Spiking patterns of a hippocampus model in electric fields

We develop a model of CA3 neurons embedded in a resistive array to mimic the effects of electric fields from a new perspective. Effects of DC and sinusoidal electric fields on firing patterns in CA3 neurons are investigated in this study. The firing patterns can be switched from no firing pattern to burst or from burst to fast periodic firing pattern with the increase of DC electric field intensity. It is also found that the firing activities are sensitive to the frequency and amplitude of the sinusoidal electric field. Different phase-locking states and chaotic firing regions are observed in the parameter space of frequency and amplitude. These findings are qualitatively in accordance with the results of relevant experimental and numerical studies. It is implied that the external or endogenous electric field can modulate the neural code in the brain. Furthermore, it is helpful to develop control strategies based on electric fields to control neural diseases such as epilepsy.     

不同手法及频率针刺神经电信号的编码特征提取

神经系统通过时间空间编码的形式表征外部刺激所包含的信息,针刺作为神经系统的一种外部刺激,使神经元产生丰富的放电模式. 为揭示针刺信息传导与作用规律,设计了不同手法以及不同频率的针刺作用足三里时,采集脊髓背根神经电信号的实验.通过对放电波形进行小波特征提取和类选算法把放电波形进行归类,然后提取出不同刺激下的时间编码和空间编码特征. 接着使用平均放电率和编码异质性系数量化了不同针刺刺激下编码的特征. 研究发现捻转法与提插法产生的神经电信号差异较大, 这种差异主要表现在针刺手法对于参与编码神经元的选择性,但对于不同频率的针刺作用, 这种编码选择性并不明显.此外,数据分析中还发现了神经系统对于针刺作用的适应性和饱和特性.     

Self-sustained firing activities of the cortical network with plastic rules in weak AC electrical fields

Both external and endogenous electrical fields widely exist in the environment of cortical neurons. The effects of a weak alternating current (AC) field on a neural network model with synaptic plasticity are studied. It is found that self-sustained rhythmic firing patterns, which are closely correlated with the cognitive functions, are significantly modified due to the self-organizing of the network in the weak AC field. The activities of the neural networks are affected by the synaptic connection strength, the external stimuli, and so on. In the presence of learning rules, the synaptic connections can be modulated by the external stimuli, which will further enhance the sensitivity of the network to the external signal. The properties of the external AC stimuli can serve as control parameters in modulating the evolution of the neural network.