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首页> 外文期刊>Journal of Computational Neuroscience >Linking dynamical and functional properties of intrinsically bursting neurons
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Linking dynamical and functional properties of intrinsically bursting neurons

机译：链接内在爆发神经元的动力学和功能特性

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Several studies have shown that bursting neurons can encode information in the number of spikes per burst: As the stimulus varies, so does the length of individual bursts. The represented stimuli, however, vary substantially among different sensory modalities and different neurons. The goal of this paper is to determine which kind of stimulus features can be encoded in burst length, and how those features depend on the mathematical properties of the underlying dynamical system. We show that the initiation and termination of each burst is triggered by specific stimulus features whose temporal characteristsics are determined by the types of bifurcations that initiate and terminate firing in each burst. As only a few bifurcations are possible, only a restricted number of encoded features exists. Here we focus specifically on describing parabolic, square-wave and elliptic bursters. We find that parabolic bursters, whose firing is initiated and terminated by saddle-node bifurcations, behave as prototypical integrators: Firing is triggered by depolarizing stimuli, and lasts for as long as excitation is prolonged. Elliptic bursters, contrastingly, constitute prototypical resonators, since both the initiating and terminating bifurcations possess well-defined oscillation time scales. Firing is therefore triggered by stimulus stretches of matching frequency and terminated by a phase-inversion in the oscillation. The behavior of squarewave bursters is somewhat intermediate, since they are triggered by a fold bifurcation of cycles of well-defined frequency but are terminated by a homoclinic bifurcation lacking an oscillating time scale. These correspondences show that stimulus selectivity is determined by the type of bifurcations. By testing several neuron models, we also demonstrate that additional biological properties that do not modify the bifurcation structure play a minor role in stimulus encoding. Moreover, we show that burst-length variability (and thereby, the capacity to transmit information) depends on a trade-off between the variance of the external signal driving the cell and the strength of the slow internal currents modulating bursts. Thus, our work explicitly links the computational properties of bursting neurons to the mathematical properties of the underlying dynamical systems.
机译：多项研究表明，爆发的神经元可以将每次爆发的尖峰数量编码为信息：随着刺激的变化，单个爆发的长度也随之变化。然而，所代表的刺激在不同的感觉方式和不同的神经元之间有很大的不同。本文的目的是确定可以在突发长度中编码哪种刺激特征，以及这些特征如何取决于基础动力系统的数学特性。我们表明，每个爆发的开始和终止是由特定的刺激特征触发的，这些刺激特征的时间特性由在每个爆发中引发和终止发射的分叉类型决定。由于只有几个分叉是可能的，因此仅存在有限数量的编码特征。在这里，我们专门关注于描述抛物线形，方波形和椭圆形的爆发器。我们发现抛物线形爆破器的发射是通过鞍形节点分叉来启动和终止的，它们表现为典型的积分器：发射是通过去极化刺激来触发的，并且持续了很长时间。相比之下，椭圆爆发器则构成了典型的谐振器，因为起始和终止分叉都具有定义明确的振荡时间尺度。因此，点火是由匹配频率的激励范围触发的，并由振荡中的相位反转终止。方波猝发器的行为在某种程度上是中间的，因为它们由频率明确的周期的折叠分叉触发，但由缺乏振荡时间尺度的同斜分叉终止。这些对应关系表明，刺激选择性是由分叉的类型决定的。通过测试几种神经元模型，我们还证明了不修饰分叉结构的其他生物学特性在刺激编码中起次要作用。而且，我们表明，突发长度的可变性（从而，信息的传输能力）取决于驱动单元的外部信号的方差与调制突发的慢速内部电流的强度之间的折衷。因此，我们的工作明确地将爆发性神经元的计算属性与基础动力学系统的数学属性联系起来。

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