Network structure within the cerebellar input layer enables lossless sparse encoding.
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Tartalom: http://real.mtak.hu/40946/
Archívum: MTA Könyvtár
Gyűjtemény: Status = Published

Type = Article
Cím:
Network structure within the cerebellar input layer enables lossless sparse encoding.
Létrehozó:
Billings, Guy
Piasini, Eugenio
LĹ‘rincz, Andrea
Nusser, Zoltan
Silver, Robin Angus
Kiadó:
Elsevier
Dátum:
2014-08-07
Téma:
QP Physiology / Ă©lettan
Tartalmi leírás:
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.
Nyelv:
magyar
Típus:
Article
PeerReviewed
info:eu-repo/semantics/article
Formátum:
text
Azonosító:
Billings, Guy and Piasini, Eugenio and LĹ‘rincz, Andrea and Nusser, Zoltan and Silver, Robin Angus (2014) Network structure within the cerebellar input layer enables lossless sparse encoding. NEURON, 83 (4). pp. 960-74. ISSN 0896-6273
Kapcsolat:
10.1016/j.neuron.2014.07.020