Claudia Casellato1, Alice Geminiani2, Alessandra Pedrocchi2, Elisa Marenzi1, Stefano Casali1, Chaitanya Medini1, Egidio D’Angelo1
1University of Pavia, Dept. of Brain and Behavioral Sciences - Unit of Neurophysiology, Pavia, Italy; 2Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milan, Italy
Correspondence: Claudia Casellato (firstname.lastname@example.org)
BMC Neuroscience 2019, 20(Suppl 1):P54
Computational models allow propagating microscopic phenomena into large-scale networks and inferencing causal relationships across scales. Here we reconstruct the cerebellar circuit by bottom-up modeling, reproducing the peculiar properties of this structure, which shows a quasi-crystalline geometrical organization well defined by convergence/divergence ratios of neuronal connections and by the anisotropic 3D orientation of dendritic and axonal processes .
Therefore, a cerebellum scaffold model has been developed and tested. It maintains scalability and can be flexibly handled to incorporate neuronal properties on multiple scales of complexity. The cerebellar scaffold includes the canonical neuron types: Granular cell, Golgi cell, Purkinje cell, Stellate and Basket cells, Deep Cerebellar Nuclei cell. Placement was based on density and encumbrance values, connectivity on specific geometry of dendritic and axonal fields, and on distance-based probability.
In the first release, spiking point-neuron models based on Integrate & Fire dynamics with exponential synapses were used. The network was run in the neural simulator pyNEST. Complex spatiotemporal patterns of activity, similar to those observed in vivo, emerged .
For a second release of the microcircuit model, an extension of the generalized Leaky Integrate & Fire model has been developed, optimized for each cerebellar neuron type and inserted into the built scaffold . It could reproduce a rich variety of electroresponsive patterns with a single set of optimal parameters.
Complex single neuron dynamics and local connectome are key elements for cerebellar functioning.
Then, point-neurons have been replaced by detailed 3D multi-compartment neuron models. The network was run in the neural simulator pyNEURON. Further properties emerged, strictly linked to the morphology and the specific properties of each compartment.
This multiscale tool with different levels of realism has the potential to summarize in a comprehensive way the electrophysiological intrinsic neural properties that drive network dynamics and high-level behaviors.
The model, equipped with ad-hoc plasticity rules, has been embedded in a sensorimotor loop of EyeBlink Classical Conditioning. The network output evolved along repetitions of the task, therefore letting emerge three fundamental operations ascribed to the cerebellum: prediction, timing and learning of motor commands.
Acknowledgments: This research was supported by the HBP Neuroinformatics, Brain Simulation, and HPAC Platforms, funded by European Union’s Horizon 2020 under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2), also involving the HBP Partnering Project CerebNEST.
D’Angelo E, Antonietti A, Casali S, et al. Modeling the cerebellar microcircuit: new strategies for a long-standing issue. Frontiers in cellular neuroscience 2016 Jul 8;10:176.
Casali S, Marenzi E, Medini KC, Casellato C, D‘Angelo E. Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network. Frontiers in Neuroinformatics 2019;13:37.
Geminiani A, Casellato C, Locatelli F, et al. Complex dynamics in simplified neuronal models: reproducing Golgi cell electroresponsiveness. Frontiers in Neuroinformatics 2018, 12, 1–19; https://doi.org/10.3389/fninf.2018.00088