The theory of attractor neural networks has been influential in our understanding of the neural processes underlying spatial, declarative, and episodic memory. Many theoretical studies focus on the inherent properties of an attractor, such as its structure and capacity. Relatively little is known about how an attractor neural network responds to external inputs, which often carry conflicting information about a stimulus. In this paper we analyze the behavior of an attractor neural network driven by two conflicting external inputs. Our focus is on analyzing the emergent properties of the megamap model, a quasi-continuous attractor network in which place cells are flexibly recombined to represent a large spatial environment. In this model, the system shows a sharp transition from the winner-take-all mode, which is characteristic of standard continuous attractor neural networks, to a combinatorial mode in which the equilibrium activity pattern combines embedded attractor states in response to conflicting external inputs. We derive a numerical test for determining the operational mode of the system a priori. We then derive a linear transformation from the full megamap model with thousands of neurons to a reduced 2-unit model that has similar qualitative behavior. Our analysis of the reduced model and explicit expressions relating the parameters of the reduced model to the megamap elucidate the conditions under which the combinatorial mode emerges and the dynamics in each mode given the relative strength of the attractor network and the relative strength of the two conflicting inputs. Although we focus on a particular attractor network model, we describe a set of conditions under which our analysis can be applied to more general attractor neural networks.

中文翻译：

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吸引子神经网络对冲突外部输入的响应分析。


吸引子神经网络理论对我们理解空间记忆、陈述性记忆和情景记忆背后的神经过程产生了影响。许多理论研究都集中在吸引子的固有属性上，例如其结构和容量。关于吸引子神经网络如何响应外部输入，人们知之甚少，外部输入通常携带有关刺激的冲突信息。在本文中，我们分析了由两个相互冲突的外部输入驱动的吸引子神经网络的行为。我们的重点是分析巨型地图模型的新兴属性，这是一种准连续吸引子网络，其中位置单元灵活地重新组合以表示大型空间环境。在该模型中，系统表现出从赢家通吃模式（标准连续吸引子神经网络的特征）到组合模式的急剧转变，在组合模式中，平衡活动模式结合嵌入的吸引子状态以响应冲突的外部输入。我们推导了一个数值测试来先验地确定系统的运行模式。然后，我们从具有数千个神经元的完整大地图模型到具有类似定性行为的简化 2 单元模型进行线性变换。我们对简化模型和将简化模型的参数与大地图相关的显式表达式的分析阐明了组合模式出现的条件以及给定吸引子网络的相对强度和两种冲突的相对强度的情况下每种模式的动态输入。尽管我们专注于特定的吸引子网络模型，但我们描述了一组条件，在这些条件下我们的分析可以应用于更一般的吸引子神经网络。