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Neuron. 2014 Aug 20;83(4):960-74. doi: 10.1016/j.neuron.2014.07.020. Epub 2014 Aug 7.

Network structure within the cerebellar input layer enables lossless sparse encoding.

Author information

1
Department of Neuroscience, Physiology and Pharmacology, University College London, London, WC1E 6BT UK.
2
Institute of Experimental Medicine, Hungarian Academy of Sciences, H-1083 Budapest, Hungary.
3
Department of Neuroscience, Physiology and Pharmacology, University College London, London, WC1E 6BT UK. Electronic address: a.silver@ucl.ac.uk.

Abstract

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.

PMID:
25123311
PMCID:
PMC4148198
DOI:
10.1016/j.neuron.2014.07.020
[Indexed for MEDLINE]
Free PMC Article

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