When similar visual stimuli are presented binocularly to both eyes, one perceives a fused single image. However, when the two stimuli are distinct, one does not perceive a single image; instead, one perceives binocular rivalry. That is, one perceives one of the stimulated patterns for a few seconds, then the other for few seconds, and so on – with random transitions between the two percepts. Most theoretical studies focus on rivalry, with few considering the coexistence of fusion and rivalry. Here we develop three distinct computational neuronal network models which capture binocular rivalry with realistic stochastic properties, fusion, and the hysteretic transition between. Each is a conductance-based point neuron model, which is multi-layer with two ocular dominance columns (L & R) and with an idealized “ring” architecture where the orientation preference of each neuron labels its location on a ring. In each model, the primary mechanism initiating binocular rivalry is cross-column inhibition, with firing rate adaptation governing the temporal properties of the transitions between percepts. Under stimulation by similar visual patterns, each of three models uses its own mechanism to overcome cross-column inhibition, and thus to prevent rivalry and allow the fusion of similar images: The first model uses cross-column feedforward inhibition from the opposite eye to “shut off” the cross-column feedback inhibition; the second model “turns on” a second layer of monocular neurons as a parallel pathway to the binocular neurons, rivaling out of phase with the first layer, and together these two pathways represent fusion; and the third model uses cross-column excitation to overcome the cross-column inhibition and enable fusion. Thus, each of the idealized ring models depends upon a different mechanism for fusion that might emerge as an underlying mechanism present in real visual cortex.

中文翻译：

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双目竞争和融合的环形模型。

当双眼向双眼呈现相似的视觉刺激时，一个人会感觉到融合的单个图像。但是，当两种刺激是不同的时，一个不会感知到单个图像；反之，则不会。取而代之的是，人们感觉到了双眼的竞争。也就是说，一个人感知一种刺激模式数秒钟，然后感知另一种刺激模式几秒钟，依此类推-在两种感知之间随机转换。大多数理论研究集中于竞争，很少考虑融合与竞争的共存。在这里，我们开发了三种截然不同的计算神经元网络模型，它们捕获了具有现实随机特性，融合以及两者之间的滞后过渡的双目竞争。每个模型都是基于电导的点神经元模型，该模型是多层的，具有两个眼优势列（L和 R）并采用理想的“环”结构，其中每个神经元的方向偏好会标记其在环上的位置。在每个模型中，引发双眼竞争的主要机制是跨列抑制，而射速匹配则控制着感知之间过渡的时间特性。在相似的视觉模式刺激下，这三个模型各自使用其自身的机制来克服跨列抑制，从而防止竞争并允许相似图像融合：第一个模型使用从另一只眼睛到“关闭“跨列反馈抑制”；第二种模型“打开”第二层单眼神经元，作为与双眼神经元的平行途径，与第一层异相竞争，这两种途径共同代表融合。第三个模型使用跨列激励来克服跨列抑制并实现融合。因此，每个理想化的环模型都依赖于不同的融合机制，而融合机制可能会作为真实视觉皮层中存在的潜在机制出现。