记忆引导搜索的神经场模型
Neural field model of memory-guided search
摘要：
许多有机体可以记住它们在搜索过程中以前访问过的位置。视觉搜索实验表明，探索被引导远离这些位置，在找到隐藏目标之前减少搜索路径的重叠。我们开发和分析了一个两层的神经场模型，在搜索任务中编码的位置信息。位置编码层维持与搜索代理的当前位置相对应的碰撞吸引子，并且搜索由传播碰撞的速度输入来建模。记忆层维持着由波前限定的持续活动，波前的边缘会响应来自位置层的兴奋性输入而扩展。然后，搜索可以根据记住的位置进行偏置，从而影响位置层的速度输入。渐近技术被用来减少我们的模型的动态跟踪凸块的位置和前边界的低维方程组。性能进行比较，为不同的目标寻找任务。
英文摘要：
Many organisms can remember locations they have previously visited during a search. Visual search experiments have shown exploration is guided away from these locations, reducing the overlap of the search path before finding a hidden target. We develop and analyze a two-layer neural field model that encodes positional information during a search task. A position-encoding layer sustains a bump attractor corresponding to the searching agent's current location, and search is modeled by velocity input that propagates the bump. A memory layer sustains persistent activity bounded by a wave front, whose edges expand in response to excitatory input from the position layer. Search can then be biased in response to remembered locations, influencing velocity inputs to the position layer. Asymptotic techniques are used to reduce the dynamics of our model to a low-dimensional system of equations that track the bump position and front boundary. Performance is compared for different target-finding tasks.
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