Dataset Information

Cerebellum morphogenesis: the foliation pattern is orchestrated by multi-cellular anchoring centers.

ABSTRACT:

Background

The cerebellum has a striking morphology consisting of folia separated by fissures of different lengths. Since folia in mammals likely serve as a broad platform on which the anterior-posterior organization of the sensory-motor circuits of the cerebellum are built, it is important to understand how such complex morphology arises.

Results

Using a combination of genetic inducible fate mapping, high-resolution cellular analysis and mutant studies in mouse, we demonstrate that a key event in initiation of foliation is the acquisition of a distinct cytoarchitecture in the regions that will become the base of each fissure. We term these regions 'anchoring centers'. We show that the first manifestation of anchoring centers when the cerebellar outer surface is smooth is an increase in proliferation and inward thickening of the granule cell precursors, which likely causes an associated slight invagination of the Purkinje cell layer. Thereafter, granule cell precursors within anchoring centers become distinctly elongated along the axis of the forming fissure. As the outer cerebellar surface begins to fold inwards, Bergmann glial fibers radiate in towards the base of the immature fissure in a fan shape. Once the anchoring center is formed, outgrowth of folia seems to proceed in a self-sustaining manner driven by granule cell migration along Bergmann glial fibers. Finally, by analyzing a cerebellum foliation mutant (Engrailed 2), we demonstrate that changing the timing of anchoring center formation leads to predictable changes in the shape and size of the surrounding folia.

Conclusion

We present a new cellular model of the initial formation of cerebellar fissures with granule cells providing the driving physical force. Both the precise timing of the appearance of anchoring centers at the prospective base of each fissure and the subsequent coordinated action of granule cells and Bergmann glial fibers within the anchoring centers dictates the shape of the folia.

SUBMITTER: Sudarov A

PROVIDER: S-EPMC2246128 | biostudies-literature | 2007 Dec

REPOSITORIES: biostudies-literature

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Publications

Cerebellum morphogenesis: the foliation pattern is orchestrated by multi-cellular anchoring centers.

Sudarov Anamaria A Joyner Alexandra L AL

Neural development 20071203

<h4>Background</h4>The cerebellum has a striking morphology consisting of folia separated by fissures of different lengths. Since folia in mammals likely serve as a broad platform on which the anterior-posterior organization of the sensory-motor circuits of the cerebellum are built, it is important to understand how such complex morphology arises.<h4>Results</h4>Using a combination of genetic inducible fate mapping, high-resolution cellular analysis and mutant studies in mouse, we demonstrate th ...[more]

PMID: 18053187

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Project description:Spiking neural networks were introduced to understand spatiotemporal information processing in neurons and have found their application in pattern encoding, data discrimination, and classification. Bioinspired network architectures are considered for event-driven tasks, and scientists have looked at different theories based on the architecture and functioning. Motor tasks, for example, have networks inspired by cerebellar architecture where the granular layer recodes sparse representations of the mossy fiber (MF) inputs and has more roles in motor learning. Using abstractions from cerebellar connections and learning rules of deep learning network (DLN), patterns were discriminated within datasets, and the same algorithm was used for trajectory optimization. In the current work, a cerebellum-inspired spiking neural network with dynamics of cerebellar neurons and learning mechanisms attributed to the granular layer, Purkinje cell (PC) layer, and cerebellar nuclei interconnected by excitatory and inhibitory synapses was implemented. The model's pattern discrimination capability was tested for two tasks on standard machine learning (ML) datasets and on following a trajectory of a low-cost sensor-free robotic articulator. Tuned for supervised learning, the pattern classification capability of the cerebellum-inspired network algorithm has produced more generalized models than data-specific precision models on smaller training datasets. The model showed an accuracy of 72%, which was comparable to standard ML algorithms, such as MLP (78%), Dl4jMlpClassifier (64%), RBFNetwork (71.4%), and libSVM-linear (85.7%). The cerebellar model increased the network's capability and decreased storage, augmenting faster computations. Additionally, the network model could also implicitly reconstruct the trajectory of a 6-degree of freedom (DOF) robotic arm with a low error rate by reconstructing the kinematic parameters. The variability between the actual and predicted trajectory points was noted to be ± 3 cm (while moving to a position in a cuboid space of 25 × 30 × 40 cm). Although a few known learning rules were implemented among known types of plasticity in the cerebellum, the network model showed a generalized processing capability for a range of signals, modulating the data through the interconnected neural populations. In addition to potential use on sensor-free or feed-forward based controllers for robotic arms and as a generalized pattern classification algorithm, this model adds implications to motor learning theory.

Dataset Information

Cerebellum morphogenesis: the foliation pattern is orchestrated by multi-cellular anchoring centers.

Background

Results

Conclusion

Publications

Cerebellum morphogenesis: the foliation pattern is orchestrated by multi-cellular anchoring centers.

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