人脑工作的复杂程度远比我们以前认识的高。神经回路的信息处理过程中以前很少关注的是时间因素。关于神经细胞这一复杂网络是怎样工作的，奥地利Graz技术大学的计算机科学家们此前提出了“液体计算（Liquid computing）”理论，近期他们刚刚完成了该理论的第一次验证，并进一步破解了神经元编码。这一研究由奥地利科学基金资助，有关成果发表在12月23日PLoS Biology上。
Graz技术大学理论计算科学中心的负责人Wolfgang Maass解释称，脑部逐步处理信息的理论已经过时，人的大脑不是按照流水线的方式工作。在加工信息时，时序可能比以前认为的更灵活。研究人员以水进行了形象的比喻，脑部工作就像在水池中投下了石头。这些石头导致的波纹没有立即消失，而是相互叠加并收集相关信息，例如有多少石头被投进来，他们有多大等等。主要的不同是，脑中的波纹在神经元网络中扩散的速度非常快而已。
随后，液体计算理论――其奠基性理论首先由瑞士神经学家Henry Markram和Graz技术大学的计算机科学家Maass共同提出――首次被实验验证。然而，对验证实验结果的评价和解读，则构成一个新的挑战。研究人员需要破解大量神经元以分散方式编码信息的编码机制。最后，借助自动模式识别方法，研究人员破解了这一编码机制。
研究人员表示，从计算科学理论中衍生的人脑计算组织结构的假说，通过神经生物学实验验证并最终得以证实，这项研究是计算科学和脑科学交叉成功案例之一。
推荐原始出处：
PLoS Biol 7(12): e1000260. doi:10.1371/journal.pbio.1000260
Distributed Fading Memory for Stimulus Properties in the Primary Visual Cortex
Danko Nikoli?1,2#*, Stefan H?usler3#, Wolf Singer1,2, Wolfgang Maass2,3
1 Department of Neurophysiology, Max-Planck-Institute for Brain Research, Frankfurt, Germany, 2 Frankfurt Institute for Advanced Studies (FIAS), Johann Wolfgang Goethe University, Frankfurt, Germany, 3 Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria
It is currently not known how distributed neuronal responses in early visual areas carry stimulus-related information. We made multielectrode recordings from cat primary visual cortex and applied methods from machine learning in order to analyze the temporal evolution of stimulus-related information in the spiking activity of large ensembles of around 100 neurons. We used sequences of up to three different visual stimuli (letters of the alphabet) presented for 100 ms and with intervals of 100 ms or larger. Most of the information about visual stimuli extractable by sophisticated methods of machine learning, i.e., support vector machines with nonlinear kernel functions, was also extractable by simple linear classification such as can be achieved by individual neurons. New stimuli did not erase information about previous stimuli. The responses to the most recent stimulus contained about equal amounts of information about both this and the preceding stimulus. This information was encoded both in the discharge rates (response amplitudes) of the ensemble of neurons and, when using short time constants for integration (e.g., 20 ms), in the precise timing of individual spikes (≤~20 ms), and persisted for several 100 ms beyond the offset of stimuli. The results indicate that the network from which we recorded is endowed with fading memory and is capable of performing online computations utilizing information about temporally sequential stimuli. This result challenges models assuming frame-by-frame analyses of sequential inputs.
