Department of Neurology and Developmental Medicine, Kennedy Krieger Institute, Baltimore, MD 21205, USA; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Institut de Neurosciences des Systèmes, INSERM, Aix-Marseille Université, 13005 Marseille, France.
Neuron. 2017 Jun 7;94(5):1010-1026. doi: 10.1016/j.neuron.2017.05.013.
In order to maintain brain function, neural activity needs to be tightly coordinated within the brain network. How this coordination is achieved and related to behavior is largely unknown. It has been previously argued that the study of the link between brain and behavior is impossible without a guiding vision. Here we propose behavioral-level concepts and mechanisms embodied as structured flows on manifold (SFM) that provide a formal description of behavior as a low-dimensional process emerging from a network's dynamics dependent on the symmetry and invariance properties of the network connectivity. Specifically, we demonstrate that the symmetry breaking of network connectivity constitutes a timescale hierarchy resulting in the emergence of an attractive functional subspace. We show that behavior emerges when appropriate conditions imposed upon the couplings are satisfied, justifying the conductance-based nature of synaptic couplings. Our concepts propose design principles for networks predicting how behavior and task rules are represented in real neural circuits and open new avenues for the analyses of neural data.
为了维持大脑功能,神经网络中的神经活动需要紧密协调。这种协调是如何实现的,以及与行为的关系,在很大程度上还不清楚。此前有人认为,如果没有一个指导性的愿景,研究大脑和行为之间的联系是不可能的。在这里,我们提出了行为层面的概念和机制,这些概念和机制体现为流形上的结构化流(SFM),为行为提供了一个正式的描述,即行为是一个从网络动力学中涌现出来的低维过程,这一过程取决于网络连接的对称性和不变性。具体来说,我们证明了网络连接的对称性破缺构成了一个时间尺度层次结构,导致吸引性功能子空间的出现。我们表明,当满足适当的条件时,耦合就会出现,从而证明了突触耦合的电导性质。我们的概念为网络提出了设计原则,预测了行为和任务规则是如何在真实的神经电路中表示的,并为分析神经数据开辟了新的途径。