研究组收集了来自所有家族的光标记神经元的数据集，以及来自新皮层和海马体的兴奋性神经元。这些神经元的生理特征以训练机器学习分类器为主题，随后在大规模记录中推断出特定的中间神经元家族。这种结合的方法可以重建中间神经元家族成员之间的突触连通性基序。该课题组进一步发现这些基序对锥体神经元的位置场特征有不同的控制。他们的发现认为中间神经元在形成灵活的认知地图中发挥了重要作用。
据了解，识别不同神经元家族的计算角色对于理解神经网络至关重要。大多数神经多样性体现在各种类型的γ-氨基丁酸介导的（GABAergic）中间神经元中，分为其主要科。
附：英文原文
Title: Cooperative actions of interneuron families support the hippocampal spatial code
Author: Manuel Valero, Pablo Abad-Perez, Andrea Gallardo, Marta Picco, Raquel García-Hernandez, Jorge Brotons, Anel Martínez-Félix, Robert Machold, Bernardo Rudy, Gyrgy Buzsáki
Issue&Volume: 2025-09-04
Abstract: Identifying the computational roles of different neuron families is crucial for understanding neural networks. Most neural diversity is embodied in various types of γ-aminobutyric acid–mediated (GABAergic) interneurons, grouped into four major families. We collected datasets of opto-tagged neurons from all four families, along with excitatory neurons, from both the neocortex and hippocampus. The physiological features of these neurons were used to train a machine learning classifier, which subsequently inferred specific interneuron families in large-scale recordings. This combined approach enabled the reconstruction of synaptic connectivity motifs across interneuron family members. We further showed that these motifs differentially control the place field features of pyramidal neurons. Our findings attribute a prominent role to interneurons in the formation of a flexible cognitive map.
DOI: adv5638
Source: https://www.science.org/doi/10.1126/science.adv5638