标题： 皮层电路中的稀疏混沌 显示英文标题

标题： Sparse chaos in cortical circuits

摘要： 神经冲动是大脑信息流动的货币，是由神经元膜电位动力学的不稳定性产生的。神经元电路表现出集体混沌，这似乎对学习、记忆、感官处理和运动控制至关重要。然而，控制神经元电路中集体混沌的性质和强度的因素尚不清楚。在这里，我们使用计算遍历理论来证明神经冲动生成的基本特征深刻地影响神经元电路中的集体混沌。Lyapunov谱、Kolmogorov-Sinai熵以及吸引子维度的上下界数值精确计算表明，单个神经元中神经冲动生成的变化适度影响信息编码速率，但定性地改变相空间结构。具体而言，我们发现不稳定流形的数量、Kolmogorov-Sinai熵和吸引子维度显著减少。在临界点之后，由扩散近似同时破坏、最大Lyapunov指数峰值和主导协变Lyapunov向量的局域化转变所标志，网络表现出稀疏混沌：长时间的近稳定动态期间夹杂着短暂的强烈混沌爆发。对大型、更现实结构的网络分析支持了这些发现的普遍性。在皮层电路中，生物物理特性似乎调整到这种稀疏混沌的模式。我们的结果揭示了单神经元生物物理学的基本方面与皮层电路集体动力学之间的紧密联系，表明神经冲动生成机制适应于增强电路可控性和信息流动。 显示英文摘要

摘要： Nerve impulses, the currency of information flow in the brain, are generated by an instability of the neuronal membrane potential dynamics. Neuronal circuits exhibit collective chaos that appears essential for learning, memory, sensory processing, and motor control. However, the factors controlling the nature and intensity of collective chaos in neuronal circuits are not well understood. Here we use computational ergodic theory to demonstrate that basic features of nerve impulse generation profoundly affect collective chaos in neuronal circuits. Numerically exact calculations of Lyapunov spectra, Kolmogorov-Sinai-entropy, and upper and lower bounds on attractor dimension show that changes in nerve impulse generation in individual neurons moderately impact information encoding rates but qualitatively transform phase space structure. Specifically, we find a drastic reduction in the number of unstable manifolds, Kolmogorov-Sinai entropy, and attractor dimension. Beyond a critical point, marked by the simultaneous breakdown of the diffusion approximation, a peak in the largest Lyapunov exponent, and a localization transition of the leading covariant Lyapunov vector, networks exhibit sparse chaos: prolonged periods of near stable dynamics interrupted by short bursts of intense chaos. Analysis of large, more realistically structured networks supports the generality of these findings. In cortical circuits, biophysical properties appear tuned to this regime of sparse chaos. Our results reveal a close link between fundamental aspects of single-neuron biophysics and the collective dynamics of cortical circuits, suggesting that nerve impulse generation mechanisms are adapted to enhance circuit controllability and information flow.