Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network

Abstract

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

Artificial cerebellum on FPGA: realistic real-time cerebellar spiking neural network model capable of real-world adaptive motor control

“…The artificial cerebellum to be implemented on the FPGA in the present study is similar to those previously constructed by referring to anatomical and physiological evidence of the cerebellar cortex (Medina et al, 2000;Inagaki and Hirata, 2017;Casali et al, 2019;Kuriyama et al, 2021). Presently, the scale of the artificial cerebellum (the number of neuron models) is set to ∼10 4 neurons which is limited by the specification of the FPGA (XC6SLX100, Xilinx) used in the current study (see below for more detailed specs).…”

“…The current i spont (t) is to simulate the spontaneous discharge, which is generated by a uniform random number [0, 2I spont ] to prevent the timing of spontaneous spikes from becoming the same between neurons. The mean spontaneous discharge current, I spont , is shown in the cerebellum model of Casali et al (2019). The constants for each neuron/fiber type are listed in Table 1 which are the same as the previous realistic artificial cerebellums (Casali et al, 2019;Kuriyama et al, 2021) except that the parameters of the input fibers were arbitrarily defined so that their firing frequencies become physiologically appropriate.…”

“…The mean spontaneous discharge current, I spont , is shown in the cerebellum model of Casali et al (2019). The constants for each neuron/fiber type are listed in Table 1 which are the same as the previous realistic artificial cerebellums (Casali et al, 2019;Kuriyama et al, 2021) except that the parameters of the input fibers were arbitrarily defined so that their firing frequencies become physiologically appropriate.…”

“…Examples of spiking models of the cerebellum include a model to explain the timing mechanism of eyeblink conditioning (Medina et al, 2000), and a model for acute vestibulo-ocular reflex motor learning (Inagaki and Hirata, 2017). Moreover, a realistic 3D cerebellar scaffold model running on pyNEST and pyNEURON simulators (Casali et al, 2019), a cerebellar model capable of real-time simulation with 100k neurons using 4 NVIDIA Tesla V100 GPUs (Kuriyama et al, 2021), and a Human-Scale Cerebellar Model composed of 68 billion spiking neurons utilizing the supercomputer K (Yamaura et al, 2020) have been constructed. A caveat with these spiking cerebellar models is their resourceintensive nature compared to non-spiking models due to the computational demands of simulating spike dynamics in neurons.…”

An overview of brain-like computing: Architecture, applications, and future trends

“…Currently, neuroscientific knowledge of the human brain is only superficial and the development of neuromorphic computing is not guided by theory. Researchers hope to refine models and theories by using brain-like computing for partial simulations of brain function and structure (Casali et al, 2019;Rizza et al, 2021).…”