Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
Sci Rep. 2020 Jul 9;10(1):11360. doi: 10.1038/s41598-020-68169-x.
Recent experiments have revealed a hierarchy of time scales in the visual cortex, where different stages of the visual system process information at different time scales. Recurrent neural networks are ideal models to gain insight in how information is processed by such a hierarchy of time scales and have become widely used to model temporal dynamics both in machine learning and computational neuroscience. However, in the derivation of such models as discrete time approximations of the firing rate of a population of neurons, the time constants of the neuronal process are generally ignored. Learning these time constants could inform us about the time scales underlying temporal processes in the brain and enhance the expressive capacity of the network. To investigate the potential of adaptive time constants, we compare the standard approximations to a more lenient one that accounts for the time scales at which processes unfold. We show that such a model performs better on predicting simulated neural data and allows recovery of the time scales at which the underlying processes unfold. A hierarchy of time scales emerges when adapting to data with multiple underlying time scales, underscoring the importance of such a hierarchy in processing complex temporal information.
最近的实验揭示了视觉皮层中的时间尺度层次结构，其中视觉系统的不同阶段以不同的时间尺度处理信息。递归神经网络是深入了解此类时间尺度层次结构如何处理信息的理想模型，已广泛用于机器学习和计算神经科学中对时间动态的建模。然而，在将此类模型作为神经元群体的发放率的离散时间逼近进行推导时，通常会忽略神经元过程的时间常数。学习这些时间常数可以使我们了解大脑中时间过程的基础时间尺度，并增强网络的表达能力。为了研究自适应时间常数的潜力，我们将标准逼近与更宽松的逼近进行了比较，该逼近考虑了过程展开的时间尺度。我们表明，该模型在预测模拟神经数据方面表现更好，并允许恢复基础过程展开的时间尺度。当适应具有多个基础时间尺度的数据时，时间尺度层次结构就会出现，这突显了这种层次结构在处理复杂时间信息方面的重要性。
