{"title":"一种高效模型,表明抑制在神经元网络对创伤损伤的恢复力中的作用。","authors":"Brian L Frost, Stanislav M Mintchev","doi":"10.1007/s10827-023-00860-0","DOIUrl":null,"url":null,"abstract":"<p><p>Recent investigations of traumatic brain injuries have shown that these injuries can result in conformational changes at the level of individual neurons in the cerebral cortex. Focal axonal swelling is one consequence of such injuries and leads to a variable width along the cell axon. Simulations of the electrical properties of axons impacted in such a way show that this damage may have a nonlinear deleterious effect on spike-encoded signal transmission. The computational cost of these simulations complicates the investigation of the effects of such damage at a network level. We have developed an efficient algorithm that faithfully reproduces the spike train filtering properties seen in physical simulations. We use this algorithm to explore the impact of focal axonal swelling on small networks of integrate and fire neurons. We explore also the effects of architecture modifications to networks impacted in this manner. In all tested networks, our results indicate that the addition of presynaptic inhibitory neurons either increases or leaves unchanged the fidelity, in terms of bandwidth, of the network's processing properties with respect to this damage.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":" ","pages":"463-474"},"PeriodicalIF":1.5000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A high-efficiency model indicating the role of inhibition in the resilience of neuronal networks to damage resulting from traumatic injury.\",\"authors\":\"Brian L Frost, Stanislav M Mintchev\",\"doi\":\"10.1007/s10827-023-00860-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Recent investigations of traumatic brain injuries have shown that these injuries can result in conformational changes at the level of individual neurons in the cerebral cortex. Focal axonal swelling is one consequence of such injuries and leads to a variable width along the cell axon. Simulations of the electrical properties of axons impacted in such a way show that this damage may have a nonlinear deleterious effect on spike-encoded signal transmission. The computational cost of these simulations complicates the investigation of the effects of such damage at a network level. We have developed an efficient algorithm that faithfully reproduces the spike train filtering properties seen in physical simulations. We use this algorithm to explore the impact of focal axonal swelling on small networks of integrate and fire neurons. We explore also the effects of architecture modifications to networks impacted in this manner. In all tested networks, our results indicate that the addition of presynaptic inhibitory neurons either increases or leaves unchanged the fidelity, in terms of bandwidth, of the network's processing properties with respect to this damage.</p>\",\"PeriodicalId\":54857,\"journal\":{\"name\":\"Journal of Computational Neuroscience\",\"volume\":\" \",\"pages\":\"463-474\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2023-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computational Neuroscience\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s10827-023-00860-0\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/8/26 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10827-023-00860-0","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/8/26 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
A high-efficiency model indicating the role of inhibition in the resilience of neuronal networks to damage resulting from traumatic injury.
Recent investigations of traumatic brain injuries have shown that these injuries can result in conformational changes at the level of individual neurons in the cerebral cortex. Focal axonal swelling is one consequence of such injuries and leads to a variable width along the cell axon. Simulations of the electrical properties of axons impacted in such a way show that this damage may have a nonlinear deleterious effect on spike-encoded signal transmission. The computational cost of these simulations complicates the investigation of the effects of such damage at a network level. We have developed an efficient algorithm that faithfully reproduces the spike train filtering properties seen in physical simulations. We use this algorithm to explore the impact of focal axonal swelling on small networks of integrate and fire neurons. We explore also the effects of architecture modifications to networks impacted in this manner. In all tested networks, our results indicate that the addition of presynaptic inhibitory neurons either increases or leaves unchanged the fidelity, in terms of bandwidth, of the network's processing properties with respect to this damage.
期刊介绍:
The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.