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简并性解释了异质性齿状回网络中模式分离的中间神经元调节的多样性。

Degeneracy explains diversity in interneuronal regulation of pattern separation in heterogeneous dentate gyrus networks.

作者信息

Saini Sarang, Narayanan Rishikesh

机构信息

Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.

出版信息

Function (Oxf). 2025 Aug 1. doi: 10.1093/function/zqaf035.

Abstract

Pattern separation, the ability of a network to distinguish similar inputs by transforming them into distinct outputs, was postulated by the Marr-Albus theory to be realized by divergent feedforward excitatory connectivity. Yet, there is evidence for strong but differential regulation of pattern separation by local circuit connectivity. How do we reconcile the conflicting views on local-circuit regulation of pattern separation in circuits receiving divergent feedforward connectivity? Here, we quantitatively examined a population of heterogeneous dentate gyrus (DG) spiking networks where identically divergent feedforward connectivity was enforced. We generated 20 000 random DG networks constructed with thousands of functionally validated, heterogeneous single-neuron models of four different DG neuronal subtypes. We recorded network outputs to morphed sets of input patterns and applied quantitative metrics that we developed to assess pattern separation performance of each network. Surprisingly, only 47 of these 20 000 networks (0.23%) manifested effective pattern separation showing that divergent feedforward connectivity alone does not guarantee pattern separation. Instead, our analyses unveiled strong contributions from the three interneuron subtypes towards granule cell sparsity and pattern separation, with pronounced network-to-network variability in such contributions. We traced this variability to differences in local synaptic weights across pattern-separating networks, highlighting synaptic degeneracy as a key mechanism that explains diversity in interneuronal regulation of pattern separation. Finally, we found heterogeneous DG networks to be more resilient to synaptic jitter compared to their homogeneous counterparts. Together, our findings reconcile conflicting evidence by revealing degeneracy in DG circuits, whereby similar pattern separation efficacy can arise through diverse interactions among granule cells and interneurons.

摘要

模式分离是指网络通过将相似输入转化为不同输出以区分它们的能力,马尔-阿尔布斯理论假定其通过发散性前馈兴奋性连接来实现。然而,有证据表明局部回路连接对模式分离有强大但不同的调节作用。在接受发散性前馈连接的回路中,我们如何协调关于模式分离的局部回路调节的相互矛盾的观点呢?在这里,我们定量研究了一群异质性齿状回(DG)尖峰网络,其中施加了相同的发散性前馈连接。我们生成了20000个随机DG网络,这些网络由数千个经过功能验证的、四种不同DG神经元亚型的异质性单神经元模型构建而成。我们记录了网络对变形输入模式集的输出,并应用我们开发的定量指标来评估每个网络的模式分离性能。令人惊讶的是,这20000个网络中只有47个(0.23%)表现出有效的模式分离,这表明仅发散性前馈连接并不能保证模式分离。相反,我们的分析揭示了三种中间神经元亚型对颗粒细胞稀疏性和模式分离有强大贡献,而且这种贡献在网络之间存在显著差异。我们将这种差异追溯到模式分离网络之间局部突触权重的差异,突出了突触简并性是解释中间神经元对模式分离调节多样性的关键机制。最后,我们发现与同质性DG网络相比,异质性DG网络对突触抖动更具弹性。总之,我们的研究结果通过揭示DG回路中的简并性,调和了相互矛盾的证据,即通过颗粒细胞和中间神经元之间的多种相互作用可以产生相似的模式分离效果。

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