Neuronal traveling waves form preferred pathways using synaptic plasticity.

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Computational Neuroscience Pub Date : 2024-12-27 DOI:10.1007/s10827-024-00890-2
Kendall Butler, Luis Cruz
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Abstract

Traveling waves of neuronal spiking activity are commonly observed across the brain, but their intrinsic function is still a matter of investigation. Experiments suggest that they may be valuable in the consolidation of memory or learning, indicating that consideration of traveling waves in the presence of plasticity might be important. A possible outcome of this consideration is that the synaptic pathways, necessary for the propagation of these waves, will be modified by the waves themselves. This will create a feedback loop where both the traveling waves and the strengths of the available synaptic pathways will change. To computationally investigate this, we model a sheet of cortical tissue by considering a quasi two-dimensional network of model neurons locally connected with plastic synaptic weights using Spike-Timing Dependent Plasticity (STDP). By using different stimulation conditions (central, stochastic, and alternating stimulation), we demonstrate that starting from a random network, traveling waves with STDP will form and strengthen propagation pathways. With progressive formation of traveling waves, we observe increases in synaptic weight along the direction of wave propagation, increases in propagation speed when pathways are strengthened over time, and an increase in the local order of synaptic weights. We also present evidence that the interaction between traveling waves and plasticity can serve as a mechanism of network-wide competition between available pathways. With an improved understanding of the interactions between traveling waves and synaptic plasticity, we can approach a fuller understanding of mechanisms of learning, computation, and processing within the brain.

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神经元行波利用突触可塑性形成首选通路。
神经元尖峰活动的行波通常在大脑中被观察到,但它们的内在功能仍然是一个研究问题。实验表明,它们在巩固记忆或学习方面可能很有价值,这表明在可塑性存在的情况下考虑行波可能很重要。这种考虑的一个可能的结果是,这些电波传播所必需的突触通路将被电波本身改变。这将产生一个反馈回路,其中行波和可用突触通路的强度都会发生变化。为了在计算上研究这一点,我们通过使用Spike-Timing Dependent Plasticity (STDP)将局部连接到可塑性突触权的模型神经元的准二维网络,对皮质组织片进行了建模。通过使用不同的刺激条件(中央、随机和交替刺激),我们证明了从随机网络开始,具有STDP的行波将形成并加强传播路径。随着行波的渐进式形成,我们观察到沿波传播方向突触权增加,随着时间的推移,通路增强,传播速度增加,突触权的局部阶数增加。我们还提供证据表明,行波和可塑性之间的相互作用可以作为可用路径之间网络范围竞争的机制。随着对行波和突触可塑性之间相互作用的理解的提高,我们可以更全面地了解大脑内的学习、计算和处理机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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