Vinken K, Boix X, Kreiman G
Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Center for Brains, Minds and Machines, Cambridge, MA 02139, USA.
Sci Adv. 2020 Oct 14;6(42). doi: 10.1126/sciadv.abd4205. Print 2020 Oct.
Adaptation is a fundamental property of sensory systems that can change subjective experiences in the context of recent information. Adaptation has been postulated to arise from recurrent circuit mechanisms or as a consequence of neuronally intrinsic suppression. However, it is unclear whether intrinsic suppression by itself can account for effects beyond reduced responses. Here, we test the hypothesis that complex adaptation phenomena can emerge from intrinsic suppression cascading through a feedforward model of visual processing. A deep convolutional neural network with intrinsic suppression captured neural signatures of adaptation including novelty detection, enhancement, and tuning curve shifts, while producing aftereffects consistent with human perception. When adaptation was trained in a task where repeated input affects recognition performance, an intrinsic mechanism generalized better than a recurrent neural network. Our results demonstrate that feedforward propagation of intrinsic suppression changes the functional state of the network, reproducing key neurophysiological and perceptual properties of adaptation.
适应性是感觉系统的一种基本属性，它可以在近期信息的背景下改变主观体验。有人推测适应性源于循环回路机制，或者是神经元内在抑制的结果。然而，目前尚不清楚内在抑制本身是否能够解释除反应降低之外的其他效应。在此，我们检验这样一个假设：复杂的适应现象可以通过内在抑制在视觉处理的前馈模型中层层传递而出现。一个具有内在抑制的深度卷积神经网络捕捉到了适应的神经特征，包括新奇性检测、增强和调谐曲线偏移，同时产生了与人类感知一致的后效。当在重复输入会影响识别性能的任务中训练适应性时，一种内在机制比循环神经网络具有更好的泛化能力。我们的结果表明，内在抑制的前馈传播改变了网络的功能状态，再现了适应的关键神经生理和感知特性。
