Author:
Dalius Krunglevicius
Affiliation:
Vilnius University, Lithuania
Keyword(s):
Artificial Neural Networks, Spike-Timing-Dependent Plasticity, STDP, Hebbian Learning, Unsupervised Learning, Temporal Coding, Neuroscience.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computational Neuroscience
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Based Data Mining and Complex Information Processing
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Theory and Methods
Abstract:
Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules which are firmly based on
biological evidence. It has been demonstrated that one of the STDP learning rules is suited for learning
spatiotemporal patterns in a very noisy environment. Parameters of the neuron are only optimal, however,
for a certain range of quantity of injected noise. This means the level of noise must be known beforehand so
that the parameters can be set accordingly. That could be a real problem when noise levels vary over time.
We found that the model of a leaky-integrate-and-fire inhibitory neuron with an inverted STDP learning rule
is capable of adjusting its response rate to a particular level of noise. In this paper we suggest a method that
uses an inverted SDTP learning rule to modulate spiking rate of the trained neuron. This method is adaptive
to noise levels; subsequently spiking neuron can be trained to learn the same spatiotemporal pattern with a
wide range of background noise in
jected during the learning process.
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