Brain-Inspired Communication Technologies: Information Networks with Continuing Internal Dynamics and Fluctuation
Brain-Inspired Communication Technologies: Information Networks with Continuing Internal Dynamics and Fluctuation
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DOI：
10.1587/transcom.e98.b.153
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Jun-nosuke Teramae;N. Wakamiya
中科院分区：
文献类型：
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作者：
Jun-nosuke Teramae;N. Wakamiya
大脑中的概要计算是在复杂、异质和超大规模的神经元网络中实现的。大约有1,000亿个神经元通过动作电位相互通信，这些动作电位被称为“尖峰放电”，从每个神经元传递给成千上万的其他神经元。网络中这些尖峰列车的反复整合和联网最终形成了我们认知、感知、规划和运动控制的实质。除了对神经网络机制的传统观点外，最近在实验和理论神经科学方面的快速发展揭示了大脑是一个动态系统，它主动地处理环境信息，而不是被动地处理环境信息。大脑利用内部动力来实现我们弹性和高效的感知和行为。在本文中，通过考虑大脑和信息网络的异同，我们讨论了信息网络具有类似大脑的持续内部动力学的可能性。我们期望所提出的网络能够有效地实现上下文相关的网内处理。通过介绍神经科学关于大脑动力学的最新发现，我们认为
SUMMARY Computation in the brain is realized in complicated, heterogeneous, and extremely large-scale network of neurons. About a hundred billion neurons communicate with each other by action potentials called “spike firings” that are delivered to thousands of other neurons from each. Repeated integration and networking of these spike trains in the network finally form the substance of our cognition, perception, planning, and motor control. Beyond conventional views of neural network mechanisms, recent rapid advances in both experimental and theoretical neuroscience unveil that the brain is a dynamical system to actively treat environmental information rather passively process it. The brain utilizes internal dynamics to realize our resilient and efficient perception and behavior. In this paper, by considering similarities and differences of the brain and information networks, we discuss a possibility of information networks with brainlike continuing internal dynamics. We expect that the proposed networks efficiently realize context-dependent in-network processing. By introducing recent findings of neuroscience about dynamics of the brain, we argue