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基于已识别神经元信号输入结构的神经网络,该神经元发出即将发生碰撞的信号。

Neural network based on the input organization of an identified neuron signaling impending collision.

作者信息

Rind F C, Bramwell D I

机构信息

Division of Neurobiology, University of Newcastle-upon-Tyne, United Kingdom.

出版信息

J Neurophysiol. 1996 Mar;75(3):967-85. doi: 10.1152/jn.1996.75.3.967.

Abstract
  1. We describe a four-layered neural network (Fig. 1), based on the input organization of a collision signaling neuron in the visual system of the locust, the lobula giant movement detector (LGMD). The 250 photoreceptors ("P" units) in layer 1 are excited by any change in illumination, generated when an image edge passes over them. Layers 2 and 3 incorporate both excitatory and inhibitory interactions, and layer 4 consists of a single output element, equivalent to the locust LGMD. 2. The output element of the neural network, the "LGMD", responds directionally when challenged with approaching versus receding objects, preferring approaching objects (Figs. 2-4). The time course and shape of the "LGMD" response matches that of the LGMD (Fig. 4). Directionality is maintained with objects of various sizes and approach velocities. The network is tuned to direct approach (Fig. 5). The "LGMD" shows no directional selectivity for translatory motion at a constant velocity across the "eye", but its response increases with edge velocity (Figs. 6 and 9). 3. The critical image cues for a selective response to object approach by the "LGMD" are edges that change in extent or in velocity as they move (Fig. 7). Lateral inhibition is crucial to the selectivity of the "LGMD" and the selective response is abolished or else much reduced if lateral inhibition is taken out of the network (Fig. 7). We conclude that lateral inhibition in the neuronal network for the locust LGMD also underlies the experimentally observed critical image cues for its directional response. 4. Lateral inhibition shapes the velocity tuning of the network for objects moving in the X and Y directions without approaching the eye (see Fig. 1). As an edge moves over the eye at a constant velocity, a race occurs between the excitation that is caused by edge movement and which passes down the network and the inhibition that passes laterally. Excitation must win this race for units in layer 3 to reach threshold (Fig. 8). The faster the edge moves over the eye the more units in layer 3 reach threshold and pass excitation on to the "LGMD" (Fig. 9). 5. Lateral inhibition shapes the tuning of the network for objects moving in the Z direction, toward or away from the eye (see Fig. 1). As an object approaches the eye there is a buildup of excitation in the "LGMD" throughout the movement whereas the response to object recession is often brief, particularly for high velocities. During object motion, a critical race occurs between excitation passing down the network and inhibition directed laterally, excitation must win this race for the rapid buildup in excitation in the "LGMD" as seen in the final stages of object approach (Figs. 10-12). The buildup is eliminated if, during object approach, excitation cannot win this race (as happens when the spread of inhibition laterally takes < 1 ms Fig. 13, D and E). Taking all lateral inhibition away increases the "LGMD" response to object approach, but overall directional selectivity is reduced as there is also a lot of residual network excitation following object recession (Fig. 13B). 6. Directional selectivity for rapidly approaching objects is further enhanced at the level of the "LGMD" by the timing of a feed-forward, inhibitory loop onto the "LGMD", activated when a large number of receptor units are excited in a short time. The inhibitory loop is activated at the end of object approach, truncating the excitatory "LGMD" response after approach has ceased, but at the initiation of object recession (*Fig. 2, 3, and 13). Eliminating the feed-forward, inhibitory loop prolongs the "LGMD" response to both receding and approaching objects (Fig. 13F).
摘要
  1. 我们描述了一种四层神经网络(图1),其基于蝗虫视觉系统中碰撞信号神经元——小叶巨型运动检测器(LGMD)的输入结构。第1层中的250个光感受器(“P”单元)会因光照变化而兴奋,当图像边缘经过它们时就会产生这种变化。第2层和第3层包含兴奋性和抑制性相互作用,第4层由单个输出元件组成,等同于蝗虫的LGMD。

  2. 神经网络的输出元件“LGMD”,在面对接近和远离的物体时会产生定向反应,更倾向于接近的物体(图2 - 4)。“LGMD”反应的时间进程和形状与LGMD的相匹配(图4)。对于各种大小和接近速度的物体,方向性都得以保持。该网络被调整为对直接接近做出反应(图5)。“LGMD”对于以恒定速度在“眼睛”上平移运动没有方向选择性,但其反应会随着边缘速度的增加而增强(图6和9)。

  3. “LGMD”对物体接近做出选择性反应的关键图像线索是那些在移动时范围或速度发生变化的边缘(图7)。侧向抑制对于“LGMD”的选择性至关重要,如果从网络中去除侧向抑制,选择性反应就会被消除或大大降低(图7)。我们得出结论,蝗虫LGMD神经元网络中的侧向抑制也是其定向反应实验观察到的关键图像线索的基础。

  4. 侧向抑制塑造了网络对在X和Y方向移动而不接近眼睛的物体的速度调谐(见图1)。当边缘以恒定速度在眼睛上移动时,由边缘移动引起并向下传递通过网络的兴奋与侧向传递的抑制之间会展开一场竞争。兴奋必须在这场竞争中获胜,第3层中的单元才能达到阈值(图8)。边缘在眼睛上移动得越快,第3层中达到阈值并将兴奋传递给“LGMD”的单元就越多(图9)。

  5. 侧向抑制塑造了网络对在Z方向朝着或远离眼睛移动的物体的调谐(见图1)。当物体接近眼睛时,在整个运动过程中“LGMD”中会有兴奋的积累,而对物体后退的反应通常很短暂,特别是对于高速情况。在物体运动期间,在向下传递通过网络的兴奋与侧向指向的抑制之间会发生一场关键竞争,在物体接近的最后阶段,兴奋必须在这场竞争中获胜,才能在“LGMD”中实现兴奋的快速积累(图10 - 12)。如果在物体接近期间兴奋无法在这场竞争中获胜(如当侧向抑制的扩散用时小于1毫秒时,见图13,D和E),这种积累就会被消除。去除所有侧向抑制会增加“LGMD”对物体接近的反应,但总体方向选择性会降低,因为在物体后退后也会有大量的残余网络兴奋(图13B)。

  6. 通过前馈抑制回路在“LGMD”层面上对快速接近物体的方向选择性进一步增强,当大量受体单元在短时间内被激发时,该抑制回路会被激活。抑制回路在物体接近结束时被激活,在接近停止后截断兴奋性的“LGMD”反应,但在物体开始后退时(*图2、3和13)。去除前馈抑制回路会延长“LGMD”对后退和接近物体的反应(图13F)。

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