Durstewitz Daniel, Deco Gustavo
Centre for Theoretical and Computational Neuroscience, University of Plymouth, Portland Square, Drake Circus, Plymouth PL4 8AA, UK.
Eur J Neurosci. 2008 Jan;27(1):217-27. doi: 10.1111/j.1460-9568.2007.05976.x. Epub 2007 Dec 17.
Neural responses are most often characterized in terms of the sets of environmental or internal conditions or stimuli with which their firing rate [corrected]increases or decreases are correlated [corrected] Their transient (nonstationary) temporal profiles of activity have received comparatively less attention. Similarly, the computational framework of attractor neural networks puts most emphasis on the representational or computational properties of the stable states of a neural system. Here we review a couple of neurophysiological observations and computational ideas that shift the focus to the transient dynamics of neural systems. We argue that there are many situations in which the transient neural behaviour, while hopping between different attractor states or moving along 'attractor ruins', carries most of the computational and/or behavioural significance, rather than the attractor states eventually reached. Such transients may be related to the computation of temporally precise predictions or the probabilistic transitions among choice options, accounting for Weber's law in decision-making tasks. Finally, we conclude with a more general perspective on the role of transient dynamics in the brain, promoting the view that brain activity is characterized by a high-dimensional chaotic ground state from which transient spatiotemporal patterns (metastable states) briefly emerge. Neural computation has to exploit the itinerant dynamics between these states.
神经反应通常根据与它们的放电率[校正后]增加或减少相关的环境或内部条件或刺激集来表征[校正后]。它们的瞬态（非平稳）时间活动轮廓受到的关注相对较少。同样，吸引子神经网络的计算框架最强调神经系统稳定状态的表征或计算属性。在这里，我们回顾一些神经生理学观察结果和计算思想，这些将焦点转移到神经系统的瞬态动力学上。我们认为，在许多情况下，瞬态神经行为在不同吸引子状态之间跳跃或沿着“吸引子遗迹”移动时，承载了大部分计算和/或行为意义，而不是最终达到的吸引子状态。这种瞬态可能与时间精确预测的计算或选择选项之间的概率转换有关，这在决策任务中解释了韦伯定律。最后，我们以更广泛的视角总结瞬态动力学在大脑中的作用，支持这样一种观点，即大脑活动的特征是高维混沌基态，瞬态时空模式（亚稳态）从中短暂出现。神经计算必须利用这些状态之间的巡回动力学。
