School of Computer and Communication Sciences and Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Switzerland.
Institute for Zoology, Faculty of Mathematics and Natural Sciences, University of Cologne, Köln, Germany.
PLoS Comput Biol. 2018 Jul 6;14(7):e1006216. doi: 10.1371/journal.pcbi.1006216. eCollection 2018 Jul.
The time scale of neuronal network dynamics is determined by synaptic interactions and neuronal signal integration, both of which occur on the time scale of milliseconds. Yet many behaviors like the generation of movements or vocalizations of sounds occur on the much slower time scale of seconds. Here we ask the question of how neuronal networks of the brain can support reliable behavior on this time scale. We argue that excitable neuronal assemblies with spike-frequency adaptation may serve as building blocks that can flexibly adjust the speed of execution of neural circuit function. We show in simulations that a chain of neuronal assemblies can propagate signals reliably, similar to the well-known synfire chain, but with the crucial difference that the propagation speed is slower and tunable to the behaviorally relevant range. Moreover we study a grid of excitable neuronal assemblies as a simplified model of the somatosensory barrel cortex of the mouse and demonstrate that various patterns of experimentally observed spatial activity propagation can be explained.
神经元网络动力学的时间尺度由突触相互作用和神经元信号整合决定,这两者都发生在毫秒的时间尺度上。然而,许多行为,如运动的产生或声音的发声,都发生在更慢的秒时间尺度上。在这里,我们提出了一个问题,即大脑的神经元网络如何能够在这个时间尺度上支持可靠的行为。我们认为,具有尖峰频率适应的可兴奋神经元集合可以作为构建块,灵活地调整神经回路功能的执行速度。我们在模拟中表明,神经元集合链可以可靠地传播信号,类似于著名的同步火焰链,但有一个关键的区别,即传播速度较慢,并且可以调整到与行为相关的范围。此外,我们研究了一个兴奋性神经元集合的网格,作为老鼠体感皮层的简化模型,并证明可以解释各种实验观察到的空间活动传播模式。