论文标题
部分可观测时空混沌系统的无模型预测
Dynamic image recognition in a spiking neuron network supplied by astrocytes
论文作者
论文摘要
研究了由星形胶质细胞提供的尖峰神经元网络（SNN）的数学模型。星形胶质细胞是特定的脑细胞类型，这些类型不是电兴奋的，而是诱导神经元发射的化学调制。我们分析了星形胶质细胞如何以SNN的动态尖峰模式的形式对图像的影响。在较慢的时间尺度上，星形胶质细胞网络与尖峰神经元相互作用可以显着提高图像识别质量。尖峰动力学受噪声扭曲信息图像的影响。我们证明，星形胶质细胞的激活可以显着抑制SNN改善动态图像表示的噪声影响。
Mathematical model of spiking neuron network (SNN) supplied by astrocytes is investigated. The astrocytes are specific type of brain cells which are not electrically excitable but inducing chemical modulations of neuronal firing. We analyzed how the astrocytes influence on images encoded in the form of dynamic spiking pattern of the SNN. Serving at much slower time scale the astrocytic network interacting with the spiking neurons can remarkably enhance the image recognition quality. Spiking dynamics was affected by noise distorting the information image. We demonstrated that the activation of astrocyte can significantly suppress noise influence improving dynamic image representation by the SNN.
