Deep Cerebellar Nuclei

Deep cerebellar nuclei (DCN) are the primary output structures of the cerebellar cortex [1]. They receive inhibitory input from Purkinje cells and excitatory input from mossy fibers and climbing fibers (from IO). DCN are composed by three distinct nuclei: dentate nucleus, fastigial nucleus and interposed nucleus. The default configuration with DCN is implemented in dcn.yaml.

Configuration

In dcn.yaml , a new region called cerebellar_nuclei was added to the canonical circuit. This region contains only one Layer Partition: dcn layer. dcn layer has a thickness of \(200 \mu m\) . Additionally, to ensure that cerebellar_nuclei are placed under the cerebellar_cortex, the origin of the dcn_layer was set to [0, 0, -200] (below the granular layer).

Cell types

In DCN, two types of neurons are considered [2] [3]:

• DCNp: they are the excitatory neurons projecting outside the cerebellum to various brain regions, including the thalamus, the red nucleus, the vestibular nuclei, and the reticular formation;

• DCNi: they are GABAergic interneurons which send inhibitory feedback to IO.

Warning

In Geminiani et al (2019) [8], DCN populations are defined in a different way:

• DCNnL for the excitatory population (DCNp)

• DCNp for the inhibitory one (DCNi)

Then, starting from Geminiani et al (2019b) [3] , names for DCN populations were redefined as reported here.

No morphologies are currently available for DCN neurons, so they are modelled as point neurons. Densities were estimated from Blue Brain Cell Atlas (version 2018 [6]), considering the ratio \(\frac{n_{GrC}}{n_{DCN}} = \frac{33 \times 10^6}{230 \times 10^3} \approx 143\) between the total number of granule cells and the total number of neurons in the cerebellar nuclei. From this ratio, considering the total amount of GrC placed in the canonical circuit, it is possible to estimate the number of DCN to be placed. Literature data reported that DCNp are around the 57% of the total number of neurons in the cerebellar nuclei, while DCNi around the 32% [4] [5]. Taking into account these percentages and dividing by the volume of the DCN layer (set to \(200 \times 200 \times 300 \mu m\)), the values reported in the following table were obtained.

Cell name	Type	Radius (:math:` mu m`)	Density (:math:` mu m^{-3}`)
DCNp	Exc.	9.5 [4]	9.92
DCNi	Inh.	7.0 [4]	5.58

Placement

DCN are assumed to be uniformly distributed in their own layer, hence the bsb RandomPlacement strategy is chosen to place them.

Connectivity

#	Source Name	Source Branch	Target Name	Target Branch	Strategy	Specifics	References
19	PC	axon	DCNp	/	AllToAll	affinity =0.375	Geminiani et al. (2024) [7]
20	PC	axon	DCNi	/	AllToAll	affinity =0.18	Geminiani et al. (2024) [7]
21	mf	/	DCNp	/	AllToAll	affinity =0.4	Geminiani et al. (2024) [7]

References

[1]

D’Angelo, Egidio. “Physiology of the cerebellum.” Handbook of clinical neurology 154 (2018): 85-108. https://doi.org/10.1016/B978-0-444-63956-1.00006-0

[2]

Uusisaari, M., and T. Knöpfel. “GABAergic synaptic communication in the GABAergic and non-GABAergic cells in the deep cerebellar nuclei.” Neuroscience 156.3 (2008): 537-549. https://doi.org/10.1016/j.neuroscience.2008.07.060

[3] (1,2)

Geminiani, A., Pedrocchi, A., D’Angelo, E., & Casellato, C. (2019). “Response dynamics in an olivocerebellar spiking neural network with non-linear neuron properties.” Frontiers in computational neuroscience, 13, 68. https://doi.org/10.3389/fncom.2019.00068

[4] (1,2,3)

Baumel, Yuval, Gilad A. Jacobson, and Dana Cohen. “Implications of functional anatomy on information processing in the deep cerebellar nuclei.” Frontiers in cellular neuroscience 3 (2009): 795. https://doi.org/10.3389/neuro.03.014.2009

[5]

Batini, Cesira, et al. “Cerebellar nuclei and the nucleocortical projections in the rat: retrograde tracing coupled to GABA and glutamate immunohistochemistry.” Journal of Comparative Neurology 315.1 (1992): 74-84. https://doi.org/10.1002/cne.903150106

[6]

Erö, Csaba, et al. “A cell atlas for the mouse brain.” Frontiers in neuroinformatics 12 (2018): 84. https://doi.org/10.3389/fninf.2018.00084

[7] (1,2,3)

Geminiani, Alice, et al. “Mesoscale simulations predict the role of synergistic cerebellar plasticity during classical eyeblink conditioning.” PLOS Computational Biology 20.4 (2024): e1011277. https://doi.org/10.1371/journal.pcbi.1011277

[8]

Geminiani, A., Casellato, C., D’Angelo, E., & Pedrocchi, A. (2019). Complex electroresponsive dynamics in olivocerebellar neurons represented with extended-generalized leaky integrate and fire models. Frontiers in Computational Neuroscience, 13, 35. https://doi.org/10.3389/fncom.2019.00035