The study presents a bio-inspired chaos sensor based on the perceptron neural network. After training, the sensor on perceptron, having 50 neurons in the hidden layer and 1 neuron at the output, approximates the fuzzy entropy of short time series with high accuracy with a determination coefficient R2 ~ 0.9. The Hindmarsh-Rose spike model was used to generate time series of spike intervals, and datasets for training and testing the perceptron. The selection of the hyperparameters of the perceptron model and the estimation of the sensor accuracy were performed using the K-block cross-validation method. Even for a hidden layer with 1 neuron, the model approximates the fuzzy entropy with good results and the metric R2 ~ 0.5-0.8. In a simplified model with 1 neuron and equal weights in the first layer, the principle of approximation is based on the linear transformation of the average value of the time series into the entropy value. The bio-inspired chaos sensor model based on an ensemble of neurons is able to dynamically track the chaotic behavior of a spiked biosystem and transmit this information to other parts of the bio-system for further processing. The study will be useful for specialists in the field of computational neuroscience.

翻译：本研究提出了一种基于感知器神经网络的仿生混沌传感器。经训练后，该传感器（隐藏层含50个神经元，输出层含1个神经元）能以高达R²~0.9的决定系数精确逼近短时间序列的模糊熵。采用Hindmarsh-Rose尖峰模型生成尖峰间隔时间序列，并构建用于感知器训练与测试的数据集。通过K-块交叉验证方法实现了感知器模型超参数的选取及传感器精度的评估。即便隐藏层仅含1个神经元，模型仍能获得R²~0.5-0.8的优异模糊熵逼近指标。在简化的单神经元等权值首层模型中，逼近原理基于时间序列均值至熵值的线性变换。基于神经元集群的仿生混沌传感器模型可动态追踪尖峰生物系统的混沌行为，并将该信息传递至生物系统其他部分进行后续处理。本研究将为计算神经科学领域的专业人士提供参考。