Hippocampal phase precession may be generated by chimera dynamics.

IF 3 3区 医学 Q2 NEUROSCIENCES
Frontiers in Neural Circuits Pub Date : 2025-10-06 eCollection Date: 2025-01-01 DOI:10.3389/fncir.2025.1634298
Maria Masoliver, Jörn Davidsen, Wilten Nicola
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引用次数: 0

Abstract

The 8 Hz theta rhythm observed in hippocampal local field potentials of animals can be regarded as a "clock" that regulates the timing of spikes. While different interneuron sub-types synchronously phase lock to different phases for every theta cycle, the phase of pyramidal neurons' spikes asynchronously vary in each theta cycle, depending on the animal's position. On the other hand, pyramidal neurons tend to fire slightly faster than the theta oscillation in what is termed hippocampal phase precession. Chimera states are specific solutions to dynamical systems where synchrony and asynchrony coexist, similar to coexistence of phase precessing and phase locked cells during the hippocampal theta oscillation. Here, we test the hypothesis that the hippocampal phase precession emerges from chimera dynamics with computational modeling. We utilized multiple network topologies and sizes of Kuramoto oscillator networks that are known to collectively display chimera dynamics. We found that by changing the oscillators' intrinsic frequency, the frequency ratio between the synchronized and unsynchronized oscillators can match the frequency ratio between the hippocampal theta oscillation (≈ 8 Hz) and phase precessing pyramidal neurons (≈ 9 Hz). The faster firing population of oscillators also displays theta-sequence-like behavior and phase precession. Finally, we trained networks of spiking integrate-and-fire neurons to output a chimera state by using the Kuramoto-chimera system as a dynamical supervisor. We found that the firing times of subsets of individual neurons display phase precession.

嵌合体动力学可能产生海马相进动。
在动物海马局部场电位中观察到的8 Hz θ节律可以看作是调节尖峰时间的“时钟”。不同的中间神经元亚型在每个θ波周期中同步锁相到不同的相位,而锥体神经元在每个θ波周期中,锥体神经元的峰值相位根据动物的位置而异步变化。另一方面,锥体神经元的放电速度略快于被称为海马相进动的θ波振荡。嵌合体状态是同步和异步共存的动力系统的特定解决方案,类似于海马体θ波振荡期间相处理细胞和相锁定细胞的共存。在这里,我们用计算模型验证了海马体相位进动来自嵌合体动力学的假设。我们利用了多种网络拓扑结构和Kuramoto振荡器网络的大小,这些网络已知可以共同显示嵌合体动力学。我们发现,通过改变振子的固有频率,同步和非同步振子的频率比可以与海马θ振荡(≈8 Hz)和相进锥体神经元(≈9 Hz)的频率比相匹配。振子的快速发射种群也显示出类似于θ序列的行为和相位进动。最后,我们使用Kuramoto-chimera系统作为动态监督器,训练由突射整合-火神经元组成的网络输出嵌合体状态。我们发现单个神经元子集的放电时间表现出相进动。
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来源期刊
CiteScore
6.00
自引率
5.70%
发文量
135
审稿时长
4-8 weeks
期刊介绍: Frontiers in Neural Circuits publishes rigorously peer-reviewed research on the emergent properties of neural circuits - the elementary modules of the brain. Specialty Chief Editors Takao K. Hensch and Edward Ruthazer at Harvard University and McGill University respectively, are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Frontiers in Neural Circuits launched in 2011 with great success and remains a "central watering hole" for research in neural circuits, serving the community worldwide to share data, ideas and inspiration. Articles revealing the anatomy, physiology, development or function of any neural circuitry in any species (from sponges to humans) are welcome. Our common thread seeks the computational strategies used by different circuits to link their structure with function (perceptual, motor, or internal), the general rules by which they operate, and how their particular designs lead to the emergence of complex properties and behaviors. Submissions focused on synaptic, cellular and connectivity principles in neural microcircuits using multidisciplinary approaches, especially newer molecular, developmental and genetic tools, are encouraged. Studies with an evolutionary perspective to better understand how circuit design and capabilities evolved to produce progressively more complex properties and behaviors are especially welcome. The journal is further interested in research revealing how plasticity shapes the structural and functional architecture of neural circuits.
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