Learning Times Required to Identify the Stimulated Position and Shortening of Propagation Path by Hebb’s Rule in Neural Network
Learning Times Required to Identify the Stimulated Position and Shortening of Propagation Path by Hebb’s Rule in Neural Network
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DOI：
10.3934/neuroscience.2017.4.238
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
S. Sakuma;Y. Mizuno-Matsumoto;Y. Nishitani;S. Tamura
中科院分区：
文献类型：
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作者：
S. Sakuma;Y. Mizuno-Matsumoto;Y. Nishitani;S. Tamura
为了加深对人类大脑的理解，许多研究人员创造了一种分析神经数据的新方法。在之前的许多研究中，研究人员基于神经元放电模式，从宏观的角度研究了神经网络。相反，我们对神经网络进行了局部研究，以了解它们的通信策略。为了了解大脑中的信息处理过程，我们在一个9 × 9的二维神经网络中模拟了神经网络的放电活动，以分析脉冲行为。在本研究中，我们使用了两种学习过程。作为主要的学习过程，我们实现了识别受刺激位置的学习过程。作为辅助算法，我们实现了改变神经元间权值的Hebb学习规则。预设了三个有发射和接收的通道，每个通道有不同的距离和方向。当所有三个通道都成功地识别出接收神经元组的源刺激时，就被视为整体成功，学习也被称为成功。此外，为了观察第二个学习过程的效果，我们阐明了每个通道类型中必要学习时间的平均值，并比较了每个通道中第一次试验和总体成功试验的发射传播时间。我们发现学习后的发射路径比学习前的发射路径短。因此，我们推断赫布法则有助于缩短发射路径。因此，Hebb规则有助于加速神经网络中的通信。
To deepen the understanding of the human brain, many researchers have created a new way of analyzing neural data. In many previous studies, researchers have examined neural networks from a macroscopic point of view, based on neuronal firing patterns. On the contrary, we have studied neural networks locally, in order to understand their communication strategies. To understand information processing in the brain, we simulated the firing activities of neural networks in a 9 × 9 two-dimensional neural network to analyze spike behavior. In this research study, we used two kinds of learning processes. As the main learning process, we implemented the learning process to identify the stimulated position. As the subsidiary one, we implemented Hebb’s learning rule which changes weight between neurons. Three channels with transmission and reception were preset, each of which has a different distance and direction. When all three channels succeeded in identifying the source stimulation in the receiving neuron group, it was regarded as an overall success and the learning was termed as successful. Furthermore, in order to see the effect of the second learning procedure, we elucidated the average of necessary learning times in each channel type and compared the firing propagation time of the first trial and an overall successful trial, in each channel. We found that the firing path after learning is shorter than the firing path before learning. Therefore, we deduced that Hebb’s rule contributes to shortening the firing path. Thus, Hebb’s rule contributes to speeding up communication in a neuronal network.