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。

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Computational Neuroscience Pub Date : 2023-11-01 Epub Date: 2023-08-26 DOI:10.1007/s10827-023-00860-0
Brian L Frost, Stanislav M Mintchev
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引用次数: 0

摘要

最近对创伤性脑损伤的研究表明,这些损伤会导致大脑皮层单个神经元水平的构象变化。局灶性轴突肿胀是这种损伤的后果之一,并导致沿着细胞轴突的宽度可变。对以这种方式受到影响的轴突的电特性的模拟表明,这种损伤可能对刺突编码的信号传输具有非线性有害影响。这些模拟的计算成本使在网络层面上研究这种损伤的影响变得复杂。我们开发了一种高效的算法,它忠实地再现了物理模拟中看到的尖峰序列滤波特性。我们使用该算法来探索局灶性轴突肿胀对整合和激发神经元的小网络的影响。我们还探讨了架构修改对以这种方式受到影响的网络的影响。在所有测试的网络中,我们的结果表明,突触前抑制性神经元的加入增加了网络处理特性对这种损伤的保真度,或者在带宽方面保持不变。
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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.

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来源期刊
CiteScore
2.00
自引率
8.30%
发文量
32
审稿时长
3 months
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.
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