齿状回
神经科学
赫比理论
内嗅皮质
突触可塑性
抑制性突触后电位
穿孔道
计算机科学
海马体
生物
人工神经网络
人工智能
生物化学
受体
作者
Segundo J. Guzman,Alois Schlögl,Claudia Espinoza,Xiaomin Zhang,Benjamin A. Suter,Péter Jónás
标识
DOI:10.1038/s43588-021-00157-1
摘要
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.
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