Abstract
Reconstructing neuronal microcircuits through computational models is fundamental to simulate local neuronal dynamics. Here a scaffold model 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 mm3) 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.
Highlights
The causal relationship between components of the nervous system at different spatio-temporal scales, from subcellular mechanisms to behavior, still needs to be disclosed, and this represents one of the main challenges of modern neuroscience
The cerebellar cortex volume had 400 × 400 μm2 base and 330 μm height subdivided into different layers: molecular layer (150 μm), Purkinje cell layer (30 μm), granular layer (150 μm)
The scaffold model was able to reconstruct the complex geometry and neuronal interactions of the cerebellar microcircuit based on intersection-connection rules
Summary
The causal relationship between components of the nervous system at different spatio-temporal scales, from subcellular mechanisms to behavior, still needs to be disclosed, and this represents one of the main challenges of modern neuroscience. Realistic modeling can predict emerging collective behaviors producing testable hypotheses for experimental and theoretical investigations (Llinas, 2014) and might play a critical role in understanding neurological disorders (Soltesz and Staley, 2018). Realistic modeling of microcircuit dynamics and causal relationships among multiscale mechanisms poses complex computational problems. The modeling strategy needs to be flexible accounting for a variety of neuronal features and network architectures, to be easy to update as new anatomical, or neurophysiological data become available, and to be easy to modify in order to test different structural and functional hypotheses. The modeling tools need to be scalable to the dimension of the network and to the nature of the scientific question (Destexhe et al, 1996), to be suitable for available simulation platforms, e.g., pyNEST and pyNEURON (Brette et al, 2007; Eppler et al, 2008; Hines et al, 2009), and to efficiently exploit High-Performance Computing (HPC) resources
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