Department of Biomedical Engineering, The University of Melbourne, Parkville 3052, Australia.
Faculty of Science, Engineering and Technology, Swinburne University of Technology, 3122 Hawthorn, Australia.
Rev Neurosci. 2023 Sep 20;35(3):243-258. doi: 10.1515/revneuro-2023-0052. Print 2024 Apr 25.
Computational modeling helps neuroscientists to integrate and explain experimental data obtained through neurophysiological and anatomical studies, thus providing a mechanism by which we can better understand and predict the principles of neural computation. Computational modeling of the neuronal pathways of the visual cortex has been successful in developing theories of biological motion processing. This review describes a range of computational models that have been inspired by neurophysiological experiments. Theories of local motion integration and pattern motion processing are presented, together with suggested neurophysiological experiments designed to test those hypotheses.
计算建模有助于神经科学家整合和解释通过神经生理学和解剖学研究获得的实验数据，从而提供了一种机制，使我们能够更好地理解和预测神经计算的原理。对视觉皮层神经元通路的计算建模在发展生物运动处理的理论方面取得了成功。本综述描述了一系列受神经生理学实验启发的计算模型。提出了局部运动整合和模式运动处理的理论，并提出了旨在检验这些假设的神经生理学实验建议。
