Guzman S Jose, Schlögl Alois, Espinoza Claudia, Zhang Xiaomin, Suter Benjamin A, Jonas Peter
IST Austria, Klosterneuburg, Austria.
Institute of Molecular Biotechnology, Vienna, Austria.
Nat Comput Sci. 2021 Dec;1(12):830-842. doi: 10.1038/s43588-021-00157-1. Epub 2021 Dec 16.
Pattern separation is a fundamental brain computation that converts small differences in input patterns into large differences in output patterns. Several synaptic mechanisms of pattern separation have been proposed, including code expansion, inhibition and plasticity; however, which of these mechanisms play a role in the entorhinal cortex (EC)-dentate gyrus (DG)-CA3 circuit, a classical pattern separation circuit, remains unclear. Here we show that a biologically realistic, full-scale EC-DG-CA3 circuit model, including granule cells (GCs) and parvalbumin-positive inhibitory interneurons (PV-INs) in the DG, is an efficient pattern separator. Both external gamma-modulated inhibition and internal lateral inhibition mediated by PV-INs substantially contributed to pattern separation. Both local connectivity and fast signaling at GC-PV-IN synapses were important for maximum effectiveness. Similarly, mossy fiber synapses with conditional detonator properties contributed to pattern separation. By contrast, perforant path synapses with Hebbian synaptic plasticity and direct EC-CA3 connection shifted the network towards pattern completion. Our results demonstrate that the specific properties of cells and synapses optimize higher-order computations in biological networks and might be useful to improve the deep learning capabilities of technical networks.
模式分离是一种基本的大脑计算过程，它将输入模式中的微小差异转化为输出模式中的巨大差异。人们已经提出了几种模式分离的突触机制，包括编码扩展、抑制和可塑性；然而，在经典的模式分离回路——内嗅皮质（EC）-齿状回（DG）-CA3回路中，这些机制中哪些发挥作用仍不清楚。在这里，我们表明，一个包含DG中的颗粒细胞（GCs）和小白蛋白阳性抑制性中间神经元（PV-INs）的生物学现实的全尺度EC-DG-CA3回路模型是一种有效的模式分离器。由PV-INs介导的外部γ调制抑制和内部侧向抑制都对模式分离有显著贡献。GC-PV-IN突触处的局部连接性和快速信号传导对于最大有效性都很重要。同样，具有条件雷管特性的苔藓纤维突触也有助于模式分离。相比之下，具有赫布突触可塑性的穿通通路突触和直接的EC-CA3连接会使网络趋向于模式完成。我们的结果表明，细胞和突触的特定属性优化了生物网络中的高阶计算，并且可能有助于提高技术网络的深度学习能力。
