Mohan Vishnu, Rideaux Reuben
School of Psychology, The University of Sydney, Camperdown, Australia.
School of Psychology, The University of Sydney, Camperdown, Australia; Queensland Brain Institute, The University of Queensland, St Lucia, Australia.
Neural Netw. 2025 Nov;191:107834. doi: 10.1016/j.neunet.2025.107834. Epub 2025 Jul 6.
Motion processing is a key function for the survival of many organisms and is initially implemented in the primary visual cortex (V1) and the middle temporal area (V5/MT) of the primate visual cortex. Advances in machine learning approaches have led to the development of motion processing neural networks that have elucidated several aspects of this process. However, it remains unclear how adaptation, a canonical function of sensory processing, influences motion processing. In this study, we developed two recurrent neural networks to study motion processing: MotionNet-R, a baseline model, and AdaptNet, a model that employs adaptive mechanisms inspired by biological systems. Both networks were trained on natural image sequences to estimate motion vectors. We found that both networks developed response properties that resembled those of neurons found in areas V1 and MT, e.g., speed tuning, and AdaptNet recapitulated the motion aftereffect phenomenon (i.e., the waterfall illusion). We show that the emergent computational properties that implement the phenomenon in AdaptNet confirm previous theoretical hypotheses. Further, we compared the performance of the two networks and found that AdaptNet processed motion more efficiently, operationalized as reduced activation. While AdaptNet incurred reduced accuracy in response to prolonged constant input, it was both more accurate and sensitive in response to changes in motion input. These results are consistent with theoretical explanations of adaptation as a neural property that supports metabolic efficiency and increased sensitivity to change in the environment. Our findings provide novel insights into the neural mechanisms underlying motion adaptation and highlight the potential advantages of adaptive neural networks in modelling biological processes.
运动处理是许多生物体生存的关键功能，最初在灵长类动物视觉皮层的初级视觉皮层（V1）和颞中区（V5/MT）中实现。机器学习方法的进展导致了运动处理神经网络的发展，这些网络阐明了这一过程的几个方面。然而，尚不清楚适应（一种感觉处理的典型功能）如何影响运动处理。在本研究中，我们开发了两个循环神经网络来研究运动处理：基线模型MotionNet-R和采用受生物系统启发的自适应机制的模型AdaptNet。两个网络都在自然图像序列上进行训练以估计运动向量。我们发现两个网络都发展出了类似于在V1和MT区域发现的神经元的反应特性，例如速度调谐，并且AdaptNet重现了运动后效现象（即瀑布错觉）。我们表明，在AdaptNet中实现该现象的新兴计算特性证实了先前的理论假设。此外，我们比较了两个网络的性能，发现AdaptNet处理运动的效率更高，以减少激活来衡量。虽然AdaptNet在响应长时间恒定输入时准确性降低，但在响应运动输入变化时既更准确又更敏感。这些结果与将适应作为一种支持代谢效率和提高对环境变化敏感性的神经特性的理论解释一致。我们的发现为运动适应背后的神经机制提供了新的见解，并突出了自适应神经网络在模拟生物过程中的潜在优势。
