摘要
神经元间相关性的研究是深入理解神经元集群信息传递与编码机理的基础.首先,采用状态空间对数线性模型初步估计神经元间的动态相关性,针对输入数据特征对模型估计值置信区间的影响,提出了通过筛选数据优化置信区间来提高模型估计精度.然后,通过提取动态相关性的特征,分析神经元间相关性在不同朝向光栅刺激下的动态特性,进而研究了神经元间同步作用对视觉刺激信息的编码作用.最后,在麻醉的Long Evens(LE)大鼠初级视觉皮层上进行了实验验证.结果表明:采用剔除发放率偏小的序列的数据筛选方案能够有效地提高模型估计值的精度;神经元间的锋电位同步作用对朝向光栅刺激信息具有一定的编码作用.
The research on correlation between neurons is the foundation to understand the mechanism of information transmission and coding of neuronal population. A novel method called state-space log-linear model was used to estimate the dynamic correlation between paired neurons,and data sieving methods were proposed to improve the accuracy of model results for the effects of input data characteristics on the confidence interval of the model estimated values. By extracting the characteristics of dynamic correlation curves,changing characteristics of paired neurons＇ correlation was analyzed and then the effect on information coding of visual stimulus from synchronization between paired neurons was studied. Experimental verification was carried out in the primary visual cortex of anesthetized rats. The results show that： the accuracy of the estimated value of the model can be improved by removing the data with small firing rates,and synchronization between paired neurons encodes the information of different grating stimuli.
出处
《郑州大学学报（工学版）》
CAS
北大核心
2015年第1期1-5,共5页
Journal of Zhengzhou University（Engineering Science）
基金
国家自然科学基金资助项目(U1304602)
河南省重点科技攻关计划资助项目(122102210102)
