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基于能量场的海马定位与导航模型研究
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Abstract:
海马体的位置细胞是大脑内部的空间定位系统的重要一环，为动物提供外部环境的认知地图。为实现认知地图构建及智能导航，本研究在Reduced Traub-Miles (RTM)模型的基础上，提出了一种基于神经能量编码的海马体位置细胞神经网络模型，基于位置细胞群的发放功率构建能量场，利用能量场梯度解决定位和导航任务。研究表明，模型能够有效地构建并更新认知地图，实现寻路任务，验证了位置细胞和突触在空间记忆中的重要性，证明了能量编码对认知活动的研究是有效的，为了解空间记忆的神经动力学机制提供了理论依据。
The place cells in the hippocampus constitute a vital component of the brain’s internal spatial positioning system, engaging in the construction of cognitive maps of the external environment for animals. This study introduces a place cell neural network model based on the Reduced Traub-Miles (RTM) model, employing a neural energy coding approach. It quantitatively describes the attenuation pattern of place cell cluster firing power, constructing an energy field model. The model utilizes energy field gradients to resolve positioning and navigation tasks. The result shows that the model can effectively construct and update the cognitive map to realize the way finding task. It verifies the importance of place cells and synapses in spatial memory, proves that energy coding is effective for the study of cognitive activities, and provides a theoretical basis for understanding the neurodynamic mechanism of spatial memory.
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