Large-Scale Algorithmic Search Identifies Stiff and Sloppy Dimensions in Synaptic Architectures Consistent With Murine Neocortical Wiring.
Large-Scale Algorithmic Search Identifies Stiff and Sloppy Dimensions in Synaptic Architectures Consistent With Murine Neocortical Wiring.
复制标题
大规模算法搜索识别出与小鼠新皮质布线一致的突触结构中僵硬且杂乱的尺寸。
DOI：
10.1162/neco_a_01544
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发表时间：
2022
影响因子：
2.9
通讯作者：
MacLean,JasonN
中科院分区：
文献类型：
--
作者：
Jabri,Tarek;MacLean,JasonN
复杂系统可以用“松散”维度和“僵硬”维度来定义，前者意味着它们的行为不会因特定参数组合的较大变化而改变，后者的变化会导致相当大的行为变化。在新大脑皮层，突触结构的松散将是关键，以允许维持低放电率的异步不规则棘波动力学，尽管输入、状态以及短期和长期可塑性的多样性。通过对具有与小鼠视皮层放电相匹配的一阶尖峰统计的神经网络的模拟，当改变连接参数时，我们确定了三类输入(短暂、连续和循环)突触结构的僵硬和粗糙的参数。从参数空间的很大一部分通过算法生成的连接性参数值表明，兴奋性和抑制性连接性的特定组合是僵硬的，所有其他架构细节都是草率的。刚性维度在输入类之间是一致的，与其他输入类相比，短暂输入后的自持性突触结构占据了较小的子空间。实验估计的小鼠视皮层的连通性概率与所发现的连通性相关性是一致的，并且落在参数空间中与通过算法识别的结构相同的区域。这表明，在研究介观尺度上的结构-功能关系时，对峰动力学的简单统计描述是对新皮质活动的充分和简明的描述。此外，粗粒化细胞类型并不妨碍生成准确的、信息量丰富的、可解释的模型来支持简单的尖峰活动。这项不偏不倚的研究进一步证明了兴奋性和抑制性连接之间的相互关系对于建立和维持新皮质中稳定的放电动力学机制的重要性。
Complex systems can be defined by “sloppy” dimensions, meaning that their behavior is unmodified by large changes to specific parameter combinations, and “stiff” dimensions, whose change results in considerable behavioral modification. In the neocortex, sloppiness in synaptic architectures would be crucial to allow for the maintenance of asynchronous irregular spiking dynamics with low firing rates despite a diversity of inputs, states, and short- and long-term plasticity. Using simulations on neural networks with first-order spiking statistics matched to firing in murine visual cortex while varying connectivity parameters, we determined the stiff and sloppy parameters of synaptic architectures across three classes of input (brief, continuous, and cyclical). Algorithmically generated connectivity parameter values drawn from a large portion of the parameter space reveal that specific combinations of excitatory and inhibitory connectivity are stiff and that all other architectural details are sloppy. Stiff dimensions are consistent across input classes with self-sustaining synaptic architectures following brief input occupying a smaller subspace as compared to the other input classes. Experimentally estimated connectivity probabilities from mouse visual cortex are consistent with the connectivity correlations found and fall in the same region of the parameter space as architectures identified algorithmically. This suggests that simple statistical descriptions of spiking dynamics are a sufficient and parsimonious description of neocortical activity when examining structure-function relationships at the mesoscopic scale. Additionally, coarse graining cell types does not prevent the generation of accurate, informative, and interpretable models underlying simple spiking activity. This unbiased investigation provides further evidence of the importance of the interrelationship of excitatory and inhibitory connectivity to establish and maintain stable spiking dynamical regimes in the neocortex.