Author:
Dalius Krunglevicius
Affiliation:
Vilnius University, Lithuania
Keyword(s):
Artificial neural networks, Spike timing-dependent plasticity, STDP, Hebbian learning, Temporal coding, Neuroscience.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computational Neuroscience
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neuroinformatics and Bioinformatics
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Theory and Methods
Abstract:
It has been demonstrated, that spike-timing-dependent plasticity (STDP) learning rule can be applied to train neuron to become selective to a spatiotemporal spike pattern. In this paper, we propose a model of neural network that is capable of memorizing prolonged sequences of different spike patterns and learn aggregated data in a larger temporal window.