Varona Pablo, Rabinovich Mikhail I
Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain
BioCircuits Institute, University of California, San Diego, 9500 Gilman Drive #0328, La Jolla, CA 92093-0328, USA.
Proc Biol Sci. 2016 Jun 15;283(1832). doi: 10.1098/rspb.2016.0475.
Traditional studies on the interaction of cognitive functions in healthy and disordered brains have used the analyses of the connectivity of several specialized brain networks-the functional connectome. However, emerging evidence suggests that both brain networks and functional spontaneous brain-wide network communication are intrinsically dynamic. In the light of studies investigating the cooperation between different cognitive functions, we consider here the dynamics of hierarchical networks in cognitive space. We show, using an example of behavioural decision-making based on sequential episodic memory, how the description of metastable pattern dynamics underlying basic cognitive processes helps to understand and predict complex processes like sequential episodic memory recall and competition among decision strategies. The mathematical images of the discussed phenomena in the phase space of the corresponding cognitive model are hierarchical heteroclinic networks. One of the most important features of such networks is the robustness of their dynamics. Different kinds of instabilities of these dynamics can be related to 'dynamical signatures' of creativity and different psychiatric disorders. The suggested approach can also be useful for the understanding of the dynamical processes that are the basis of consciousness.
传统上，关于健康大脑和患病大脑中认知功能相互作用的研究采用了对几个专门大脑网络（即功能连接组）的连通性分析。然而，新出现的证据表明，大脑网络和功能性全脑自发网络通信本质上都是动态的。鉴于研究不同认知功能之间的协作，我们在此考虑认知空间中层次网络的动态性。我们以基于序列情景记忆的行为决策为例，展示了基础认知过程背后的亚稳态模式动态描述如何有助于理解和预测诸如序列情景记忆回忆和决策策略竞争等复杂过程。在相应认知模型的相空间中，所讨论现象的数学图像是层次异宿网络。此类网络最重要的特征之一是其动态的稳健性。这些动态的不同类型不稳定性可能与创造力和不同精神疾病的“动态特征”有关。所提出的方法对于理解作为意识基础的动态过程也可能有用。
