摘要翻译：
利用非线性Schroedinger波动方程提出了一个理论量子脑模型。该模型提出，存在一个量子过程，介导神经晶格（经典大脑）的集体反应。该模型用于解释跟踪运动目标时的眼动。利用递归量子神经网络(RQNN)模拟量子大脑模型，观察到两个非常有趣的现象。首先，当眼睛传感器数据在经典大脑中处理时，量子大脑中会触发一个波包。这个波包像粒子一样运动。其次，当眼睛跟踪一个固定目标时，这个波包不是以连续的方式运动，而是以离散的方式运动。这个结果让人想起眼睛的扫视运动，包括“跳跃”和“休息”。然而，当眼睛必须跟踪动态轨迹时，这样的扫视运动与平滑的追逐运动交织在一起。从某种意义上说，这是第一个解释静态场景下眼动实验观察的理论模型。结果发现，与经典的目标建模方案如卡尔曼滤波相比，预测是非常精确和有效的。
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英文标题：
《Quantum Brain: A Recurrent Quantum Neural Network Model to Describe Eye
  Tracking of Moving Targets》
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作者：
Laxmidhar Behera, Indrani Kar and Avshalom Elitzur
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最新提交年份：
2004
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Neurons and Cognition        神经元与认知
分类描述：Synapse, cortex, neuronal dynamics, neural network, sensorimotor control, behavior, attention
突触，皮层，神经元动力学，神经网络，感觉运动控制，行为，注意
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一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  A theoretical quantum brain model is proposed using a nonlinear Schroedinger wave equation. The model proposes that there exists a quantum process that mediates the collective response of a neural lattice (classical brain). The model is used to explain eye movements when tracking moving targets. Using a Recurrent Quantum Neural Network(RQNN) while simulating the quantum brain model, two very interesting phenomena are observed. First, as eye sensor data is processed in a classical brain, a wave packet is triggered in the quantum brain. This wave packet moves like a particle. Second, when the eye tracks a fixed target, this wave packet moves not in a continuous but rather in a discrete mode. This result reminds one of the saccadic movements of the eye consisting of 'jumps' and 'rests'. However, such a saccadic movement is intertwined with smooth pursuit movements when the eye has to track a dynamic trajectory. In a sense, this is the first theoretical model explaining the experimental observation reported concerning eye movements in a static scene situation. The resulting prediction is found to be very precise and efficient in comparison to classical objective modeling schemes such as the Kalman filter.
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PDF链接：
https://arxiv.org/pdf/q-bio/0407001