Fitting of dynamic recurrent neural network models to sensory stimulus-response data.
Fitting of dynamic recurrent neural network models to sensory stimulus-response data.
复制标题
动态循环神经网络模型与感觉刺激响应数据的拟合。
DOI：
10.1007/s10867-018-9501-z
复制
发表时间：
2018
影响因子：
1.8
通讯作者：
Zhang,Kechen
中科院分区：
文献类型：
--
作者：
Doruk,ROzgur;Zhang,Kechen
我们提出了一项针对感觉神经元模型拟合的理论研究。由于缺乏连续数据，传统的神经网络训练方法不适用于这个问题。虽然刺激可以被认为是一个平滑的时间依赖变量，但相关的响应将是一组没有幅度信息的神经尖峰时间（大致是连续动作电位峰值的时刻）。通过使用最大似然估计方法，可以将递归神经网络模型拟合到这样的刺激-响应数据对，其中似然函数来自神经尖峰的泊松统计。递归动态神经元网络模型的通用近似特性使我们能够描述具有任何所需数量神经元的实际感觉神经网络的兴奋-抑制特性。刺激数据由具有固定幅度和频率但随机激发相位的相位余弦傅立叶级数生成。应用各种振幅值、刺激分量大小和样本大小，以检查刺激对识别过程的影响。结果以表格和图形的形式在本文的结尾。此外，为了证明本研究的成功，一项研究涉及相同的模型，标称参数和刺激结构，以及另一项研究，工作在不同的模型进行比较，本研究。
We present a theoretical study aiming at model fitting for sensory neurons. Conventional neural network training approaches are not applicable to this problem due to lack of continuous data. Although the stimulus can be considered as a smooth time-dependent variable, the associated response will be a set of neural spike timings (roughly the instants of successive action potential peaks) that have no amplitude information. A recurrent neural network model can be fitted to such a stimulus-response data pair by using the maximum likelihood estimation method where the likelihood function is derived from Poisson statistics of neural spiking. The universal approximation feature of the recurrent dynamical neuron network models allows us to describe excitatory-inhibitory characteristics of an actual sensory neural network with any desired number of neurons. The stimulus data are generated by a phased cosine Fourier series having a fixed amplitude and frequency but a randomly shot phase. Various values of amplitude, stimulus component size, and sample size are applied in order to examine the effect of the stimulus to the identification process. Results are presented in tabular and graphical forms at the end of this text. In addition, to demonstrate the success of this research, a study involving the same model, nominal parameters and stimulus structure, and another study that works on different models are compared to that of this research.