Biological cortical networks are potentially fully recurrent networks without any distinct output layer, where recognition may instead rely on the distribution of activity across its neurons. Because such biological networks can have rich dynamics, they are well-designed to cope with dynamical interactions of the types that occur in nature, while traditional machine learning networks may struggle to make sense of such data. Here we connected a simple model neuronal network (based on the 'linear summation neuron model' featuring biologically realistic dynamics (LSM), consisting of 10 of excitatory and 10 inhibitory neurons, randomly connected) to a robot finger with multiple types of force sensors when interacting with materials of different levels of compliance. Scope: to explore the performance of the network on classification accuracy. Therefore, we compared the performance of the network output with principal component analysis of statistical features of the sensory data as well as its mechanical properties. Remarkably, even though the LSM was a very small and untrained network, and merely designed to provide rich internal network dynamics while the neuron model itself was highly simplified, we found that the LSM outperformed these other statistical approaches in terms of accuracy.

翻译：生物皮质网络可能是完全重复的网络,没有任何不同的产出层,而承认则取决于其神经神经系统的活动分布。由于这种生物网络具有丰富的动态,因此它们设计得周密,能够应对自然中出现的类型的动态互动,而传统的机器学习网络可能难以理解这些数据。在这里,我们将一个简单的神经网络模型(基于以生物现实动态为特征的“线性对称神经模型”,由10个刺激性和10个抑制性神经元组成,随机连接)与带有多种类型威力传感器的机器人手指相连接,在与不同水平的合规材料进行互动时。范围:探索网络在分类准确性方面的性能。因此,我们将网络输出的性能与对感官数据的统计特征及其机械特性的主要组成部分分析进行比较。值得注意的是,即使LSM是一个非常小且未经训练的网络,而且仅仅旨在提供丰富的内部网络动态,而神经模型本身则高度简化,我们发现LSM在准确性方面优于这些其他统计方法。