In and out of the loop: external and internal modulation of the olivo-cerebellar loop

Marenzi

Medini

et al. 2019

Front. Neuroinform.

110

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 mm 3 ) 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.

Section: Discussionmentioning

confidence: 99%

Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network

Marenzi

Medini

et al. 2019

Front. Neuroinform.

110

“…What is becoming clear is that this subcircuit has all the ingredients to subserve both functions. The IO operates as a pattern generator exploiting gap-junctions and local synaptic inhibition coming from the DCN in order to organize internal activity patterns that are then conveyed to PCs (Jacobson et al, 2008 ; Chen et al, 2010 ; Libster et al, 2010 ; Lefler et al, 2013 ; Libster and Yarom, 2013 ). This cf pattern, in turn, could be used to select mossy fiber patterns in specific groups of PCs.…”

Section: Critical Dynamic Properties Of the Cerebellar Microcircuitmentioning

confidence: 99%

“…There are aspects of intracerebellar organization and connectivity that remain to be incorporated into large-scale realistic models, including the granular layer-molecular layer projections (Valera et al, 2016 ), the PC-DCN convergence (Person and Raman, 2012b ), the DCN-granular layer projections (Houck and Person, 2015 ), the PC-DCN-IO loops (Libster and Yarom, 2013 ). Beyond this, these are needed for guided cerebellar model simplification and incorporation into large-scale networks running into robotic controllers and simulated environments (Garrido et al, 2013 ; Casellato et al, 2015 ; Yamazaki et al, 2015 ).…”

Section: Current Perspectives For Realistic Cerebellar Modelingmentioning

confidence: 99%

Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue

D’Angelo

Antonietti

et al. 2016

Front. Cell. Neurosci.

The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate “realistic” models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems.

“…Thirdly, fully implementing cerebellar connectivity requires the introduction of models of the 563 inferior cerebellar olive (IO) (Libster and Yarom, 2013;De Gruijl et al, 2014). This will complete 564 the DCN-PC-IO cerebellar circuit, allowing the model to simulate oscillations in the olivo-cerebellar 565 circuit, their impact on PC dendritic calcium signaling and computation, and eventually climbing 566 fiber control of plasticity at parallel fiber synapses (Coesmans et al, 2004).…”

Section: The Impact Of Molecular Layer Interneurons On Pc Activation mentioning

confidence: 99%

Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network

Marenzi

Medini

et al. 2019

Preprint

16Reconstructing neuronal microcircuits through computational models is fundamental to 17 simulate local neuronal dynamics. Here a scaffold model of the cerebellum has been developed in 18 order to flexibly place neurons in space, connect them synaptically and endow neurons and synapses 19with biologically-grounded mechanisms. The scaffold model can keep neuronal morphology 20 separated from network connectivity, which can in turn be obtained from convergence/divergence 21 ratios and axonal/dendritic field 3D geometries. We first tested the scaffold on the cerebellar 22 microcircuit, which presents a challenging 3D organization, at the same time providing appropriate 23 datasets to validate emerging network behaviors. The scaffold was designed to integrate the 24 cerebellar cortex with deep cerebellar nuclei (DCN), including different neuronal types: Golgi cells, 25 granule cells, Purkinje cells, stellate cells, basket cells and DCN principal cells. Mossy fiber (mf) 26 inputs were conveyed through the glomeruli. An anisotropic volume (0.077 mm 3 ) of mouse 27 cerebellum was reconstructed, in which point-neuron models were tuned toward the specific 28 discharge properties of neurons and were connected by exponentially decaying excitatory and 29 inhibitory synapses. Simulations using pyNEST and pyNEURON showed the emergence of 30 organized spatio-temporal patterns of neuronal activity similar to those revealed experimentally in 31 response to background noise and burst stimulation of mossy fiber bundles. Different configurations 32 of granular and molecular layer connectivity consistently modified neuronal activation patterns, 33revealing the importance of structural constraints for cerebellar network functioning. The scaffold 34 provided thus an effective workflow accounting for the complex architecture of the cerebellar 35 network. In principle, the scaffold can incorporate cellular mechanisms at multiple levels of detail 36and be tuned to test different structural and functional hypotheses. A future implementation using 37 detailed 3D multi-compartment neuron models and dynamic synapses will be needed to investigate 38 the impact of single neuron properties on network computation. 39 40 41 Cerebellar scaffold model 2 This is a provisional file, not the final typeset article 42