Non-monotonic effects of GABAergic synaptic inputs on neuronal firing.
Non-monotonic effects of GABAergic synaptic inputs on neuronal firing.
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GABA能突触输入对神经元射击的非单调影响。
DOI：
10.1371/journal.pcbi.1010226
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发表时间：
2022-06
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
GABA 通常被认为是神经系统中主要的抑制性神经递质，通常通过超极化膜电位发挥作用。然而，GABA能电流有时会表现出非抑制作用，具体取决于大脑区域、发育阶段或病理状况。在这里，我们使用分析计算和数值模拟研究 GABA 对几个单神经元模型的放电率的不同影响。我们发现，GABA 能突触电导和输出放电率作为 GABA 逆转电位 EGABA 的函数表现出三种性质不同的状态：对于足够低的 EGABA（抑制性）单调递减，对于高于放电阈值（兴奋性）的 EGABA 单调递增；以及 EGABA 中间值的非单调区域。在非单调状态下，小的 GABA 电导具有兴奋作用，而大的 GABA 电导则具有抑制作用。我们提供了不同 GABA 能效应与 GABA 逆转电位和谷氨酸电导的函数关系的相图。我们发现噪声输入增加了可以观察到非单调效应的 EGABA 范围。我们还构建了纹状体的微电路模型，以解释观察到的 GABA 能快速尖峰中间神经元对多刺投射神经元的影响，包括非单调性以及影响的异质性。我们的工作为 GABA 突触输入的矛盾效应提供了机制解释，对于理解 GABA 在神经计算和发育中的影响具有重要意义。神经系统中的神经元主要通过化学突触进行通讯，通过从突触前侧释放神经递质与突触后侧的受体结合，触发离子流过细胞膜上的离子通道，并改变突触后神经元的膜电位。 γ-氨基丁酸（GABA）是抑制性神经元表达的主要神经递质。它与 GABA 能离子型受体的结合主要引起氯离子跨膜流动，通常会使突触后神经元超极化，从而导致放电抑制。虽然 GABA 通常被视为一种抑制性神经递质，但在发育早期、压力相关疾病以及大脑结构的特定部分（如皮层、小脑和海马体）中观察到非抑制性作用。在这里，我们对尖峰神经元模型采用分析和计算方法来研究 GABA 突触输入的不同影响的机制。我们发现，除了单调兴奋或单调抑制效应外，GABA能输入还表现出非单调效应，其效应取决于输入的强度。这种效应在存在噪声的情况下更强，并且在单细胞和网络级别的不同模型中都可以观察到。我们的研究结果为几个自相矛盾的实验观察结果提供了机械解释，对神经网络动力学和计算具有潜在影响。
GABA is generally known as the principal inhibitory neurotransmitter in the nervous system, usually acting by hyperpolarizing membrane potential. However, GABAergic currents sometimes exhibit non-inhibitory effects, depending on the brain region, developmental stage or pathological condition. Here, we investigate the diverse effects of GABA on the firing rate of several single neuron models, using both analytical calculations and numerical simulations. We find that GABAergic synaptic conductance and output firing rate exhibit three qualitatively different regimes as a function of GABA reversal potential, EGABA: monotonically decreasing for sufficiently low EGABA (inhibitory), monotonically increasing for EGABA above firing threshold (excitatory); and a non-monotonic region for intermediate values of EGABA. In the non-monotonic regime, small GABA conductances have an excitatory effect while large GABA conductances show an inhibitory effect. We provide a phase diagram of different GABAergic effects as a function of GABA reversal potential and glutamate conductance. We find that noisy inputs increase the range of EGABA for which the non-monotonic effect can be observed. We also construct a micro-circuit model of striatum to explain observed effects of GABAergic fast spiking interneurons on spiny projection neurons, including non-monotonicity, as well as the heterogeneity of the effects. Our work provides a mechanistic explanation of paradoxical effects of GABAergic synaptic inputs, with implications for understanding the effects of GABA in neural computation and development. Neurons in nervous systems mainly communicate at chemical synapses by releasing neurotransmitters from the presynaptic side that bind to receptors on the post-synaptic side, triggering ion flow through ion channels on the cell membrane and changes in the membrane potential of the post-synaptic neuron. Gamma-aminobutyric acid (GABA) is the principal neurotransmitter expressed by inhibitory neurons. Its binding to GABAergic ionotropic receptors mainly causes a flow of chloride ions across the membrane, and typically hyperpolarizes the post-synaptic neuron, resulting in firing suppression. While GABA is canonically viewed as an inhibitory neurotransmitter, non-inhibitory effects have been observed in early stages of development, in stress-related disorders, and in specific parts of brain structures such as cortex, cerebellum and hippocampus. Here, we employ analytical and computational approaches on spiking neuronal models to investigate the mechanisms of diverse effects of GABAergic synaptic inputs. We find that in addition to monotonically excitatory or monotonically inhibitory effects, GABAergic inputs show non-monotonic effects, for which the effect depends on the strength of the input. This effect is stronger in the presence of noise, and is observed in different models both at the single cell, and at the network level. Our findings provide a mechanistic explanation of several paradoxical experimental observations, with potential implications for neural network dynamics and computation.
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影响因子：
4.3
作者：
Doyon N;Prescott SA;Castonguay A;Godin AG;Kröger H;De Koninck Y
通讯作者：
De Koninck Y
计算建模揭示了CA1锥体神经元中GABA（A）介导的激发的树突状起源。
影响因子：
3.7
作者：
Lewin N;Aksay E;Clancy CE
通讯作者：
Clancy CE
影响因子：
16.2
作者：
Gidon, Albert;Segev, Idan
通讯作者：
Segev, Idan
影响因子：
3
作者：
Berke JD
通讯作者：
Berke JD
影响因子：
16.2
作者：
Gulledge, AT;Stuart, GJ
通讯作者：
Stuart, GJ