{"title":"成年海马神经发生对模式分离的影响及其应用","authors":"Zengbin Wang, Kai Yang, Xiaojuan Sun","doi":"10.1007/s11571-024-10110-3","DOIUrl":null,"url":null,"abstract":"<p>Adult hippocampal neurogenesis (AHN) is considered essential in memory formation. The dentate gyrus neural network containing newborn dentate gyrus granule cells at the critical period (4–6 weeks) have been widely discussed in neurophysiological and behavioral experiments. However, how newborn dentate gyrus granule cells at this critical period influence pattern separation of dentate gyrus remains unclear. To address this issue, we propose a biologically related dentate gyrus neural network model with AHN. By Leveraging this model, we find pattern separation is enhanced at the medium level of neurogenesis (5% of mature granule cells). This is because the sparse firing of mature granule cells is increased. We can understand this change from the following two aspects. On one hand, newborn granule cells compete with mature granule cells for inputs from the entorhinal cortex, thereby weakening the firing of mature granule cells. On the other hand, newborn granule cells effectively enhance the feedback inhibition level of the network by promoting the firing of interneurons (Mossy cells and Basket cells) and then indirectly regulating the sparse firing of mature granule cells. To verify the validity of the model for pattern separation, we apply the proposed model to a similar concept separation task and reveal that our model outperforms the original model counterparts in this task.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"16 1","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Effect of adult hippocampal neurogenesis on pattern separation and its applications\",\"authors\":\"Zengbin Wang, Kai Yang, Xiaojuan Sun\",\"doi\":\"10.1007/s11571-024-10110-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Adult hippocampal neurogenesis (AHN) is considered essential in memory formation. The dentate gyrus neural network containing newborn dentate gyrus granule cells at the critical period (4–6 weeks) have been widely discussed in neurophysiological and behavioral experiments. However, how newborn dentate gyrus granule cells at this critical period influence pattern separation of dentate gyrus remains unclear. To address this issue, we propose a biologically related dentate gyrus neural network model with AHN. By Leveraging this model, we find pattern separation is enhanced at the medium level of neurogenesis (5% of mature granule cells). This is because the sparse firing of mature granule cells is increased. We can understand this change from the following two aspects. On one hand, newborn granule cells compete with mature granule cells for inputs from the entorhinal cortex, thereby weakening the firing of mature granule cells. On the other hand, newborn granule cells effectively enhance the feedback inhibition level of the network by promoting the firing of interneurons (Mossy cells and Basket cells) and then indirectly regulating the sparse firing of mature granule cells. To verify the validity of the model for pattern separation, we apply the proposed model to a similar concept separation task and reveal that our model outperforms the original model counterparts in this task.</p>\",\"PeriodicalId\":10500,\"journal\":{\"name\":\"Cognitive Neurodynamics\",\"volume\":\"16 1\",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-04-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cognitive Neurodynamics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s11571-024-10110-3\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Neurodynamics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11571-024-10110-3","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
Effect of adult hippocampal neurogenesis on pattern separation and its applications
Adult hippocampal neurogenesis (AHN) is considered essential in memory formation. The dentate gyrus neural network containing newborn dentate gyrus granule cells at the critical period (4–6 weeks) have been widely discussed in neurophysiological and behavioral experiments. However, how newborn dentate gyrus granule cells at this critical period influence pattern separation of dentate gyrus remains unclear. To address this issue, we propose a biologically related dentate gyrus neural network model with AHN. By Leveraging this model, we find pattern separation is enhanced at the medium level of neurogenesis (5% of mature granule cells). This is because the sparse firing of mature granule cells is increased. We can understand this change from the following two aspects. On one hand, newborn granule cells compete with mature granule cells for inputs from the entorhinal cortex, thereby weakening the firing of mature granule cells. On the other hand, newborn granule cells effectively enhance the feedback inhibition level of the network by promoting the firing of interneurons (Mossy cells and Basket cells) and then indirectly regulating the sparse firing of mature granule cells. To verify the validity of the model for pattern separation, we apply the proposed model to a similar concept separation task and reveal that our model outperforms the original model counterparts in this task.
期刊介绍:
Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models.
The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results. In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome.
The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences. Papers that are appropriate for non-specialist readers are encouraged.
1. There is no page limit for manuscripts submitted to Cognitive Neurodynamics. Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics.
2. Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication. Brief Communications should consist of approximately four manuscript pages.
3. Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey. There are no restrictions on the number of pages. Review articles are usually invited, but submitted reviews will also be considered.