Université de Strasbourg, CNRS, Inria, ICube, MLMS, MIMESIS, Strasbourg F-67000, France.
Krembil Brain Institute, Division of Clinical and Computational Neuroscience, University Health Network, Toronto, ON M5T 0S8, Canada.
Proc Natl Acad Sci U S A. 2023 Jul 11;120(28):e2218841120. doi: 10.1073/pnas.2218841120. Epub 2023 Jul 3.
Heterogeneity is the norm in biology. The brain is no different: Neuronal cell types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs, and ion channel distributions. While this biophysical diversity enriches neural systems' dynamical repertoire, it remains challenging to reconcile with the robustness and persistence of brain function over time (resilience). To better understand the relationship between excitability heterogeneity (variability in excitability within a population of neurons) and resilience, we analyzed both analytically and numerically a nonlinear sparse neural network with balanced excitatory and inhibitory connections evolving over long time scales. Homogeneous networks demonstrated increases in excitability, and strong firing rate correlations-signs of instability-in response to a slowly varying modulatory fluctuation. Excitability heterogeneity tuned network stability in a context-dependent way by restraining responses to modulatory challenges and limiting firing rate correlations, while enriching dynamics during states of low modulatory drive. Excitability heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in population size, connection probability, strength and variability of synaptic weights, by quenching the volatility (i.e., its susceptibility to critical transitions) of its dynamics. Together, these results highlight the fundamental role played by cell-to-cell heterogeneity in the robustness of brain function in the face of change.
异质性是生物学的常态。大脑也不例外：神经元细胞类型繁多，反映在其细胞形态、类型、兴奋性、连接模式和离子通道分布上。虽然这种生物物理多样性丰富了神经网络的动态范围，但要将其与大脑功能随时间的稳健性和持久性（弹性）相协调仍然具有挑战性。为了更好地理解兴奋性异质性（神经元群体内兴奋性的变异性）与弹性之间的关系，我们从分析和数值两方面研究了具有平衡兴奋性和抑制性连接的非线性稀疏神经网络，这些连接在长时间尺度上演变。均匀网络表现出兴奋性增加，以及强放电率相关性——不稳定的标志——对缓慢变化的调制波动的反应。兴奋性异质性通过限制对调制挑战的反应和限制放电率相关性，以依赖于上下文的方式调节网络稳定性，同时在调制驱动较低的状态下丰富动力学。研究发现，兴奋性异质性通过抑制动力学的易变性（即对关键转变的敏感性），实现了增强网络对群体大小、连接概率、突触权重强度和变异性变化的弹性的自稳态控制机制。总的来说，这些结果突出了细胞间异质性在大脑功能面对变化时的稳健性中所起的基本作用。
