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Information communication in brain based on memory loop neural circuit

机译:基于记忆回路神经电路的大脑信息交流

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We have developed a time-shift map of the brain, which shows/ visualizes signal such as EEG and MEG etc. is transmitted/ propagated at each brain part where in each frequency band action potential information of time-to-time is transmitted to and processed at exact remote portion. Assuming the memory is composed of loop neural circuit in the cerebral cortex, we have also shown a feasibility of self organization of loop neural circuit for memory, association, and abstraction, which is algorithmically realizable and able to account well the time-shift map. The generated sequence from the transmitting loop corresponding to the memory is a code by pseudo random sequence which is now popular in spread spectrum communication or mobile CDMA. We give in this paper how a memory content expressed by loop circuit shape is copied to blank area of cerebral cortex etc. using simulation of small scale loop circuit model with four neuron cells and back-propagation learning algorithm. The loop circuit self-organizing model seems to be reasonable in the sense of hardware realizable, having large number of codes corresponding to various memory contents, and able to access to other memories associatively. Thus, the model seems able to account well unifledly the information processing function of the brain.
机译:我们已经开发了大脑的时移图,该图显示/可视化了诸如EEG和MEG等信号在每个大脑部位的传输/传播,在每个大脑部位,每个时间的动作电位信息都被传输到在确切的远程部分进行处理。假设记忆由大脑皮层中的回路神经回路组成,我们还展示了回路神经回路自组织进行记忆,关联和抽象的可行性,该回路在算法上可实现并且能够很好地说明时移图。从发射回路产生的对应于存储器的序列是伪随机序列的代码,该伪随机序列现在在扩频通信或移动CDMA中很流行。我们通过使用具有四个神经元细胞的小规模回路电路模型和反向传播学习算法,模拟了如何将回路电路形状表示的存储内容复制到大脑皮层等的空白区域。就可实现的硬件而言,环路电路自组织模型似乎是合理的,具有与各种存储器内容相对应的大量代码,并且能够关联地访问其他存储器。因此,该模型似乎能够很好地说明大脑的信息处理功能。

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