Mosqueiro Thiago S, Maia Leonardo P
Instituto de Física de São Carlos, Universidade de São Paulo, 13560-970 São Carlos, SP, Brazil.
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Jul;88(1):012712. doi: 10.1103/PhysRevE.88.012712. Epub 2013 Jul 11.
Spontaneous neural activity has been increasingly recognized as a subject of key relevance in neuroscience. It exhibits nontrivial spatiotemporal structure reflecting the organization of the underlying neural network and has proved to be closely intertwined with stimulus-induced activity patterns. As an additional contribution in this regard, we report computational studies that strongly suggest that a stimulus-free feature rules the behavior of an important psychophysical measure of the sensibility of a sensory system to a stimulus, the so-called dynamic range. Indeed in this paper we show that the entropy of the distribution of avalanche lifetimes (information efficiency, since it can be interpreted as the efficiency of the network seen as a communication channel) always accompanies the dynamic range in the benchmark model for sensory systems. Specifically, by simulating the Kinouchi-Copelli (KC) model on two broad families of model networks, we generically observed that both quantities always increase or decrease together as functions of the average branching ratio (the control parameter of the KC model) and that the information efficiency typically exhibits critical optimization jointly with the dynamic range (i.e., both quantities are optimized at the same value of that control parameter, that turns out to be the critical point of a nonequilibrium phase transition). In contrast with the practice of taking power laws to identify critical points in most studies describing measured neuronal avalanches, we rely on data collapses as more robust signatures of criticality to claim that critical optimization may happen even when the distribution of avalanche lifetimes is not a power law, as suggested by a recent experiment. Finally, we note that the entropy of the size distribution of avalanches (information capacity) does not always follow the dynamic range and the information efficiency when they are critically optimized, despite being more widely used than the latter to describe the computational capabilities of a neural network. This strongly suggests that dynamical rules allowing a proper temporal matching of the states of the interacting neurons is the key for achieving good performance in information processing, rather than increasing the number of available units.
自发神经活动在神经科学中已日益被视为一个具有关键相关性的主题。它展现出反映底层神经网络组织的复杂时空结构，并已被证明与刺激诱发的活动模式紧密交织。作为这方面的一项额外贡献，我们报告了计算研究，这些研究有力地表明，一种无刺激特征支配着感官系统对刺激敏感度的一项重要心理物理学测量指标——即所谓的动态范围——的行为。事实上，在本文中我们表明，雪崩寿命分布的熵（信息效率，因为它可被解释为视为通信通道的网络的效率）在感官系统的基准模型中总是伴随着动态范围。具体而言，通过在两大类模型网络上模拟Kinouchi - Copelli（KC）模型，我们普遍观察到这两个量总是作为平均分支比（KC模型的控制参数）的函数一起增加或减少，并且信息效率通常与动态范围共同呈现临界优化（即，这两个量在该控制参数的相同值处达到优化，该值结果是一个非平衡相变的临界点）。与大多数描述实测神经元雪崩的研究中采用幂律来识别临界点的做法不同，我们依赖数据塌缩作为临界性的更稳健特征来声称，即使雪崩寿命分布不是幂律，临界优化也可能发生，正如最近一项实验所表明的那样。最后，我们注意到，尽管雪崩规模分布的熵（信息容量）比后者更广泛地用于描述神经网络的计算能力，但在它们进行临界优化时，它并不总是跟随动态范围和信息效率。这有力地表明，允许相互作用神经元状态进行适当时间匹配的动力学规则是在信息处理中实现良好性能的关键，而不是增加可用单元的数量。
