Gradients in the mammalian cerebellar cortex enable Fourier-like transformation and improve storing capacity

Abstract

Cerebellar granule cells (GCs) make up the majority of all neurons in the vertebrate brain, but heterogeneities among GCs and potential functional consequences are poorly understood. Here, we identified unexpected gradients in the biophysical properties of GCs in mice. GCs closer to the white matter (inner-zone GCs) had higher firing thresholds and could sustain firing with larger current inputs than GCs closer to the Purkinje cell layer (outer-zone GCs). Dynamic Clamp experiments showed that inner- and outer-zone GCs preferentially respond to high- and low-frequency mossy fiber inputs, respectively, enabling dispersion of the mossy fiber input into its frequency components as performed by a Fourier transformation. Furthermore, inner-zone GCs have faster axonal conduction velocity and elicit faster synaptic potentials in Purkinje cells. Neuronal network modeling revealed that these gradients improve spike-timing precision of Purkinje cells and decrease the number of GCs required to learn spike-sequences. Thus, our study uncovers biophysical gradients in the cerebellar cortex enabling a Fourier-like transformation of mossy fiber inputs.

Projection-dependent heterogeneity of cerebellar granule cell calcium responses

“…Our analysis of GFP-positive GCs vastly labeled by the injection of AAV-GABRα6-GFP reveals that GFP-positive GC somas were not clustered along anterior–posterior axis of the GCL, and that apparent unorganized distribution of GC somas was not different among D-GCs, M-GCs, and S-GCs, whose PFs were clearly bundled in the deep, middle, and superficial sublayers of ML, respectively. Although a recent study analyzing DiI-labeled GCs showed a correlation between the position of GC somas in the GCL and their PFs in the ML [ 26 ], our results are consistent with the three other studies that analyzed the distributions of GCs and PFs labeled by mosaic analysis with double marker, in vivo electroporation, or loading of Ca 2+ indicator into a small cluster of GCs [ 43 – 45 ]. Because of the uncorrelated nature of positions of GC somas in the GCL and their PFs in the ML, labeling GCs according to the location of their PFs by our AAV-GABRα6 method was critical in analyzing PF location-associated functional variability in GCs.…”

“…In addition, there is an obvious diversity in GC network structure, which is the location of their axons, parallel fibers (PFs), in the molecular layer (ML), and morphological and functional differences of PFs according to their locations have been reported. Studies using electron microscopy revealed that diameters of PFs located in the deeper ML were wider [ 26 , 28 – 32 ]. Depending on the location of the PFs, the velocity of action potential propagation in PFs, as well as the processing of PF inputs by Purkinje cells was different [ 26 ].…”

“…Studies using electron microscopy revealed that diameters of PFs located in the deeper ML were wider [ 26 , 28 – 32 ]. Depending on the location of the PFs, the velocity of action potential propagation in PFs, as well as the processing of PF inputs by Purkinje cells was different [ 26 ]. Thus, the information processing through GCs is likely varied according to the locations of their PFs in the ML.…”

Topographic gradients of intrinsic dynamics across neocortex

“…An emerging literature emphasizes the hierarchical organization of neural systems, whereby systematic variations in laminar architecture across the cortical sheet are mirrored by multiple cytological properties, including neuron density, spine count, branching and neurotransmitter receptor profiles [47,63,65]. These variations ultimately manifest as spatially ordered gradients of structural and functional attributes [50], including gene expression [12,31], cortical thickness [100], intracortical myelin [49], laminar differentiation [72,99] and excitability [22,64,88,102]. Indeed, we find that the two patterns of intrinsic dynamics are closely related to gene expression, intracortical myelin and cortical thickness.…”

Topographic gradients of intrinsic dynamics across neocortex

“…An emerging literature emphasizes the hierarchical organization of neural systems, whereby systematic variation in laminar architecture across the cortical sheet is mirrored by multiple cytological properties, including neuron density, spine count, branching and neurotransmitter receptor profiles ( Mesulam, 1998 ; Margulies et al, 2016 ; Hilgetag and Goulas, 2020 ). These variations manifest as spatially ordered gradients of structural and functional attributes ( Huntenburg et al, 2018 ), including gene expression ( Burt et al, 2018 ; Fulcher et al, 2019 ; Hansen et al, 2020 ), cortical thickness ( Wagstyl et al, 2015 ), intracortical myelin ( Huntenburg et al, 2017 ), laminar differentiation ( Paquola et al, 2019 ; Wagstyl et al, 2020 ) and excitability ( Demirtaş et al, 2019 ; Wang, 2020 ; Markicevic et al, 2020 ; Straub et al, 2020 ). Indeed, we find that the two patterns of intrinsic dynamics are closely related to gene expression, intracortical myelin and cortical thickness.…”