NEUROSCIENCE Neural population control via deep image synthesis
NEUROSCIENCE Neural population control via deep image synthesis
复制标题
DOI：
10.1126/science.aav9436
复制
发表时间：
2019-05-03
期刊：
影响因子：
56.9
通讯作者：
DiCarlo, James J.
中科院分区：
文献类型：
--
作者：
Bashivan, Pouya;Kar, Kohitij;DiCarlo, James J.
特定的深人造神经网络（ANN）是当今灵长类动物脑视觉流的最准确的模型。使用ANN驱动的图像合成方法，我们发现可以将发光的功率模式（即图像）应用于灵长类动物视网膜，以预见的是将目标V4神经位点的尖峰活动推到自然存在的水平之外。这种方法虽然尚不完美，但可以实现对V4神经部位的整个人群的活性状态的前所未有的独立控制，即使是那些具有重叠的接收场的人群。这些结果表明，在当今的ANN模型中嵌入的知识如何用于在神经元级别的分辨率下无创设置所需的内部大脑状态，并建议更准确的ANN模型会产生更准确的控制。
Particular deep artificial neural networks (ANNs) are today's most accurate models of the primate brain's ventral visual stream. Using an ANN-driven image synthesis method, we found that luminous power patterns (i.e., images) can be applied to primate retinae to predictably push the spiking activity of targeted V4 neural sites beyond naturally occurring levels. This method, although not yet perfect, achieves unprecedented independent control of the activity state of entire populations of V4 neural sites, even those with overlapping receptive fields. These results show how the knowledge embedded in today's ANN models might be used to noninvasively set desired internal brain states at neuron-level resolution, and suggest that more accurate ANN models would produce even more accurate control.