Recognition of partially occluded and rotated images with a network of spiking neurons.
In this paper, we introduce a novel system for recognition of partially occluded and rotated images. The system is based on a hierarchical network of integrate-and-fire spiking neurons with random synaptic connections and a novel organization process. The network generates integrated output sequences that are used for image classification. The proposed network is shown to provide satisfactory predictive performance given that the number of the recognition neurons and synaptic connections are adjusted to the size of the input image. Comparison of synaptic plasticity activity rule (SAPR) and spike timing dependant plasticity rules, which are used to learn connections between the spiking neurons, indicates that the former gives better results and thus the SAPR rule is used. Test results show that the proposed network performs better than a recognition system based on support vector machines.
Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284 USA. shinjh2@vcu.edu
Joo-Heon
JH
Shin
David
D
Smith
Waldemar
W
Swiercz
Kevin
K
Staley
J Terry
JT
Rickard
Javier
J
Montero
Lukasz A
LA
Kurgan
Krzysztof J
KJ
Cios
D000200:Action Potentials / Q000502:physiology*; D001185:Artificial Intelligence; D002540:Cerebral Cortex / Q000502:physiology; D003198:Computer Simulation / Q000592:standards; D006801:Humans; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012399:Rotation; D009435:Synaptic Transmission / Q000502:physiology