Hendrickson Phillip J, Yu Gene J, Song Dong, Berger Theodore W
Department of Biomedical Engineering, University of Southern California Los Angeles, CA, USA.
Front Syst Neurosci. 2015 Nov 17;9:155. doi: 10.3389/fnsys.2015.00155. eCollection 2015.
This paper reports on findings from a million-cell granule cell model of the rat dentate gyrus that was used to explore the contributions of local interneuronal and associational circuits to network-level activity. The model contains experimentally derived morphological parameters for granule cells, which each contain approximately 200 compartments, and biophysical parameters for granule cells, basket cells, and mossy cells that were based both on electrophysiological data and previously published models. Synaptic input to cells in the model consisted of glutamatergic AMPA-like EPSPs and GABAergic-like IPSPs from excitatory and inhibitory neurons, respectively. The main source of input to the model was from layer II entorhinal cortical neurons. Network connectivity was constrained by the topography of the system, and was derived from axonal transport studies, which provided details about the spatial spread of axonal terminal fields, as well as how subregions of the medial and lateral entorhinal cortices project to subregions of the dentate gyrus. Results of this study show that strong feedback inhibition from the basket cell population can cause high-frequency rhythmicity in granule cells, while the strength of feedforward inhibition serves to scale the total amount of granule cell activity. Results furthermore show that the topography of local interneuronal circuits can have just as strong an impact on the development of spatio-temporal clusters in the granule cell population as the perforant path topography does, both sharpening existing clusters and introducing new ones with a greater spatial extent. Finally, results show that the interactions between the inhibitory and associational loops can cause high frequency oscillations that are modulated by a low-frequency oscillatory signal. These results serve to further illustrate the importance of topographical constraints on a global signal processing feature of a neural network, while also illustrating how rich spatio-temporal and oscillatory dynamics can evolve from a relatively small number of interacting local circuits.
本文报道了一项关于大鼠齿状回百万细胞颗粒细胞模型的研究结果，该模型用于探究局部中间神经元和联合回路对网络水平活动的贡献。该模型包含从实验中得出的颗粒细胞形态学参数（每个颗粒细胞约有200个区室），以及基于电生理数据和先前发表的模型得出的颗粒细胞、篮状细胞和苔藓细胞的生物物理参数。模型中细胞的突触输入分别由来自兴奋性和抑制性神经元的谷氨酸能AMPA样兴奋性突触后电位（EPSP）和GABA能样抑制性突触后电位（IPSP）组成。模型的主要输入源来自内嗅皮层第II层神经元。网络连接受到系统拓扑结构的限制，其来源于轴突运输研究，该研究提供了轴突终末场空间扩散的细节，以及内侧和外侧内嗅皮层的子区域如何投射到齿状回的子区域。本研究结果表明，篮状细胞群体的强反馈抑制可导致颗粒细胞产生高频节律，而前馈抑制的强度则用于调节颗粒细胞活动的总量。此外，结果还表明，局部中间神经元回路的拓扑结构对颗粒细胞群体中时空簇的发展的影响与穿通路径拓扑结构一样大，既能锐化现有的簇，又能引入空间范围更大的新簇。最后，结果表明，抑制性回路和联合回路之间的相互作用可导致由低频振荡信号调制的高频振荡。这些结果进一步说明了拓扑约束对神经网络全局信号处理特征的重要性，同时也说明了丰富的时空和振荡动力学如何从相对少量相互作用的局部回路中演变而来。
