Adelaide Medical School, The University of Adelaide, Adelaide, SA 5000, Australia
Department of Bioengineering, Imperial College London, London SW7 2AZ, UK.
J Exp Biol. 2019 Sep 6;222(Pt 17):jeb207316. doi: 10.1242/jeb.207316.
Dragonflies pursue and capture tiny prey and conspecifics with extremely high success rates. These moving targets represent a small visual signal on the retina and successful chases require accurate detection and amplification by downstream neuronal circuits. This amplification has been observed in a population of neurons called small target motion detectors (STMDs), through a mechanism we term predictive gain modulation. As targets drift through the neuron's receptive field, spike frequency builds slowly over time. This increased likelihood of spiking or gain is modulated across the receptive field, enhancing sensitivity just ahead of the target's path, with suppression of activity in the remaining surround. Whilst some properties of this mechanism have been described, it is not yet known which stimulus parameters modulate the amount of response gain. Previous work suggested that the strength of gain enhancement was predominantly determined by the duration of the target's prior path. Here, we show that predictive gain modulation is more than a slow build-up of responses over time. Rather, the strength of gain is dependent on the velocity of a prior stimulus combined with the current stimulus attributes (e.g. angular size). We also describe response variability as a major challenge of target-detecting neurons and propose that the role of predictive gain modulation is to drive neurons towards response saturation, thus minimising neuronal variability despite noisy visual input signals.
蜻蜓以极高的成功率追逐和捕获微小的猎物和同种个体。这些移动的目标在视网膜上代表着一个小的视觉信号，成功的追逐需要下游神经元回路进行准确的检测和放大。这种放大在称为小目标运动检测器（STMD）的神经元群体中被观察到，通过我们称为预测增益调制的机制。当目标在神经元的感受野中漂移时，尖峰频率随时间缓慢增加。这种增加的尖峰频率或增益的可能性在感受野中被调制，在目标路径前方增强了敏感性，同时抑制了剩余周围的活动。虽然已经描述了这种机制的一些特性，但尚不清楚哪些刺激参数调节了响应增益的数量。之前的工作表明，增益增强的强度主要取决于目标先前路径的持续时间。在这里，我们表明，预测性增益调制不仅仅是随着时间的推移对响应的缓慢积累。相反，增益的强度取决于先前刺激的速度以及当前刺激的属性（例如角度大小）。我们还将响应的可变性描述为目标检测神经元的主要挑战，并提出预测性增益调制的作用是使神经元趋向于响应饱和，从而在存在噪声视觉输入信号的情况下最小化神经元的可变性。
