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Robust Reservoir Generation by Correlation-Based Learning (Yamazaki & Tanaka 2008)
 
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Model Information
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Citations
Simulation Platform
Accession:
116806
"Reservoir computing (RC) is a new framework for neural computation. A reservoir is usually a recurrent neural network with fixed random connections. In this article, we propose an RC model in which the connections in the reservoir are modifiable. ... We apply our RC model to trace eyeblink conditioning. The reservoir bridged the gap of an interstimulus interval between the conditioned and unconditioned stimuli, and a readout neuron was able to learn and express the timed conditioned response."
Reference:
1 .
Yamazaki T, Tanaka S (2009) Robust Reservoir Generation by Correlation-Based Learning
Advances in Artificial Neural Systems
2009
:1-7
Model Information
(Click on a link to find other models with that property)
Model Type:
Realistic Network;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment:
C or C++ program;
Model Concept(s):
Temporal Pattern Generation;
Spatio-temporal Activity Patterns;
Rate-coding model neurons;
Learning;
Implementer(s):
Download the displayed file
/
aans2008
README.html
button.png
*
Other models using button.png:
Neural modeling of an internal clock (Yamazaki and Tanaka 2008)
main.c
Makefile
mt19937ar-cok.c
*
Other models using mt19937ar-cok.c:
Cerebellar gain and timing control model (Yamazaki & Tanaka 2007)(Yamazaki & Nagao 2012)
Neural modeling of an internal clock (Yamazaki and Tanaka 2008)
run.sh
w.0.bz2
xcorr.c
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