Network Structure within the Cerebellar Input Layer Enables Lossless Sparse Encoding

By Guy O. Billings, Eugenio Piasini, A. Lorincz, Z. Nusser, R. Silver. 2014

135 citations recorded by the source corpus.

Summary The synaptic connectivity within neuronal networks is thought to determine the information processing they perform, yet network structure-function relationships remain poorly understood. By combining quantitative anatomy of the cerebellar input layer and information theoretic analysis of network models, we investigated how synaptic connectivity affects information transmission and processing. Simplified binary models revealed that the synaptic connectivity within feedforward networks determines the trade-off between information transmission and sparse encoding. Networks with few synaptic connections per neuron and network-activity-dependent threshold were optimal for lossless sparse encoding over the widest range of input activities. Biologically detailed spiking network models with experimentally constrained synaptic conductances and inhibition confirmed our analytical predictions. Our results establish that the synaptic connectivity within the cerebellar input layer enables efficient lossless sparse encoding. Moreover, they provide a functional explanation for why granule cells have approximately four dendrites, a feature that has been evolutionarily conserved since the appearance of fish.

Subfields: Neurology, Radiology, Nuclear Medicine and Imaging, Sensory Systems

Citations in the corpus

• Computational Principles of Supervised Learning in the Cerebellum (2018, unknown): “…separation; the number of mossy fiber inputs per granule cell is low, which should support decorrelation of spatially overlapping mossy fiber patterns and the generation of high-dimensional representations in granule cells (Billings et al. 2014, Cayco-Gajic et al. 2017, Litwin-Kumar et al. 2017).”