RI: Medium: An Analysis of the Consequences of Cortical Structure on Computation

皮层神经元网络被清楚地组织成层和柱状，但相对而言，人们对这些排列如何影响皮层计算知之甚少。为了解决这个问题，将使用512微电极阵列来刺激和记录数百个皮层神经元的活动。有了这个，皮层网络的输入和输出可以通过实验来控制。最近开发的一种理解神经计算的框架被称为“油藏计算”，它允许基于神经网络输入和输出的知识来量化神经网络的计算能力。512电极系统允许输入刺激定位到不同的皮质层或柱。类似地，可以通过记录不同的层或列来选择输出。因此，可以测量和比较层和列对计算的贡献以及它们执行的计算类型。这项研究的结果有望增加对大脑皮层如何获得其非凡计算能力的理解。此外，这项工作的结果有望为未来类脑计算电路的设计提供信息。为了促进科学教育和推广，将进一步开发和传播一个名为“Simbrain”的现有软件包。该软件包将允许高中及以上水平的学生了解皮层网络在执行计算时如何将输入转换为输出。将实现三个具体目标。首先，计算能力的测量必须基于随机背景刺激的实际水平。高电导状态是一种众所周知的现象，是由恒定的随机突触输入引起的，也是许多（特别是水库计算）神经回路模型的共同特征。512个电极阵列将用于提供背景刺激，以确定将提高计算性能的水平。其次，研究层的输入和输出位置。使用内核质量和vc维度量，计算能力和每一层单独或作为一个整体的作用将被评估。有可能一些层更强地泛化输入模式，而另一些层则将它们分开。这样就有可能剖析每一层的计算贡献。第三，同样的指标将被应用到一个柱的刺激中，而这个柱又会连接到另一个柱。这里将评估多个列的计算能力和作用，并且可以观察到由阵列直接激发的列和由其他列激发的列之间的计算差异。

Networks of cortical neurons are clearly organized into layers and columns, but relatively little is known about how these arrangements affect cortical computations. To approach this issue, a 512 micro-electrode array will be used to stimulate and record activity from hundreds of cortical neurons. With this, the inputs and outputs of a cortical network can be experimentally controlled. A recently-developed framework for understanding neural computation known as "reservoir computing" permits the computational power of neural networks to be quantified based on knowledge of their inputs and outputs. The 512-electrode system allows input stimulation to be localized to different cortical layers or columns. Similarly, outputs can be selected by recording from different layers or columns. Thus, the contributions of layers and columns to computations, and the types of computations they perform, can be measured and compared. The results of this research are expected to increase the understanding of how the cortex attains its remarkable computational power. In addition, the results of this work are expected to inform future designs of brain-like computing circuits. To promote scientific education and outreach, an existing software package called "Simbrain" will be further developed and disseminated. This package will allow students from high school level and above to understand how cortical networks transform inputs into outputs as they perform computations.Three specific aims will be pursued. First, the measurement of computational capacity must be based on realistic levels of random background stimulation. The high-conductance state is a well-known phenomenon in vivo resulting from constant random synaptic inputs, and is also a common feature in many (particularly reservoir computing) neural circuit models. The 512-electrode array will be used to deliver background stimulation to determine levels that will improve computational performance. Second, layer input and output locations will be studied. Using kernel quality and VC-dimension metrics, the computational power and role of each layer taken individually or as a whole will be assessed. It is possible that some layers more strongly generalize input patterns while others separate them. Thus it will be possible to dissect the computational contribution of each layer. Third, the same metrics will be applied to stimulation to one column which feeds to another. Here the computational power and role of multiple columns will be assessed, and any computational differences between columns directly stimulated by the array and columns stimulated by other columns can be observed.