Adelaide Centre for Neuroscience Research, School of Medical Sciences, The University of Adelaide, Adelaide SA 5005, Australia.
J Neurosci. 2011 May 11;31(19):7141-4. doi: 10.1523/JNEUROSCI.0970-11.2011.
Flying insects engage in spectacular high-speed pursuit of targets, requiring visual discrimination of moving objects against cluttered backgrounds. As a first step toward understanding the neural basis for this complex task, we used computational modeling of insect small target motion detector (STMD) neurons to predict responses to features within natural scenes and then compared this with responses recorded from an identified STMD neuron in the dragonfly brain (Hemicordulia tau). A surprising model prediction confirmed by our electrophysiological recordings is that even heavily cluttered scenes contain very few features that excite these neurons, due largely to their exquisite tuning for small features. We also show that very subtle manipulations of the image cause dramatic changes in the response of this neuron, because of the complex inhibitory and facilitatory interactions within the receptive field.
飞行昆虫在追逐目标时会进行惊人的高速追逐，需要在杂乱的背景中识别移动的物体。为了深入了解这项复杂任务的神经基础，我们首先利用昆虫小目标运动探测器（STMD）神经元的计算模型来预测对自然场景中特征的反应，然后将其与在蜻蜓大脑中记录的一个已识别的 STMD 神经元的反应进行比较。我们的电生理记录证实了一个令人惊讶的模型预测，即即使是非常杂乱的场景也只包含很少的能激发这些神经元的特征，这主要是由于它们对小特征的高度精确调谐。我们还表明，由于在感受野内存在复杂的抑制和促进相互作用，对图像进行非常细微的操作都会导致这个神经元的反应发生巨大变化。
