Publication
Title
Realistic modeling applied to cerebellar function
Author
Abstract
Realistic network and single cell modeling can be powerful tools to study the operation of real brain structures. We have used realistic simulation approaches based on fully active compartmental models to study the cerebellum. Here I summarize some of our recent results that address cerebellar learning. Assuming that long-term depression (LTD) is the basis of learning in cerebellar Purkinje cells, two questions arise: what is a parallel fiber pattern and how can the occurrence of learned patterns be decoded from the Purkinje cell spike train? We have used detailed network simulations of the granular layer using conductance based models of granule and Golgi cells to study the patterns of parallel fiber activity in response to natural mossy fiber stimulation. Based on these simulations we predict a sparse temporal coding by parallel fibers of complex spatial maps of mossy fiber input. We have studied for the first time pattern recognition by Purkinje cells in a realistic context, i.e. how to recognize the effect of depressed synapses on a spiking neuron which is spontaneously active most of the time. Using our standard Purkinje cell model we predict that parallel fiber patterns that have undergone LTD cause an increase In the Purkinje cell output instead of the decrease assumed by most cerebellar learning theories.
Language
English
Source (journal)
IEEE International Conference on Neural Networks (ICNN)
NETWORKS, VOLS 1-3
Source (book)
International Joint Conference on Neural Networks (IJCNN 02), MAY 12-17, 2002, HONOLULU, HI
Publication
2002
ISBN
0-7803-7278-6
Volume/pages
(2002), p. 75-76
ISI
000177402800014
Full text (Publishers DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 03.01.2013
Last edited 03.05.2017
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