2/3层小鼠的钙成像实验的实验数据进行比较，使我们能够进一步测试和完善模型。具体来说，双光子钙成像将使我们能够捕获大量2/3层神经元的活动，并提取特定于任务的活动模式。在将模型与数据拟合之后，我们将有一个工具来探索网络架构及其关键组件，以及第2/3层可以运行的易于处理的编码方案。此外，该模型将提供关于2/3层桶皮质的扰动如何影响信息编码和相应行为的预测，我们可以通过实验进行测试。这些结果将导致新的见解的配置皮层电路和皮层代码的性质。

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.