Casali Stefano, Marenzi Elisa, Medini Chaitanya, Casellato Claudia, D'Angelo Egidio
Neurophysiology Unit, Neurocomputational Laboratory, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
Front Neuroinform. 2019 May 15;13:37. doi: 10.3389/fninf.2019.00037. eCollection 2019.
Reconstructing neuronal microcircuits through computational models is fundamental to simulate local neuronal dynamics. Here a of the cerebellum has been developed in order to flexibly place neurons in space, connect them synaptically, and endow neurons and synapses with biologically-grounded mechanisms. The scaffold model can keep neuronal morphology separated from network connectivity, which can in turn be obtained from convergence/divergence ratios and axonal/dendritic field 3D geometries. We first tested the scaffold on the cerebellar microcircuit, which presents a challenging 3D organization, at the same time providing appropriate datasets to validate emerging network behaviors. The scaffold was designed to integrate the cerebellar cortex with deep cerebellar nuclei (DCN), including different neuronal types: Golgi cells, granule cells, Purkinje cells, stellate cells, basket cells, and DCN principal cells. Mossy fiber inputs were conveyed through the glomeruli. An anisotropic volume (0.077 mm) of mouse cerebellum was reconstructed, in which point-neuron models were tuned toward the specific discharge properties of neurons and were connected by exponentially decaying excitatory and inhibitory synapses. Simulations using both pyNEST and pyNEURON showed the emergence of organized spatio-temporal patterns of neuronal activity similar to those revealed experimentally in response to background noise and burst stimulation of mossy fiber bundles. Different configurations of granular and molecular layer connectivity consistently modified neuronal activation patterns, revealing the importance of structural constraints for cerebellar network functioning. The scaffold provided thus an effective workflow accounting for the complex architecture of the cerebellar network. In principle, the scaffold can incorporate cellular mechanisms at multiple levels of detail and be tuned to test different structural and functional hypotheses. A future implementation using detailed 3D multi-compartment neuron models and dynamic synapses will be needed to investigate the impact of single neuron properties on network computation.
通过计算模型重建神经元微电路对于模拟局部神经元动力学至关重要。在此，为了灵活地将神经元放置在空间中，通过突触连接它们，并赋予神经元和突触基于生物学的机制，已经开发了一种小脑支架模型。该支架模型可以将神经元形态与网络连接性分离，而网络连接性又可以从汇聚/发散比率以及轴突/树突场三维几何结构中获得。我们首先在具有挑战性的三维组织结构的小脑微电路上测试了该支架，同时提供适当的数据集以验证新出现的网络行为。该支架旨在将小脑皮质与小脑深部核团（DCN）整合在一起，包括不同的神经元类型：高尔基细胞、颗粒细胞、浦肯野细胞、星状细胞、篮状细胞和DCN主细胞。苔藓纤维输入通过肾小球进行传递。重建了小鼠小脑的一个各向异性体积（0.077立方毫米），其中点神经元模型根据神经元的特定放电特性进行了调整，并通过指数衰减的兴奋性和抑制性突触进行连接。使用pyNEST和pyNEURON进行的模拟显示，出现了有组织的神经元活动时空模式，类似于实验中在响应背景噪声和苔藓纤维束的爆发性刺激时所揭示的模式。颗粒层和分子层连接性的不同配置持续改变了神经元激活模式，揭示了结构约束对小脑网络功能的重要性。因此，该支架提供了一个有效的工作流程，考虑了小脑网络的复杂架构。原则上，该支架可以纳入多个细节层次的细胞机制，并进行调整以测试不同的结构和功能假设。未来需要使用详细的三维多房室神经元模型和动态突触来研究单个神经元特性对网络计算的影响。
